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Senapaty, Goutam, and U. Sathish Rao. "Vibration based condition monitoring of rotating machinery." MATEC Web of Conferences 144 (2018): 01021. http://dx.doi.org/10.1051/matecconf/201814401021.
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This project looks at the different maintenance philosophies and the importance of vibration analysis in predictive maintenance. Since most industries and plants make use of rotational equipment, vibration analysis plays a major role in detecting machine defects and developing flaws before the equipment fails and potentially damages other related equipment and to avoid unwanted breakdowns and downtime. Vibration analysis can help increase the lifetime of equipment when the faults are diagnosed at the right time. Vibration analysis of a rotating table top model is also done to show that some faults might exist even though they are not visible to the naked eye.
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Semma, El Mehdi, Ahmed Mousrij, and Hassan Gziri. "Preliminary study of the vibration-based maintenance implementation: case study." Journal of Quality in Maintenance Engineering 24, no. 2 (May 14, 2018): 134–51. http://dx.doi.org/10.1108/jqme-10-2016-0047.
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Purpose The purpose of this paper is to develop the different phases of the implementation of vibration-based maintenance (VBM). Then, the focus will be on the first two stages, namely, the inventory and feasibility study where each step will be translated into a very detailed implementation process through an industrial case study. Design/methodology/approach The study is based on a state of art on the implementation of the VBM; a survey of national and international experts in the field of VBM and finally an analysis of 30 years of VBM practice in a large Moroccan company in the field of chemical processing, via a collective approach called Diagnostic Court Autonome. Findings The study showed that improving productivity by reducing downtimes due to vibration defects through effective vibration monitoring is possible and investment in equipment and vibration monitoring personnel is largely justified in the company studied. Originality/value This paper presents in detail the two preliminary phases with all procedures describing in a practical way the operating rules to apply and organize the roles of different actors. The work will be useful both to researchers and maintenance managers interested in structuring their vibration monitoring cells.
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Jenab, K., K. Rashidi, and S. Moslehpour. "An Intelligence-Based Model for Condition Monitoring Using Artificial Neural Networks." International Journal of Enterprise Information Systems 9, no. 4 (October 2013): 43–62. http://dx.doi.org/10.4018/ijeis.2013100104.
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This paper reports a newly developed Condition-Based Maintenance (CBM) model based on Artificial Neural Networks (ANNs) which takes into account a feature (e.g., vibration signals) from a machine to classify the condition into normal or abnormal. The model can reduce equipment downtime, production loss, and maintenance cost based on a change in equipment condition (e.g., changes in vibration, power usage, operating performance, temperatures, noise levels, chemical composition, debris content, and volume of material). The model can effectively determine the maintenance/service time that leads to a low maintenance cost in comparison to other types of maintenance strategy. Neural Networks tool (NNTool) in Matlab is used to apply the model and an illustrative example is discussed.
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Jardine, A. K. S., T. Joseph, and D. Banjevic. "Optimizing condition‐based maintenance decisions for equipment subject to vibration monitoring." Journal of Quality in Maintenance Engineering 5, no. 3 (September 1999): 192–202. http://dx.doi.org/10.1108/13552519910282647.
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Yunusa-kaltungo, Akilu, and Jyoti K. Sinha. "Effective vibration-based condition monitoring (eVCM) of rotating machines." Journal of Quality in Maintenance Engineering 23, no. 3 (August 14, 2017): 279–96. http://dx.doi.org/10.1108/jqme-08-2016-0036.
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Purpose The purpose of this paper is mainly to highlight how a simplified and streamlined approach to the condition monitoring (CM) of industrial rotating machines through the application of frequency domain data combination can effectively enhance the eMaintenance framework. Design/methodology/approach The paper commences by providing an overview to the relevance of maintenance excellence within manufacturing industries, with particular emphasis on the roles that rotating machines CM of rotating machines plays. It then proceeds to provide details of the eMaintenance as well as its possible alignment with the introduced concept of effective vibration-based condition monitoring (eVCM) of rotating machines. The subsequent sections of the paper respectively deal with explanations of data combination approaches, experimental setups used to generate vibration data and the theory of eVCM. Findings This paper investigates how a simplified vibration-based rotating machinery faults classification method based on frequency domain data combination can increase the feasibility and practicality of eMaintenance. Research limitations/implications The eVCM approach is based on classifying data acquired under several experimentally simulated conditions on two different machines using combined higher order signal processing parameters so as to reduce CM data requirements. Although the current study was solely based on the application of vibration data acquired from rotating machines, the knowledge exchange platform that currently dominates present day scientific research makes it very likely that the lessons learned from the development of eVCM concept can be easily transferred to other scientific domains that involve continuous CM such as medicine. Practical implications The concept of eMaintenance as a cost-effective and smart means of increasing the autonomy of maintenance activities within industries is rapidly growing in maintenance-related literatures. As viable as the concept appears, the achievement of its optimum objectives and full deployment to the industry is still subjective due to the complexity and data intensiveness of conventional CM practices. In this paper, an eVCM approach is proposed so that rotating machine faults can be effectively detected and classified without the need for repetitive analysis of measured data. Social implications The main strength of eVCM lies in the fact that it permits the sharing of historical vibration data between identical rotating machines irrespective of their foundation structures and speed differences. Since eMaintenance is concerned with driving maintenance excellence, eVCM can potentially contribute towards its optimisation as it cost-effectively streamlines faults diagnosis. This therefore implies that the simplification of vibration-based CM of rotating machines positively impacts the society with regard to the possibility of reducing how much time is actually spent on the accurate detection and classification of faults. Originality/value Although the currently existing body of literature already contains studies that have attempted to show how the combination of measured vibration data from several industrial machines can be used to establish a universal vibration-based faults diagnosis benchmark for incorporation into eMaintenance framework, these studies are limited in the scope of faults, severity and rotational speeds considered. In the current study, the concept of multi-faults, multi-sensor, multi-speed and multi-rotating machine data combination approach using frequency domain data fusion and principal components analysis is presented so that faults diagnosis features for identical rotating machines with different foundations can be shared between industrial plants. Hence, the value of the current study particularly lies in the fact that it significantly highlights a new dimension through which the practical implementation and operation of eMaintenance can be realized using big data management and data combination approaches.
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Koenig, Frank, Pauline Anne Found, and Maneesh Kumar. "Innovative airport 4.0 condition-based maintenance system for baggage handling DCV systems." International Journal of Productivity and Performance Management 68, no. 3 (March 4, 2019): 561–77. http://dx.doi.org/10.1108/ijppm-04-2018-0136.
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PurposeThe purpose of this paper is to present the findings of a recent study conducted with the objective of addressing the problem of failure of baggage carts in the high-speed baggage tunnel at Heathrow Terminal 5 by the development of an innovative condition-based maintenance (CBM) system designed to meet the requirements of 21st century airport systems and Industry 4.0.Design/methodology/approachAn empirical experimental approach to this action research was taken to install a vibration condition monitoring pilot test in the north tunnel at Terminal 5. Vibration data were collected over a 6-month period and analysed to find the threshold of good quality tyres and those with worn bearings that needed replacement. The results were compared with existing measures to demonstrate that vibration monitoring could be used as a predictive model for CBM.FindingsThe findings demonstrated a clear trend of increasing vibration velocity with age and use of the baggage cart wheels caused by wheel mass unbalanced inertia that was transmitted to the tracks as vibration. As a result, preventative maintenance is essential to ensure the smooth running of airport baggage. This research demonstrates that a healthy wheel produces vibration of under 60 mm/s whereas a damaged wheel measures up to 100 mm/s peak to peak velocity and this can be used in real-time condition monitoring to prevent baggage cart failure. It can also run as an autonomous system linked to AI and Industry 4.0 airport logic.Originality/valueWhilst vibration monitoring has been used to measure movement in static structures such as bridges and used in rotating machinery such as railway wheels (Tondon and Choudhury, 1999); this is unique as it is the first time it has been applied on a stationary structure (tracks) carrying high-speed rotating machinery (baggage cart wheels). This technique has been patented and proven in the pilot study and is in the process of being rolled out to all Heathrow terminal connection tunnels. It has implications for all other airports worldwide and, with new economic sensors, to other applications that rely on moving conveyor belts.
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Wang, Jianguo, Minmin Xu, Chao Zhang, Baoshan Huang, and Fengshou Gu. "Online Bearing Clearance Monitoring Based on an Accurate Vibration Analysis." Energies 13, no. 2 (January 13, 2020): 389. http://dx.doi.org/10.3390/en13020389.
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Accurate diagnosis of incipient faults in wind turbine (WT) assets will provide sufficient lead time to apply an optimal maintenance for the expensive WT assets which often are located in a remote and harsh environment and their maintenance usually needs heavy equipment and highly skilled engineers. This paper presents an online bearing clearance monitoring approach to diagnose the change of bearing clearance, providing an early and interpretable indication of bearing health conditions. A novel dynamic load distribution method is developed to efficiently gain the general characteristics of vibration response of bearings without local defects but with small geometric errors. It shows that the ball pass frequency of outer race (BPFO) is the primary exciting source due to biased load distribution relating to bearing clearance. The geometric errors, including various orders of runouts on different bearing parts, can be the secondary excitation source. Both sources lead to compound modulation responses with very low amplitudes, being more than 20 dB lower than that of a small local defect on raceways and often buried by background noise. Then, Modulation Signal Bispectrum (MSB) is identified to purify the noisy signal and Gini index is introduced to represent the peakness of MSB results, thereby an interpretable indicator bounded between 0 and 1 is established to show bearing clearance status. Datasets from both a dedicated bearing test and a run-to-failure gearbox test are employed to verify the performance and reliability of the proposed approach. Results show that the proposed method is capable to indicate a change of about 20 µm in bearing clearance online, which provides a significantly long lead time compared to the diagnosis method that focuses only on local defects. Therefore, this method provides a big opportunity to implement more cost-effective maintenance works or carry out timely remedial actions to prolong the lifespan of bearings. Obviously, it is applicable to not only WT assets, but also most rotating machines.
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Ma, Gang, Ke He Wu, and Pan Pan. "Application of Monitoring Technology Based on Aeolian Vibration in Smart Grid." Advanced Materials Research 341-342 (September 2011): 672–77. http://dx.doi.org/10.4028/www.scientific.net/amr.341-342.672.
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The aeolian vibration of overhead transmission lines frequently brings about the breaking fatigue of wires, which makes a serious threat to the security of transmission lines.Today, more and more attentions have been paid to the aeolian vibration both in research and commercial realms . As aeolian vibration monitoring can accurately monitor wire damage, it is conducive to timely maintenance and avoiding accidents. In our paper, we propose a system model adopting bending amplitude method to estimate bending amplitude, calculate dynamic bending strain of wire and obtain early warning tips by comparing with strain chart based on the research of principles of aeolian vibration. Besides, A prototype system is developed to realize the effectiveness of the proposed model.
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Widodo, A., Dj Satrijo, T. Prahasto, and I. Haryanto. "Health State Indicator-Based Vibration Signature for Gearbox Condition Monitoring and Maintenance." IOP Conference Series: Materials Science and Engineering 598 (September 6, 2019): 012073. http://dx.doi.org/10.1088/1757-899x/598/1/012073.
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Daga, Alessandro Paolo, and Luigi Garibaldi. "Machine Vibration Monitoring for Diagnostics through Hypothesis Testing." Information 10, no. 6 (June 7, 2019): 204. http://dx.doi.org/10.3390/info10060204.
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Nowadays, the subject of machine diagnostics is gathering growing interest in the research field as switching from a programmed to a preventive maintenance regime based on the real health conditions (i.e., condition-based maintenance) can lead to great advantages both in terms of safety and costs. Nondestructive tests monitoring the state of health are fundamental for this purpose. An effective form of condition monitoring is that based on vibration (vibration monitoring), which exploits inexpensive accelerometers to perform machine diagnostics. In this work, statistics and hypothesis testing will be used to build a solid foundation for damage detection by recognition of patterns in a multivariate dataset which collects simple time features extracted from accelerometric measurements. In this regard, data from high-speed aeronautical bearings were analyzed. These were acquired on a test rig built by the Dynamic and Identification Research Group (DIRG) of the Department of Mechanical and Aerospace Engineering at Politecnico di Torino. The proposed strategy was to reduce the multivariate dataset to a single index which the health conditions can be determined. This dimensionality reduction was initially performed using Principal Component Analysis, which proved to be a lossy compression. Improvement was obtained via Fisher’s Linear Discriminant Analysis, which finds the direction with maximum distance between the damaged and healthy indices. This method is still ineffective in highlighting phenomena that develop in directions orthogonal to the discriminant. Finally, a lossless compression was achieved using the Mahalanobis distance-based Novelty Indices, which was also able to compensate for possible latent confounding factors. Further, considerations about the confidence, the sensitivity, the curse of dimensionality, and the minimum number of samples were also tackled for ensuring statistical significance. The results obtained here were very good not only in terms of reduced amounts of missed and false alarms, but also considering the speed of the algorithms, their simplicity, and the full independence from human interaction, which make them suitable for real time implementation and integration in condition-based maintenance (CBM) regimes.
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Hart, Genevieve A., Scott D. Moss, Dylan J. Nagle, and Steve C. Galea. "Endurance Testing of a Vibration Energy Harvester for Structural Health Monitoring." Advanced Materials Research 891-892 (March 2014): 1261–67. http://dx.doi.org/10.4028/www.scientific.net/amr.891-892.1261.
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The Australian Defence Science and Technology Organisation (DSTO) is developing a variety of in-situ structural health monitoring (SHM) approaches for potential use in high value platforms across the Australian Defence Force (ADF). The implementation of SHM systems would allow the ADF to move from expensive interval based inspection and maintenance regimes for ageing platforms to more cost-effective condition-based approaches, and therefore reduce aircraft through - life support costs. One critical issue is determining the optimal means of supplying power to these in-situ SHM systems. To address this issue DSTO has developed a bi-axial vibration energy harvesting approach based on a vibrating spherical-mass, magnet and wire-coil transducer arrangement. It is important that the vibration energy harvesting devices themselves are resistant to fatigue and wear related damage as they may need to operate in service for many years. This paper examines work done on mitigating wear effects in vibration energy harvesting devices, with the goal of ensuring device longevity.
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Marçal, Rui Francisco Martins, Kazuo Hatakeyama, and Dani Juliano Czelusniak. "Expert System Based on Fuzzy Rules for Monitoring and Diagnosis of Operation Conditions in Rotating Machines." Advanced Materials Research 1061-1062 (December 2014): 950–60. http://dx.doi.org/10.4028/www.scientific.net/amr.1061-1062.950.
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This work provides a detection method for failure in rotating machines based on a change of vibration pattern and offers the diagnosis about the operation conditions using Fuzzy Logic. A mechanic structure (as an experimental prototype where faults can be inserted) called Rotating System has been used. The vibration standard of the Rotating System, called "The Spectral Signature", has been obtained. The changes in the vibration standard have been analyzed and used as parameters for detecting incipient failures, as well as their condition evolution, allowing predictive monitoring and planning of maintenance. The faults analyzed in this work are caused due to insertion of asymmetric masses for unbalancing in the axle wheel. The system for diagnosing Fuzzy System was calibrated to detect and diagnose the conditions: normal, incipient failure, maintenance, and danger, using linguistic variables. The frequency of rotation and the amplitudes of vibration of the axle wheel are considered in each situation as parameters for analysis, diagnostic, for the decision by the Expert System based on Fuzzy rules. The results confirm that the proposed method is useful for detecting incipient failures, monitoring the evolution of severity and offering grants for planning and decision making about maintenance or prevention of rotating machines.
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Zhu, Jia Lin, and Li Li. "Monitoring Mechanical Vibration Amplitude System Design Based on the PVDF Piezoelectric Film Sensor." Applied Mechanics and Materials 556-562 (May 2014): 2110–13. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.2110.
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Vibration monitoring of machinery and equipment plays a very important role in judging the equipment maintenance and operational status. This paper discussed the mechanical vibration amplitude measurement system scheme based on PVDF piezoelectric sensors. The entire test system is mainly composed of PVDF piezoelectric film sensors, charge amplifiers, data acquisition and processing and display of valid values. It can display the amplitude of vibration signals in real time and alarm. The measurement system has the advantages of a simple structure and convenient operation.
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Kim, Yeon Whan, Ju-Young Ho, and Young Shin Lee. "DEVELOPMENT OF VIBRATION CONDITION MONITORING SYSTEM APPLYING OPTICAL SENSORS FOR GENERATOR WINDING INTEGRITY OF POWER UTILITIES." International Journal of Modern Physics: Conference Series 06 (January 2012): 98–103. http://dx.doi.org/10.1142/s2010194512003005.
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This paper describes the vibration condition monitoring diagnosis system developed for stator and rotor winding integrity assessment of 100MW class gas turbine generator in combined-cycle thermal power plant. High reliability of windings is one of the most essential prerequisite for generators of power utilities. Assessing the condition of stator winding insulation systems requires objective information from condition monitoring system. In-service monitoring is essential if a power plant is following a condition-based maintenance strategy. Generator damages are caused by the high vibration and the power system instability by secondary impacts of an unannounced plant stop and the life of the generator is decreased. The mechanical vibration in generator is induced by both mechanical and magnetic forces. The vibration condition monitoring system is required for the improved savings of operation and maintenance cost in terms of reliability in power plant.
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Esquincalha Luparelli, Marcela. "ENSAIO NÃO DESTRUTIVO (END) DE ANÁLISE DE VIBRAÇÕES APLICADO À MANUTENÇÃO PREDITIVA." Revista Científica Semana Acadêmica 9, no. 209 (September 17, 2021): 1–16. http://dx.doi.org/10.35265/2236-6717-206-9169.
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Predictive maintenance consists of monitoring data obtained from monitoring, with the main results of increasing the availability and reliability of equipment. Several non-destructive testing can be performed by this type of maintenance, with vibration analysis being one of the most common. The aim of this study is to evaluate the relevance of vibration analysis in the context of industrial predictive maintenance, as well as its applicability in mechanical and electrical equipment. The scientific production of this article was based on the experiments carried out by several authors in their areas of expertise, based on bibliographic source. It was concluded that the vibration analysis is an important test for the application of predictive maintenance, being able to be implemented in several equipments, besides being a method that contributes directly to the reduction of costs in the supply chain. Finally, it is necessary to take into account that there are points of attention that must be considered for a more adequate implementation of the method.
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Gao, Hong Li, Xiao Hui Shi, Ling Cong Feng, and Li Ping Xu. "Condition Monitoring and Life Prediction of Rolling Guide Based on Hybrid Intelligence." Applied Mechanics and Materials 44-47 (December 2010): 2045–49. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.2045.
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To evaluate accurately working condition of guide, make maintenance strategy, and predict its residual life in the process of machining operation, a rolling guide rail condition monitoring system based on neural networks was constructed after key factors to guide life were investigated carefully. Eight B&K 4321 three-way vibration sensor were installed on slider surface to monitor the on-line condition of four guides and eight sliders. Vibration signals were processed by wavelet packet decomposition and the most sensitive features to guide life were selected by fuzzy clustering method. The relation between guide life and input vectors including vibration features and machining condition was built by radial basis probabilistic neural networks (RBPNN), which parameters were optimized by genetic algorithm. The experimental results show maximum forecast error is 360 hours and minimum forecast error is 63 hours.
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Shi, Wenbo, Ming Li, Jingxuan Guo, and Kaixuan Zhai. "Evaluation of Road Service Performance Based on Human Perception of Vibration While Driving Vehicle." Journal of Advanced Transportation 2020 (December 22, 2020): 1–8. http://dx.doi.org/10.1155/2020/8825355.
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Road surface monitoring is a significant issue in providing smooth road infrastructure for vehicles, and the key to road condition monitoring is to detect road potholes that affect driving comfort and transportation safety. This paper presents a simple, efficient, and accurate way to evaluate road service performance based on the acquisition of road vibration data by vibration sensors installed in vehicles. Inspired by the discrete fast Fourier transform, the vibration acceleration is processed, and the RMS value of vibration acceleration at 1/2 octave is calculated, after which the road vibration level is calculated. The vibration level is optimized according to the human body’s sensitivity to different frequencies of vibration, resulting in road service performance indicators that can reflect the human body’s real feelings. According to the road service performance index values on the road grading, combined with GPS data on the electronic map color block labeling, the results obtained for the road condition warning, road maintenance, driver route selection have an important significance.
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Guo, Liang, Hongli Gao, Haifeng Huang, Xiang He, and ShiChao Li. "Multifeatures Fusion and Nonlinear Dimension Reduction for Intelligent Bearing Condition Monitoring." Shock and Vibration 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/4632562.
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Condition-based maintenance is critical to reduce the costs of maintenance and improve the production efficiency. Data-driven method based on neural network (NN) is one of the most used models for mechanical components condition recognition. In this paper, we introduce a new bearing condition recognition method based on multifeatures extraction and deep neural network (DNN). First, the method calculates time domain, frequency domain, and time-frequency domain features to represent characteristic of vibration signals. Then the nonlinear dimension reduction algorithm based on deep learning is proposed to reduce the redundancy information. Finally, the top-layer classifier of deep neural network outputs the bearing condition. The proposed method is validated using experiment test-bed bearing vibration data. Meanwhile some comparative studies are performed; the results show the advantage of the proposed method in adaptive features selection and superior accuracy in bearing condition recognition.
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Firlik, Bartosz, Maciej Tabaszewski, and Bogdan Sowinski. "Vibration-Based Symptoms in Condition Monitoring of a Light Rail Vehicle." Key Engineering Materials 518 (July 2012): 409–17. http://dx.doi.org/10.4028/www.scientific.net/kem.518.409.
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Light rail systems have now their great return in many European cities carrying an increasing number of people every year. This increasing trend requires suitable operation and maintenance standards for both vehicle and track. Furthermore, in order to make a public transport competitive to private transport, its very important to increase safety and ride comfort for passengers. The aim of the presented work was to determine the suitable vibration-based symptoms for the identification of a light rail vehicle technical state, as well as the development of appropriate methodology to use the information contained therein. Both simulation and experimental phase are described. The present analysis is focused mainly on the suspension state monitoring, but some others failures were also considered.
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Nathan, R. J., and M. P. Norton. "Vibration Signature Based Condition Monitoring of Bowl-Roller Coal Pulverizers." Journal of Vibration and Acoustics 115, no. 4 (October 1, 1993): 452–62. http://dx.doi.org/10.1115/1.2930372.
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The overall objective of the work reported in this paper is to minimize the cost of power generation in thermal power stations utilizing pulverized coal combustion processes for steam generation. The strategy of achieving this objective is based on an “on-condition maintenance” philosophy and vibration based diagnostic signature analysis techniques. The coal pulverizers reported on here are 783 RP (roll pressure) and 823 RP combustion engineering (CE) bowl-roller coal pulverizers (bowl mills) installed at the State Energy Commission of Western Australia (SECWA) power stations. This paper reviews the design philosophy, operational principles, and system dynamics and establishes the procedures for identifying the potential malfunction of bowl mills and their associated components. The influence of operating parameters, such as coal flow, primary air flow, and operating temperature, on mill vibration are investigated. The effects of journal spring force variation, such as magnitude, uneven spring force, and broken springs, are also studied. Special attention is also given to the diagnosis of the top radial bearing problem due to its remoteness from the bowl mill external structure. A spectral recovery technique utilizing the inverse frequency response function was developed for trend analysis and diagnostic purposes.
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Si, Jun Shan, Hui Zhu, Jian Yu, Qing Chun Meng, and Xian Jiang Shi. "Wind Turbine Gear Fault Diagnosis Experiment Research Based on Sensorless Detection." Applied Mechanics and Materials 427-429 (September 2013): 1191–95. http://dx.doi.org/10.4028/www.scientific.net/amm.427-429.1191.
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In the wind turbine gear fault detection, using conventional vibration monitoring exists installation and maintenance inconvenience and numerous other shortcomings, this often leads to the wind turbine vibration detection techniques are not widely promoted. Based on sensorless wind turbine gear fault diagnosis experiment research in this paper, the use of general-purpose inverter and induction motor, built a double-fed asynchronous induction generator simulation test bed, and had a broken tooth gear fault simulation experiments. Through the generator stator current signal and the gear vibration signal contrast and analysis, preliminary validation of the proposed method is effective and feasible.
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Chu, Y. C., T. N. Pham, F. R. Hsu, M. J. Tuw, C. W. Tan, M. C. Chay, S. C. Lim, and M. F. Tsai. "An effective method for monitoring the vibration data of bearings to diagnose and minimize defects." MATEC Web of Conferences 189 (2018): 03019. http://dx.doi.org/10.1051/matecconf/201818903019.
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Monitoring of vibration in machine tools is becoming a very important application in industry to reduce machine failures, maintenance costs, and dead time. In this paper, we propose a method to identify possible faults based on vibration data from which predictions about the working condition of the machine tools can be made. We used an accelerometer to collect the vibration data from which to analyse the health of machine tools by diagnosing whether they are in good or faulty condition for working. In our experiments, we introduced a machine called the Reliance Electric motor, which has a bearing running inside it. Our research analyses vibration data from components of the bearing including the outer bearing, inner bearing, and rolling element. The experimental results show that our method is highly accurate in diagnosing failures and significantly reduces the maintenance costs of machine tools.
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Qadir, Javed, Hameed Qaiser, Mehar Ali, and Masood Iqbal. "Condition monitoring of PARR-1 rotating machines by vibration analysis technique." Nuclear Technology and Radiation Protection 29, no. 3 (2014): 249–52. http://dx.doi.org/10.2298/ntrp1403249q.
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Vibration analysis is a key tool for preventive maintenance involving the trending and analysis of machinery performance parameters to detect and identify developing problems before failure and extensive damage can occur. A lab-based experimental setup has been established for obtaining fault-free and fault condition data. After this analysis, primary and secondary motor and pump vibration data of the Pakistan Research Reactor-1 were obtained and analyzed. Vibration signatures were acquired in horizontal, vertical, and axial directions. The 48 vibration signatures have been analyzed to assess the operational status of motors and pumps. The vibration spectrum has been recorded for a 2000 Hz frequency span with a 3200 lines resolution. The data collected should be helpful in future Pakistan Research Reactor-1 condition monitoring.
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Coscetta, Agnese, Aldo Minardo, Lucio Olivares, Maurizio Mirabile, Mario Longo, Michele Damiano, and Luigi Zeni. "Wind Turbine Blade Monitoring with Brillouin-Based Fiber-Optic Sensors." Journal of Sensors 2017 (2017): 1–5. http://dx.doi.org/10.1155/2017/9175342.
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Wind turbine (WT) blade is one of the most important components in WTs, as it is the key component for receiving wind energy and has direct influence on WT operation stability. As the size of modern turbine blade increases, condition monitoring and maintenance of blades become more important. Strain detection is one of the most effective methods to monitor blade conditions. In this paper, a distributed fiber-optic strain sensor is used for blade monitoring. Preliminary experimental tests have been carried out over a 14 m long WT composite blade, demonstrating the possibility of performing distributed strain and vibration measurements.
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Zhang, Jin, Yue Gu, Jia Xu, and Jian Guo Wang. "Research and Application of 'Distribution Acquisition-Centralized Analysis' Vibration Monitoring Mode for Wind Turbine." Advanced Materials Research 827 (October 2013): 264–69. http://dx.doi.org/10.4028/www.scientific.net/amr.827.264.
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With the rapid development of wind power in recent years, the wind power industry has gradually shifted from infrastructure construction to equipment operation and maintenance. The importance of the operation and maintenance of the wind turbine is increasingly apparent. A new vibration monitoring mode of wind turbine, called distribution acquisition-Centralized analysis, has been established by full investigation and study. This mode involves information technology, electronics technology and automation control technology. It is based on the mechanical movement principle and the principles of mechanics analysis. Throughout the one-year application of this vibration monitoring mode in wind farms, it is obseraved that: this model can grasp the operational status of the key equipment of the wind turbine, and effectively identify potential failures in a timely manner. This mode reduces costs of maintenance, improves equipment reliability and extends equipments operation time.
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Zhou, Biao, Xiongyao Xie, and Xiaojian Wang. "The Tunnel Structural Mode Frequency Characteristics Identification and Analysis Based on a Modified Stochastic Subspace Identification Method." Shock and Vibration 2018 (December 2, 2018): 1–12. http://dx.doi.org/10.1155/2018/6595841.
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With the rapid development of underground engineering in China, the heavy structural maintenance work followed is expected to be a great challenge in the future. The development also provides a promising application prospect for the newly developed vibration-based health assessment and monitoring methods. However, the fact that tunnels are embedded in soil makes collecting and identifying the vibration characteristics more difficult, especially for the online monitoring. In this paper, a new identification method that combines the natural excitation technique (NExT) and stochastic subspace identification (SSI) method is developed. The new method is compared with the traditional SSI method, and mode frequency analysis is made based on a series of field tests carried out at the subway and power tunnel. It is found that both stability and efficiency of the mode frequency identification have been greatly improved, and it more suitable for online monitoring. Meanwhile, a mathematical model is used to analyze the original mode characteristics and the influence of soil coupling. The results are also compared with the field tests results by using the NExT-SSI method, and some recommendations are also made for how to choose the vibration modals for vibration-based monitoring in the tunnel.
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Koenig, Frank, Pauline Anne Found, and Maneesh Kumar. "Condition monitoring for airport baggage handling in the era of industry 4.0." Journal of Quality in Maintenance Engineering 25, no. 3 (August 16, 2019): 435–51. http://dx.doi.org/10.1108/jqme-03-2018-0014.
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Purpose The purpose of this paper is to present the findings of a recent study conducted with the objective of addressing the problem of failure of baggage carts in the high-speed baggage tunnel at Heathrow Terminal 5 by the development of an innovative condition-based maintenance system designed to meet the requirements of twenty-first century airport systems and Industry 4.0. Design/methodology/approach An empirical experimental approach to this action research was taken to install a vibration condition monitoring pilot test in the north tunnel at Terminal 5. Vibration data were collected over a 6-month period and analysed to find the threshold of good quality tires and those with worn bearings that needed replacement. The results were compared with existing measures to demonstrate that vibration monitoring could be used as a predictive model for condition-based maintenance. Findings The findings demonstrated a clear trend of increasing vibration velocity with age and use of the baggage cart wheels caused by wheel mass unbalanced inertia that was transmitted to the tracks as vibration. As a result, preventative maintenance is essential to ensure the smooth running of airport baggage. This research demonstrates that a healthy wheel produces vibration of under 60 mm/s whereas a damaged wheel measures up to 100 mm/s peak-to-peak velocity and this can be used in real-time condition monitoring to prevent baggage cart failure. It can also run as an autonomous system linked to AI and Industry 4.0 airport logic. Originality/value Whilst vibration monitoring has been used to measure movement in static structures such as bridges and used in rotating machinery such as railway wheels (Tondon and Choudhury, 1999) this is unique as it is the first time it has been applied on a stationary structure (tracks) carrying high-speed rotating machinery (baggage cart wheels). This technique has been patented and proven in the pilot study and is in the process of being rolled out to all Heathrow terminal connection tunnels. It has implications for all other airports world-wide and, with new economic sensors, to other applications that rely on moving conveyor belts.
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Fu, Sheng, and Hao Lin. "Design of Mine Ventilators Monitoring System Based on ZigBee." Applied Mechanics and Materials 385-386 (August 2013): 626–30. http://dx.doi.org/10.4028/www.scientific.net/amm.385-386.626.
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In the current mine ventilators monitoring system, there are some difficulties in the installation and maintenance because of the wired connection. To solve the problem, this paper introduces a new ventilators monitoring system based on ZigBee in which the monitoring parameters are transmitted wirelessly. The collecting devices are designed to form a cluster network to collect temperature, vibration, air pressure and other parameters of the mine ventilator. All these devices are battery-powered. Besides, the monitoring software in PC is developed using MFC. Experiments show that the designed wireless sensor network works well in the site environmental condition and the system is very convenient to install since the wireless connection. This monitoring system will have a wide application prospect in the upgrade of the old monitoring system of the ventilators.
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Bianchini, Augusto, Jessica Rossi, and Lauro Antipodi. "A procedure for condition-based maintenance and diagnostics of submersible well pumps through vibration monitoring." International Journal of System Assurance Engineering and Management 9, no. 5 (February 10, 2018): 999–1013. http://dx.doi.org/10.1007/s13198-018-0711-3.
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Chang, Hong-Chan, Yu-Ming Jheng, Cheng-Chien Kuo, and Yu-Min Hsueh. "Induction Motors Condition Monitoring System with Fault Diagnosis Using a Hybrid Approach." Energies 12, no. 8 (April 18, 2019): 1471. http://dx.doi.org/10.3390/en12081471.
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This study develops a condition monitoring system, which includes operating condition monitoring (OCM) and fault diagnosis analysis (FDA). The OCM uses a vibration detection approach based on the ISO 10816-1 and NEMA MG-1 international standards, and the FDA uses a vibration-electrical hybrid approach based on various indices. The system can acquire real-time vibration and electrical signals. Once an abnormal vibration has been detected by using OCM, the FDA is applied to classify the type of faults. Laboratory results indicate that the OCM can successfully diagnose induction motors healthy condition, and FDA can classify the various damages stator fault, rotor fault, bearing fault and eccentric fault. The FDA with the hybrid approach is more reliable than the traditional approach using electrical detection alone. The proposed condition monitoring system can provide simple and clear maintenance information to improve the reliability of motor operations.
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Gómez, María Jesús, Cristina Castejón, Eduardo Corral, and Juan Carlos García-Prada. "Railway Axle Condition Monitoring Technique Based on Wavelet Packet Transform Features and Support Vector Machines." Sensors 20, no. 12 (June 24, 2020): 3575. http://dx.doi.org/10.3390/s20123575.
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Railway axles are critical to the safety of railway vehicles. However, railway axle maintenance is currently based on scheduled preventive maintenance using Nondestructive Testing. The use of condition monitoring techniques would provide information about the status of the axle between periodical inspections, and it would be very valuable in the prevention of catastrophic failures. Nevertheless, in the literature, there are not many studies focusing on this area and there is a lack of experimental data. In this work, a reliable real-time condition-monitoring technique for railway axles is proposed. The technique was validated using vibration measurements obtained at the axle boxes of a full bogie installed on a rig, where four different cracked railway axles were tested. The technique is based on vibration analysis by means of the Wavelet Packet Transform (WPT) energy, combined with a Support Vector Machine (SVM) diagnosis model. In all cases, it was observed that the WPT energy of the vibration signals at the first natural frequency of the axle when the wheelset is first installed (the healthy condition) increases when a crack is artificially created. An SVM diagnosis model based on the WPT energy at this frequency demonstrates good reliability, with a false alarm rate of lower than 10% and defect detection for damage occurring in more than 6.5% of the section in more than 90% of the cases. The minimum number of wheelsets required to build a general model to avoid mounting effects, among others things, is also discussed.
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Holroyd, Trevor J. "Use of AE Based Instrumentation to Monitor Machinery Condition in the Industrial Environment." Advanced Materials Research 13-14 (February 2006): 45–50. http://dx.doi.org/10.4028/www.scientific.net/amr.13-14.45.
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The use of AE by maintenance personnel for monitoring the condition of rotating machinery on the industrial shop floor is now well established and provides both a quick and effective assessment. Despite early resistance, especially by those accustomed to vibration based monitoring, it now enjoys a widespread acceptance. The development of signal processing routines and instrumentation specifically for the condition monitoring role has been a major factor in this achievement. Experience has shown that as a portable instrument AE can be very quickly applied and give instant indications of machine condition with high sensitivity to fault conditions. Appropriately pre-processed AE signals are particularly useful for on-line monitoring since the fault indications are in general less affected by changes in operating conditions than vibration based techniques as well as being far simpler to interpret. This is especially important where many machines are being simultaneously monitored. This paper discusses the accompanying developments and presents illustrative application examples.
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Pan, Zhi Yong, Quan Cai Wang, and Wei Hong Ren. "Based on the Online Monitoring and Fault Diagnosis System of the Main Hoist for Mine." Applied Mechanics and Materials 42 (November 2010): 250–54. http://dx.doi.org/10.4028/www.scientific.net/amm.42.250.
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According to the reality, an online monitoring and fault diagnosis system of the main hoist for Mine was designed in this article. The system adopts the signal acquisition and processing, fault diagnosis, Web visualization, network real-time database and other related technologies, Real-time monitoring the current, voltage, temperature, speed, vibration and other parameters of the main elevators to Achieve the goals that Increasing efficiency by downsizing, protecting the safe operation of equipment, reducing the maintenance costs.
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Ling, Hao Yi, Ling Mei Wang, and Xing Yong Zhao. "Transmission Network Status Monitoring Overview." Applied Mechanics and Materials 325-326 (June 2013): 599–603. http://dx.doi.org/10.4028/www.scientific.net/amm.325-326.599.
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Transmission lines is an important part of the power system. Transmission line condition monitoring system can enhance the operational reliability of the grid line level of safety, at the same time lay the foundation for intelligent transmission line. Insulator contamination monitoring , lightning monitoring, environmental monitoring, wire breeze vibration monitoring online monitoring technology on the existing transmission line condition monitoring technologies , including comparative analysis of the far-reaching. It can reduce the workload of the artificial line inspection , to reduce the occurrence of pollution flashover to improve power supply reliability. To reduce the pollution flashover occurred to improve the reliability of power supply. Condition based maintenance decision support and sharing of information with other systems. HOMER and MATLAB simulation software , simulation , historical data analysis . Export real - time wind speed data, provide data to support the conductor galloping and aeolian vibration of monitoring and environmental monitoring.
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Mathias, Mauro Hugo, Everton Coelho de Medeiros, and Valdeci Donizete Gonçalves. "Design of a LabVIEW System Applied to Predictive Maintenance." Applied Mechanics and Materials 249-250 (December 2012): 208–12. http://dx.doi.org/10.4028/www.scientific.net/amm.249-250.208.
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The implementation of vibration analysis techniques based on virtual instrumentation has spread increasingly in the academic and industrial branch, since the use of any software for this type of analysis brings good results at low cost. Among the existing software for programming and creation of virtual instruments, the LabVIEW was chosen for this project. This software has good interface with the method of graphical programming. In this project, it was developed a system of rotating machine condition monitoring. This monitoring system is applied in a test stand, simulating large scale applications, such as in hydroelectric, nuclear and oil exploration companies. It was initially used a test stand, where an instrumentation for data acquisition was inserted, composed of accelerometers and inductive proximity sensors. The data collection system was structured on the basis of an NI 6008 A/D converter of National Instruments. An electronic circuit command was developed through the A/D converter for a remote firing of the test stand. The equipment monitoring is performed through the data collected from the sensors. The vibration signals collected by accelerometers are processed in the time domain and frequency. Also, proximity probes were used for the axis orbit evaluation and an inductive sensor for the rotation and trigger measurement.
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Paul, Sumit, Wolfgang Legner, Angelika Krenkow, Gerhard Müller, Thierry Lemettais, Francois Pradat, and Delphine Hertens. "Chemical Contamination Sensor for Phosphate Ester Hydraulic Fluids." International Journal of Aerospace Engineering 2010 (2010): 1–9. http://dx.doi.org/10.1155/2010/156281.
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The paper deals with chemical contamination monitoring in phosphate-ester-based hydraulic fluids using nondispersive infrared (NDIR) optical absorption. Our results show that NDIR monitoring allows detecting the take-up of water into such fluids and their hydrolytic disintegration as these become additionally stressed by Joule heating. Observations on the O–H stretching vibration band (3200–3800 ) are used for determining the free water content (0–1.5%) and the Total Acid Number (0–1 mgKOH/g). Both quantities can be assessed by monitoring the strength and the asymmetry of the O–H vibration band with regard to the free water absorption band centred around 3500 . As such optical parameters can be assessed without taking fluid samples from a pressurised hydraulic system, fluid degradation trends can be established based on regular measurements, before irreversible damage to the fluid has occurred. Therefore maintenance actions can be planned accordingly, which is very important for the airline, as unscheduled maintenance disturbs the flights organisation and often generates money loss.
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Sharma, Amandeep, Lini Mathew, Shantanu Chatterji, and Deepam Goyal. "Artificial Intelligence-Based Fault Diagnosis for Condition Monitoring of Electric Motors." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 13 (May 11, 2020): 2059043. http://dx.doi.org/10.1142/s0218001420590430.
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In the era of globalization, manufacturing industries are facing intense pressure to prevent unexpected breakdowns, reduce maintenance cost and increase plant availability. Induction motors are the most sought-after prime movers in modern-day industries due to their robustness. Recently, research has picked up a fervent pace in the area of fault diagnosis of electrical machines. This paper presents the application of Support Vector Machine (SVM) and Artificial Neural Network (ANN)-based system to diagnose the vibration and Instantaneous Power (IP)-based responses of rolling element bearings and broken rotor bars in an induction motor. The dimensionality of the extracted features was reduced using Principal Component Analysis (PCA) and thereafter the selected features were ranked in order of relevance using the Sequential Floating Forward Selection (SFFS) method for reducing the size of input features and finding the most optimal feature set. A comparative analysis of the effectiveness of SVM and ANN is carried out using statistical parameters extracted from vibration and IP signals. The highest accuracy of 92.5% and 98.2% was achieved for vibration and IP signatures, respectively, using the proposed SFFS-based feature selection technique and ANN classification method. The results reveal that ANN has better performance than SVM and the proposed strategy can be used for automatic recognition of machine faults. The use of this type of intelligent system helps in avoiding unwanted and unplanned system shutdowns due to the failure of the motor.
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Hernández-Muriel, José Alberto, Jhon Bryan Bermeo-Ulloa, Mauricio Holguin-Londoño, Andrés Marino Álvarez-Meza, and Álvaro Angel Orozco-Gutiérrez. "Bearing Health Monitoring Using Relief-F-Based Feature Relevance Analysis and HMM." Applied Sciences 10, no. 15 (July 28, 2020): 5170. http://dx.doi.org/10.3390/app10155170.
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Nowadays, bearings installed in industrial electric motors are constituted as the primary mode of a failure affecting the global energy consumption. Since industries’ energy demand has a growing tendency, interest for efficient maintenance in electric motors is decisive. Vibration signals from bearings are employed commonly as a non-invasive approach to support fault diagnosis and severity evaluation of rotating machinery. However, vibration-based diagnosis poses a challenge concerning the signal properties, e.g., highly dynamic and non-stationary. Here, we introduce a knowledge-based tool to analyze multiple health conditions in bearings. Our approach includes a stochastic feature selection method, termed Stochastic Feature Selection (SFS), highlighting and interpreting relevant multi-domain attributes (time, frequency, and time–frequency) related to the bearing faults discriminability. In particular, a relief-F-based ranking and a Hidden Markov Model are trained under a windowing scheme to achieve our SFS. Obtained results in a public database demonstrate that our proposal is competitive compared to state-of-the-art algorithms concerning both the number of features selected and the classification accuracy.
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Ali Lilo, Moneer, and Maath Jasem Mahammad. "Design and implementation of wireless system for vibration fault detection using fuzzy logic." IAES International Journal of Artificial Intelligence (IJ-AI) 9, no. 3 (September 1, 2020): 545. http://dx.doi.org/10.11591/ijai.v9.i3.pp545-552.
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This paper aims at constructing the wireless system for fault detecting and monitoring by computer depending on the wireless and fuzzy logic technique. Wireless applications are utilized to identify, classify, and monitor faults in the real time to protect machines from damage .Two schemes were tested; first scheme fault collected X-Y-Z-axes mode while the second scheme collected Y-axis mode, which is utilized to protect the induction motor (IM) from vibrations fault. The vibration signals were processed in the central computer to reduce noise by signal processing stage, and then the fault was classified and monitored based on Fuzzy Logic (FL). The wireless vibration sensor was designed depending on the wireless techniques and C++ code. A fault collection, noise reduction, vibration fault classification and monitoring were implemented by MATLAB code. In the second scheme the processed real time was reduced to 60%, which is included collection, filtering, and monitoring fault level. Results showed that the system has the ability to early detect the fault if appears on the machine with time processing of 1.721s. This work will reduce the maintenance cost and provide the ability to utilize the system with harsh industrial applications to diagnose the fault in real time processing.
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He, Miao, David He, Jae Yoon, Thomas J. Nostrand, Junda Zhu, and Eric Bechhoefer. "Wind turbine planetary gearbox feature extraction and fault diagnosis using a deep-learning-based approach." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 233, no. 3 (April 23, 2018): 303–16. http://dx.doi.org/10.1177/1748006x18768701.
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Planetary gearboxes are widely used in the drivetrain of wind turbines. Planetary gearbox fault diagnosis is very important for reducing the downtime and maintenance cost and improving the safety, reliability, and life span of the wind turbines. The wind energy industry is currently using condition monitoring systems to collect massive real-time data and conventional vibratory analysis as a standard method for planetary gearbox condition monitoring. As an attractive option to process big data for fault diagnosis, deep learning can automatically learn features that otherwise require much skill, time, and experience. This article presents a new deep-learning-based method for wind turbine planetary gearbox fault diagnosis developed by a large memory storage and retrieval neural network with dictionary learning. The developed approach can automatically extract self-learned fault features from raw vibration monitoring data and perform planetary gearbox fault diagnosis without supervised fine-tuning process. From the raw vibration monitoring data, a dictionary is first learned by a large memory storage and retrieval with dictionary learning network. Based on the learned dictionary, a sparse representation of the raw vibration signals is generated by shift-invariant sparse coding and input to a large memory storage and retrieval network classifier to obtain fault diagnosis results. The structure of the large memory storage and retrieval with dictionary learning is determined by optimal selection of the sliding box size to generate sub-patterns from the vibration data. The effectiveness of the presented method is tested and validated with a set of seeded fault vibration data collected at a planetary gearbox test rig in laboratory. The validation results have shown a promising planetary gearbox fault diagnosis performance with the presented method.
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Kamran, M. S., A. ur Rehman, M. Adnan, H. Ali, and F. Noor. "Diagnostics of reciprocating machines using vibration analysis and ultrasound techniques." Insight - Non-Destructive Testing and Condition Monitoring 61, no. 11 (November 1, 2019): 676–82. http://dx.doi.org/10.1784/insi.2019.61.11.676.
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This paper presents a condition monitoring technique for the identification and correction of faults in reciprocating machines by recording engine vibration signals arising from different processes. The proposed data acquisition set-up allows the analyst to record vibration signals from different locations, analyse them and make the appropriate decisions regarding predictive maintenance. Data are recorded before and after carrying out major overhauling to assess the effectiveness of the proposed condition monitoring technique. For a complete health assessment, vibration signals and ultrasonic signature with respect to the crank angle are recorded and interpreted. All of the data are obtained at constant engine speed to depict the mechanical condition of the components and indicate any poor performance. A scuffed main bearing and a broken piston ring are diagnosed successfully. Suspected components are replaced based upon the findings of the initial analysis and data are recorded again to verify the results. Post-evaluation data confirm a normal mechanical condition and satisfactory performance except for some initial friction. The main objective of this experimentation is the identification of major tasks related to maintenance prior to major overhauling. This work shows that the use of non-intrusive condition monitoring techniques can reduce the frequency of failures by identifying developing faults.
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Cornel, Daniel, Francisco Gutiérrez Guzmán, Georg Jacobs, and Stephan Neumann. "Condition monitoring of roller bearings using acoustic emission." Wind Energy Science 6, no. 2 (March 5, 2021): 367–76. http://dx.doi.org/10.5194/wes-6-367-2021.
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Abstract. Roller bearing failures in wind turbines' gearboxes lead to long downtimes and high repair costs, which could be reduced by the implementation of a predictive maintenance strategy. In this paper and within this context, an acoustic-emission-based condition monitoring system is applied to roller bearing test rigs with the aim of identifying critical operating conditions before bearing failures occurs. Furthermore, a comparison regarding detection times is carried out with traditional vibration-based condition monitoring systems, with a focus on premature bearing failures such as white etching cracks. The investigations show a sensitivity of the acoustic-emission system towards lubricating conditions. In addition, the system has shown that a damaged surface can be detected at least ∼ 4 % (8 h, regarding the time to failure) earlier than by using the vibration-based system. Furthermore, significant deviations from the average acoustic-emission signal were detected up to ∼ 50 % (130 h) before the test stop and are possibly related to sub-surface damage initiation and might result in an earlier damage detection in the future.
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Du, Yan, Yingpin Chen, Guoying Meng, Jun Ding, and Yajing Xiao. "Fault Severity Monitoring of Rolling Bearings Based on Texture Feature Extraction of Sparse Time–Frequency Images." Applied Sciences 8, no. 9 (September 3, 2018): 1538. http://dx.doi.org/10.3390/app8091538.
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Rolling bearings are important components of rotating machines. For their preventive maintenance, it is not enough to know whether there is any fault or the fault type. For an effective maintenance, a fault severity monitoring needs to be conducted. Currently, the bearing fault diagnosis method based on time–frequency image (TFI) recognition is attracting increasing attention. This paper contributes to the ongoing investigation by proposing a new approach for the fault severity monitoring of rolling bearings based on the texture feature extraction of sparse TFIs. The first and main step is to obtain accurate TFIs from the vibration signals of rolling bearings. Traditional time–frequency analysis methods have disadvantages such as low resolution and cross-term interference. Therefore, the TFIs obtained cannot satisfactorily express the time–frequency characteristics of bearing vibration signals. To solve this problem, a sparse time–frequency analysis method based on the first-order primal-dual algorithm (STFA-PD) was developed in this paper. Unlike traditional time–frequency analysis methods, the time–frequency analysis model of the STFA-PD method is based on the theory of sparse representation, and is solved using the first-order primal-dual algorithm. For employing the sparse constraint in the frequency domain, the STFA-PD obtains a higher time–frequency resolution and is free from cross-term interference, as the model is based on a linear time–frequency analysis method. The gray level co-occurrence matrix is then employed to extract texture features from the sparse TFIs as input features for classifiers. Vibration signals of rolling bearings with different fault severity degrees are used to validate the proposed approach. The experimental results show that the developed STFA-PD outperforms traditional time–frequency analysis methods in terms of the accuracy and effectiveness for the fault severity monitoring of rolling bearings.
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Saleh, Yusuf, Muhammad Sani Yahya, and Aliyu Bukar Dala. "Vibro-Acoustics for Reli-ability Modeling of Un-derwater Pipeline using Wireless Sensor Network (WSN), with Minimum Energy Consumption." International Journal of Engineering & Technology 7, no. 3.36 (May 6, 2018): 118. http://dx.doi.org/10.14419/ijet.v7i3.36.29090.
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WSN is a network of clusters of sensors nodes which sense the parameter and communicates to a server. Target is to develop underwater pipeline monitoring system remotely with lower energy consumption. Reliability maintenance will effectively give optimum performance of pipeline system operation. Reliability ensures optimum performance in the range of operation based on the behavior of the system. Validation of the mathematical model for the flow in pipeline system is used to study vibrations as elements that affect the reliability of the pipeline. In this, the focus is to validate and model the reliability for the pipeline system from the vibrations for predictive maintenance and optimum performance. Subsequently, vibro-acoustics will be used to model the reliability equation. A software can be developed for simulating the reliability model for pipeline system behavior from the vibration factors and use for predictive maintenance. Reliability factors of mean time to failure, repair and others will be put in to use to develop the original model for the pipeline. A hypothesis of 90% predictive model from the reliability under pipeline system behavior will be developed.
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Borissova, Daniela, Ivan Mustakerov, and Lyubka Doukovska. "Predictive Maintenance Sensors Placement by Combinatorial Optimization." International Journal of Electronics and Telecommunications 58, no. 2 (June 1, 2012): 153–58. http://dx.doi.org/10.2478/v10177-012-0022-6.
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Predictive Maintenance Sensors Placement by Combinatorial Optimization The strategy of predictive maintenance monitoring is important for successful system damage detection. Maintenance monitoring utilizes dynamic response information to identify the possibility of damage. The basic factors of faults detection analysis are related to properties of the structure under inspection, collect the signals and appropriate signals processing. In vibration control, structures response sensing is limited by the number of sensors or the number of input channels of the data acquisition system. An essential problem in predictive maintenance monitoring is the optimal sensor placement. The paper addresses that problem by using mixed integer linear programming tasks solving. The proposed optimal sensors location approach is based on the difference between sensor information if sensor is present and information calculated by linear interpolation if sensor is not present. The tasks results define the optimal sensors locations for a given number of sensors. The results of chosen sensors locations give as close as possible repeating the curve of structure dynamic response function. The proposed approach is implemented in an algorithm for predictive maintenance and the numerical results indicate that together with intelligent signal processing it could be suitable for practical application.
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Pacheco, João, Gustavo Oliveira, Filipe Magalhães, Carlos Moutinho, and Álvaro Cunha. "Vibration-Based Monitoring of Wind Turbines: Influence of Layout and Noise of Sensors." Energies 14, no. 2 (January 15, 2021): 441. http://dx.doi.org/10.3390/en14020441.
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The reduction in operating and maintenance costs of wind farms is a fundamental element to guarantee the competitiveness and growth of the wind market. Wind turbines are highly dynamic structures prone to wear during their lifetime. Therefore, dynamic monitoring systems represent an excellent option to continuously evaluate their structural conditions. These systems allow early detection of damages, permit a proactive response, minimising downtime, and maximising productivity. In this context, the present paper describes the main results obtained with alternative instrumentation strategies tested in a 2.0 MW onshore wind turbine to reduce the costs of the monitoring equipment and at the same time ensure an adequate accuracy in structural condition evaluation. The data processing strategy encompasses the use of operational modal analysis combined with algorithms that deal with the particularities of operation of the wind turbines to continuously track the main vibration modes. After this automated online identification, the influence of the environmental and operating conditions on the tracked natural frequencies is mitigated, making the detection of abnormal variations of the natural frequencies possible, which might flag the appearance of damage. A database of continuously collected acceleration time series during one year is adopted to test the efficiency of alternative monitoring system layouts in detecting simulated damage scenarios. The tested alternative monitoring layouts present a varying number of sensors, alternative distributions in the wind turbine tower, and different sensor noise levels.
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Jiang, Xiaomo, Fumin Wang, Haixin Zhao, Shengli Xu, and Lin Lin. "Novel Orbit-based CNN Model for Automatic Fault Identification of Rotating Machines." Annual Conference of the PHM Society 12, no. 1 (November 3, 2020): 7. http://dx.doi.org/10.36001/phmconf.2020.v12i1.1147.
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Various faults in high-fidelity turbomachinery such as steam turbines and centrifugal compressors usually result in unplanned outage thus lowering the reliability and productivity while largely increasing the maintenance costs. Condition monitoring has been increasingly applied to provide early alerting on component faults by using the vibration signals. However, each type of fault in different types of rotating machines usually require an individual model to isolate the damage for accurate condition monitoring, which require costly computation efforts and resources due to the data uncertainties and modeling complexity. This paper presents a generalized deep learning methodology for accurately automatic diagnostics of various faults in general rotating machines by utilizing the shaft orbits generated from vibration signals, considering the high non-linearity and uncertainty of the sensed vibration signals. The sensor anomalies and environmental noise in the vibration signals are first addressed through waveform compensation and Bayesian wavelet noise reduction filtering. Shaft orbit images are generated from the cleansed vibration data collected from different turbomachinery with various fault modes. A multi-layer convolutional neural network model is then developed to classify and identify the shaft orbit images of each fault. Finally, the fault diagnosis of rotating machinery is realized through the automated identification process. The proposed approach retains the fault information in the axis trajectory to the greatest extent, and can adeptly extract and accurately identify features of various faults. The effectiveness and feasibility of the proposed methodology is demonstrated by using the sensed vibration signals collected from real-world centrifugal compressors and steam turbines with different fault modes.
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Jaber, Alaa Abdulhady, and Robert Bicker. "Design of a Wireless Sensor Node for Vibration Monitoring of Industrial Machinery." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 2 (April 1, 2016): 639. http://dx.doi.org/10.11591/ijece.v6i2.9296.
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Machine healthy monitoring is a type of maintenance inspection technique by which an operational asset is monitored and the data obtained is analysed to detect signs of degradation, diagnose the causes of faults and thus reducing the maintenance costs. Vibration signals analysis was extensively used for machines fault detection and diagnosis in various industrial applications, as it respond immediately to manifest itself if any change is appeared in the monitored machine. However, recent developments in electronics and computing have opened new horizons in the area of condition monitoring and have shown their practicality in fault detection and diagnosis processes. The main aim of using wireless embedded systems is to allow data analysis to be carried out locally at field level and transmitting the results wirelessly to the base station, which as a result will help to overcome the need for wiring and provides an easy and cost-effective sensing technique to detect faults in machines. So, the main focuses of this research is to design and develop an online condition monitoring system based on wireless embedded technology that can be used to detect and diagnose the most common faults in the transmission systems (gears and bearings) of an industrial robot joints using vibration signal analysis.
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Jaber, Alaa Abdulhady, and Robert Bicker. "Design of a Wireless Sensor Node for Vibration Monitoring of Industrial Machinery." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 2 (April 1, 2016): 639. http://dx.doi.org/10.11591/ijece.v6i2.pp639-653.
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Machine healthy monitoring is a type of maintenance inspection technique by which an operational asset is monitored and the data obtained is analysed to detect signs of degradation, diagnose the causes of faults and thus reducing the maintenance costs. Vibration signals analysis was extensively used for machines fault detection and diagnosis in various industrial applications, as it respond immediately to manifest itself if any change is appeared in the monitored machine. However, recent developments in electronics and computing have opened new horizons in the area of condition monitoring and have shown their practicality in fault detection and diagnosis processes. The main aim of using wireless embedded systems is to allow data analysis to be carried out locally at field level and transmitting the results wirelessly to the base station, which as a result will help to overcome the need for wiring and provides an easy and cost-effective sensing technique to detect faults in machines. So, the main focuses of this research is to design and develop an online condition monitoring system based on wireless embedded technology that can be used to detect and diagnose the most common faults in the transmission systems (gears and bearings) of an industrial robot joints using vibration signal analysis.
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Baban, Marius, Calin Florin Baban, and Marius Darius Suteu. "Maintenance Decision-Making Support for Textile Machines: A Knowledge-Based Approach Using Fuzzy Logic and Vibration Monitoring." IEEE Access 7 (2019): 83504–14. http://dx.doi.org/10.1109/access.2019.2923791.
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