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Journal articles on the topic 'Condition monitoring and vibration analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 February 2022

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1

Oba, Takuya, Koichi Yamada, Hitoshi Soma, and Katsuya Tanifuji. "356776 CONDITION MONITORING FOR SHINKANSEN BOGIES BASED ON VIBRATION ANALYSIS(Condition Monitoring,Technical Session)." Proceedings of International Symposium on Seed-up and Service Technology for Railway and Maglev Systems : STECH 2009 (2009): _356776–1_—_356776–6_. http://dx.doi.org/10.1299/jsmestech.2009._356776-1_.

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2

Xiao, Feng, Chen Tian, Isaac Wait, Zhaohui (Joey) Yang, Benjamin Still, and Gang S. Chen. "Condition monitoring and vibration analysis of wind turbine." Advances in Mechanical Engineering 12, no. 3 (March 2020): 168781402091378. http://dx.doi.org/10.1177/1687814020913782.

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Health condition monitoring through comprehensive monitoring, incipient fault diagnosis, and the prediction of impending faults allows for the promotion of the long-term performance of wind turbines, particularly those in harsh environments such as cold regions. The condition monitoring of wind turbines is characterized by the difficulties associated with the lack of measured data and the nonstationary, stochastic, and complicated nature of vibration responses. This article presents a characterization of the vibrations of an operational wind turbine by spectrogram, scalogram, and bi-spectrum analyses. The results reveal varied nonstationary stochastic properties and mode-coupling instability in the vibrations of the tested wind turbine tower. The analysis illustrates that the wind turbine system vibrations exhibit certain non-Gaussian stochastic properties. An analytical model is used to evaluate the nonstationary, stochastic phenomena and mode-coupling phenomena observed in the experimental results. These results are of significance for the fault diagnosis of wind turbine system in operation as well as for improving fatigue designs beyond the wind turbulence spectral models recommended in the standards.

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3

Bensalem, A., C. A. Fairfield, and A. Sibbald. "Vibration Condition Monitoring of Brickwork Columns." Journal of Low Frequency Noise, Vibration and Active Control 16, no. 1 (March 1997): 25–42. http://dx.doi.org/10.1177/026309239701600103.

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This paper describes the development of a condition monitoring and analysis technique, based on the pulse echo and impulse excitation method, for brickwork structures. The method is applied to structural brickwork columns with different brick and mortar strength combinations. Dynamic time integration finite element analysis modelled the columns’ behaviour under impact loading. The results were transformed to the frequency domain for obtaining the resonant frequencies: the results were successfully compared to those found experimentally and by natural frequency analysis. The effect of a controlled defect was then analysed. Good prediction of dynamic response and resonant frequencies was achieved and the technique is recommended for condition monitoring of brickwork structures.

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4

Yesilyurt, I., and H. Ozturk. "Tool condition monitoring in milling using vibration analysis." International Journal of Production Research 45, no. 4 (February 15, 2007): 1013–28. http://dx.doi.org/10.1080/00207540600677781.

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5

Zhou, Chang'an, Bin Yang, Kai Guo, Jiangwei Liu, Jie Sun, Ge Song, Shaowei Zhu, Chao Sun, and Zhenxi Jiang. "Vibration singularity analysis for milling tool condition monitoring." International Journal of Mechanical Sciences 166 (January 2020): 105254. http://dx.doi.org/10.1016/j.ijmecsci.2019.105254.

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6

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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7

Hui, K. H., L. M. Hee, M. Salman Leong, and Ahmed M. Abdelrhman. "Vibration Condition Monitoring: Latest Trend and Review." Applied Mechanics and Materials 773-774 (July 2015): 139–43. http://dx.doi.org/10.4028/www.scientific.net/amm.773-774.139.

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Vibration analysis has proven to be the most effective method for machine condition monitoring to date. Various effective signal analysis methods to analyze and extract fault signature that embedded in the raw vibration signals have been introduced in the past few decades such as fast Fourier transform (FFT), short time Fourier transform (STFT), wavelets analysis, empirical mode decomposition (EMD), Hilbert-Huang transform (HHT), etc. however, these is still a need for human to interpret vibration signature of faults and it is regarded as one of the major challenge in vibration condition monitoring. Thus, most recent researches in vibration condition monitoring revolved around using Artificial Intelligence (AI) techniques to automate machinery faults detection and diagnosis. The most recent literatures in this area show that researches are mainly focus on using machine learning techniques for data fusion, features fusion, and also decisions fusion in order to achieve a higher accuracy of decision making in vibration condition monitoring. This paper provides a review on the most recent development in vibration signal analysis methods as well as the AI techniques used for automated decision making in vibration condition monitoring in the past two years.

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8

Muhlisin, Iip, and Rusman Rusyadi. "Vibration Analysis on Rotating Machines using Fast Fourier Transform (FFT)." ACMIT Proceedings 3, no. 1 (March 18, 2019): 67–75. http://dx.doi.org/10.33555/acmit.v3i1.28.

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The history has record that heavy industries face major problems that causes by variant types of mechanical failures came from rotating machines. The Vibrations in rotating machine almost fond in everywhere, due to unbalances, misalignments and imperfect part, analytical approaches has shown that vibration monitoring has great capability in detecting and addressing the defect particular part in the machine line .The vibration velocities and vibration load will be measured at different speeds using The Time-frequency analysis at initial condition. The result of vibration readings spectrum analysis and phase analysis can be determining the figure of vibrations character, and the causes of height vibration will be found. By reading the spectrum unbalance will be identified. When the unbalanced part was balanced then we found that the vibration was decrease. The Vibration experimental frequency spectrum test will be conduct for both balanced and unbalanced condition and also in different speed conditions. To full fill the vibration analysis test, in this experimental research a prototype of vibration monitoring system was constructed. The vibration can be generated and the system performance can be monitored. In this prototype the signal from load cell and velocity sensor will be processed in microcontroller and send to computer where FFT will processed the signal to create spectrum in the computer display. The actual final result of Vibration analysis test will be provide after finish the vibrations analysis test that will be done latter, therefore the chart result on this paper is based on theoretical only.

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9

OBA, Takuya, Koichi YAMADA, Nobuyuki OKADA, Hitoshi SOMA, and Katsuya TANIFUJI. "Condition Monitoring for Shinkansen Bogies Based on Vibration Analysis." Transactions of the Japan Society of Mechanical Engineers Series C 75, no. 757 (2009): 2459–67. http://dx.doi.org/10.1299/kikaic.75.2459.

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10

OBA, Takuya, Koichi YAMADA, Nobuyuki OKADA, and Katsuya TANIFUJI. "Condition Monitoring for Shinkansen Bogies Based on Vibration Analysis." Journal of Mechanical Systems for Transportation and Logistics 2, no. 2 (2009): 133–44. http://dx.doi.org/10.1299/jmtl.2.133.

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11

Song, Limin. "System and methodology for vibration analysis and condition monitoring." Journal of the Acoustical Society of America 121, no. 5 (2007): 2486. http://dx.doi.org/10.1121/1.2739161.

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12

Biswas, R. K., M. C. Majumdar, and S. K. Basu. "Vibration and Oil Analysis by Ferrography for Condition Monitoring." Journal of The Institution of Engineers (India): Series C 94, no. 3 (July 2013): 267–74. http://dx.doi.org/10.1007/s40032-013-0079-8.

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13

El-Wardany, T. I., D. Gao, and M. A. Elbestawi. "Tool condition monitoring in drilling using vibration signature analysis." International Journal of Machine Tools and Manufacture 36, no. 6 (June 1996): 687–711. http://dx.doi.org/10.1016/0890-6955(95)00058-5.

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14

Goto, Satoru, Yoshinori Takahashi, and Mikio Tohyama. "Structural Vibration Monitoring Using Cumulative Spectral Analysis." Advances in Acoustics and Vibration 2013 (March 5, 2013): 1–7. http://dx.doi.org/10.1155/2013/158329.

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This paper describes a resonance decay estimation for structural health monitoring in the presence of nonstationary vibrations. In structural health monitoring, the structure's frequency response and resonant decay characteristics are very important for understanding how the structure changes. Cumulative spectral analysis (CSA) estimates the frequency decay by using the impulse response. However, measuring the impulse response of buildings is impractical due to the need to shake the building itself. In a previous study, we reported on system damping monitoring using cumulative harmonic analysis (CHA), which is based on CSA. The current study describes scale model experiments on estimating the hidden resonance decay under non-stationary noise conditions by using CSA for structural condition monitoring.

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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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Takahashi, Yoshinori, Toru Taniguchi, and Mikio Tohyama. "Structural Condition Monitoring by Cumulative Harmonic Analysis of Random Vibration." Advances in Acoustics and Vibration 2008 (August 3, 2008): 1–8. http://dx.doi.org/10.1155/2008/261758.

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Analysis of signals based on spectral accumulation has great potential for enabling the condition of structures excited by natural forces to be monitored using random vibration records. This article describes cumulative harmonic analysis (CHA) that was achieved by introducing a spectral accumulation function into Berman and Fincham's conventional cumulative analysis, thus enabling potential new areas in cumulative analysis to be explored. CHA effectively enables system damping and modal overlap conditions to be visualized without the need for transient-vibration records. The damping and modal overlap conditions lead to a spectral distribution around dominant spectral peaks due to structural resonance. This distribution can be revealed and emphasized by CHA records of magnitude observed even within short intervals in stationary random vibration samples.

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17

Mohd Ghazali, Mohamad Hazwan, and Wan Rahiman. "Vibration Analysis for Machine Monitoring and Diagnosis: A Systematic Review." Shock and Vibration 2021 (September 10, 2021): 1–25. http://dx.doi.org/10.1155/2021/9469318.

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Untimely machinery breakdown will incur significant losses, especially to the manufacturing company as it affects the production rates. During operation, machines generate vibrations and there are unwanted vibrations that will disrupt the machine system, which results in faults such as imbalance, wear, and misalignment. Thus, vibration analysis has become an effective method to monitor the health and performance of the machine. The vibration signatures of the machines contain important information regarding the machine condition such as the source of failure and its severity. Operators are also provided with an early warning for scheduled maintenance. Numerous approaches for analyzing the vibration data of machinery have been proposed over the years, and each approach has its characteristics, advantages, and disadvantages. This manuscript presents a systematic review of up-to-date vibration analysis for machine monitoring and diagnosis. It involves data acquisition (instrument applied such as analyzer and sensors), feature extraction, and fault recognition techniques using artificial intelligence (AI). Several research questions (RQs) are aimed to be answered in this manuscript. A combination of time domain statistical features and deep learning approaches is expected to be widely applied in the future, where fault features can be automatically extracted from the raw vibration signals. The presence of various sensors and communication devices in the emerging smart machines will present a new and huge challenge in vibration monitoring and diagnosing.

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18

Harrison, Gregory A. "Neural network based analysis system for vibration analysis and condition monitoring." Journal of the Acoustical Society of America 111, no. 5 (2002): 1969. http://dx.doi.org/10.1121/1.1486369.

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19

Orman, Maciej, and Cajetan Pinto. "Vibration, Sound and Thermal Analysis as a Condition Monitoring Methods for Electric Motors." Applied Mechanics and Materials 239-240 (December 2012): 462–67. http://dx.doi.org/10.4028/www.scientific.net/amm.239-240.462.

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This paper presents a comparison of different measurement techniques for condition monitoring of electric motors. Results are presented for vibration, acoustic and thermal analysis. Vibration signals were measured by piezoelectric accelerometers, acoustic by microphones and temperature data was collected by an infrared camera. Two induction motor cases were examined – healthy motor case and combination of static eccentricity with soft foot case. Vibration monitoring is a well known technique used in condition monitoring and in this work vibration measurements were used as a reference signal for assessment of the value of acoustic and thermal measurements. As presented in result section both acoustic analyses as well as thermal images appear as valuable techniques for condition monitoring of electric motors. All the measurements where conducted in an industrial environment.

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20

Takahashi, Yoshinori, Satoru Goto, and Mikio Tohyama. "Modal Experiment on Structural Condition Monitoring Using Cumulative Harmonic Analysis." Advances in Acoustics and Vibration 2012 (February 27, 2012): 1–6. http://dx.doi.org/10.1155/2012/327135.

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This paper describes a cumulative harmonic analysis (CHA) that replaces the stepwise time window function of Berman and Fincham's cumulative spectral analysis with a spectral accumulation function, thereby enabling a new structural health monitoring method. CHA estimates and visualizes system damping conditions without the need of transient-vibration records. The damping conditions are closely related to the spectral distribution around the dominant spectral peaks due to structural resonance. This type of spectral distribution can be visualized with CHA even within a short interval of random vibration samples. The effect of CHA on monitoring the damping conditions was reported by the authors in a previous study. In the current study, the authors confirmed the usefulness of CHA for structural condition monitoring by conducting scale-model experiments.

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21

Zhang, Yong Sheng, Yu Jing Jia, and Guang Zhen Cheng. "State Analysis of Mine Hoist." Applied Mechanics and Materials 201-202 (October 2012): 139–42. http://dx.doi.org/10.4028/www.scientific.net/amm.201-202.139.

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The objective is to provide a method about equipment condition monitoring and fault diagnosis. The method could indicate equipment operating parameters, working conditions and specific status. We used CDMS system monitoring mine hoist key parts of the vibration velocity, introduces the vibration sensor reasonable decorate, in the time domain analysis of speed amplitude values, in frequency domain speed spectrum analysis, according to the monitoring signal analysis processing results, give the diagnostic conclusions that the gear of second shaft have uniform force, and a little corrosion, peel and eccentric, puts forward that we should strengthen vibration monitoring about the second shaft of speed reducer, Observe reducer wear trend using the iron spectral analysis, Supervise lubricating oil quality, change oil when necessary.

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22

Gomathi, K., A. Senthil Kumar, and M. Raghunath. "Design and Analysis of Micro Accelerometer for Tool Condition Monitoring." Applied Mechanics and Materials 787 (August 2015): 932–36. http://dx.doi.org/10.4028/www.scientific.net/amm.787.932.

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Tool condition monitoring plays a huge role in the CNC machines in which it is used to avoid the breakage of the tools.In this paper area changed capacitive micro accelerometer is designed to measure the vibration exposure of the tool on various applications in the CNC machines. The design process and simulation of the Micro accelerometer are done using INTELLISUITE 8.6. The rectangular folded beamis designed to reduce the residual stress and to obtain a better sensitivity. Static and dynamic analysis shows that sensitivity of the designed capacitive accelerometer is good enough to detect the vibration of the tool.

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23

Bartelmus, W. "Root cause and vibration signal analysis for gearbox condition monitoring." Insight - Non-Destructive Testing and Condition Monitoring 50, no. 4 (April 2008): 195–201. http://dx.doi.org/10.1784/insi.2008.50.4.195.

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24

Gierlak, Piotr, Andrzej Burghardt, Dariusz Szybicki, Marcin Szuster, and Magdalena Muszyńska. "On-line manipulator tool condition monitoring based on vibration analysis." Mechanical Systems and Signal Processing 89 (May 2017): 14–26. http://dx.doi.org/10.1016/j.ymssp.2016.08.002.

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25

Vishwakarma, Manish, Rajesh Purohit, V. Harshlata, and P. Rajput. "Vibration Analysis & Condition Monitoring for Rotating Machines: A Review." Materials Today: Proceedings 4, no. 2 (2017): 2659–64. http://dx.doi.org/10.1016/j.matpr.2017.02.140.

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26

Petersen, DR, RE Link, ACH Tan, PSK Chua, and GH Lim. "Condition Monitoring of a Water Hydraulic Cylinder by Vibration Analysis." Journal of Testing and Evaluation 28, no. 6 (2000): 507. http://dx.doi.org/10.1520/jte12142j.

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27

Meng, Dejian, Feng Xiao, Lijun Zhang, Xiaotian Xu, Gang S. Chen, Wael Zatar, and J. Leroy Hulsey. "Nonlinear vibration analysis of vehicle–bridge interaction for condition monitoring." Journal of Low Frequency Noise, Vibration and Active Control 38, no. 3-4 (December 1, 2018): 1422–32. http://dx.doi.org/10.1177/1461348418811703.

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KiranKumar, M. V., M. Lokesha, Sujesh Kumar, and Ajith Kumar. "Review on Condition Monitoring of Bearings using vibration analysis techniques." IOP Conference Series: Materials Science and Engineering 376 (June 2018): 012110. http://dx.doi.org/10.1088/1757-899x/376/1/012110.

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29

Ebersbach, Stephan, and Zhongxiao Peng. "Expert system development for vibration analysis in machine condition monitoring." Expert Systems with Applications 34, no. 1 (January 2008): 291–99. http://dx.doi.org/10.1016/j.eswa.2006.09.029.

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30

Szczepanik, Andre, Ian Roy, and B. T. Kuhnell. "Vibration and Stress Analysis for Condition Monitoring of Symon Cone Crushers." Journal of Vibration and Acoustics 112, no. 2 (April 1, 1990): 268–73. http://dx.doi.org/10.1115/1.2930122.

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Condition-Monitoring of the crusher frames, using stress and vibration analysis, is described. It is shown that the meshing clearances between the countershaft pinion and the eccentric ring gear are critical to safe operation. It also is shown that shock stresses occur for very small close-side settings and these stresses are caused by lateral vibration of the relief springs.

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31

KUMAR MADISETTY, KIRAN, DIWAKAR GARIKAPATI, and SHAKTI PRASANNA JENA. "Detection of Faults and Vibration Analysis in Beam Structures - A Review." INCAS BULLETIN 13, no. 2 (June 4, 2021): 67–82. http://dx.doi.org/10.13111/2066-8201.2021.13.2.8.

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This review focuses on vibrating beam structures with different constraints. The review is also aimed to analyse the different fault detection approaches for condition monitoring of cracks in vibrating structures. The advancement in this field with significant and diversified techniques namely analytical, numerical and experimental approaches are elaborated and presented here. The significance of crack location and the severe effects on the responses of the vibrating structure are discussed. The application of piezoelectric material on structures, as well as different fault detection and soft computing techniques are summarized to monitor the conditions of a structure. A concise review study has also been conducted for better improvement in the service period. The integrity of the structure can be improved with the aid of vibration control and Structural Health Monitoring (SHM) approaches.

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Luo, G. Y., D. Osypiw, and M. Irle. "On-Line Vibration Analysis with Fast Continuous Wavelet Algorithm for Condition Monitoring of Bearing." Journal of Vibration and Control 9, no. 8 (August 2003): 931–47. http://dx.doi.org/10.1177/10775463030098002.

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The newly developed technique of wavelet transform enables us to observe the evolution in time of the frequency content of a signal. This property makes it very suitable for the detection of vibration transients. However, current algorithms for vibration analysis either have low resolution of features for detailed analysis in the frequency-band scale or are very time consuming. In this paper, we present fast wavelet-based algorithms for vibration analyses. The approximated Morlet wavelet is used to develop an infinite impulse response causal filter with the error kept at an acceptable level. Using this filter, the continuous wavelet transform (CWT) can be computed. The rapid computation of the CWT, together with autocorrelation enhancement, is developed for the detailed on-line vibration analysis. Thus, the raw vibration signal can be continuously processed and monitored, with warning or alarm signals being generated when pre-programmed levels are exceeded. Examples are used to illustrate the application of arbitrary fine frequency-band scale analysis and single specified central frequency in the time domain using the wavelet power spectrum, which enables small defect detection of bearings. Real-time condition monitoring, fault detection, tool wear monitoring, etc., which are related to vibration, can be achieved and implemented with the detailed analysis of vibration signals.

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Rameshkumar, G. R., B. V. A. Rao, and K. P. Ramachandran. "Condition Monitoring of Forward Curved Centrifugal Blower Using Coast Down Time Analysis." International Journal of Rotating Machinery 2010 (2010): 1–12. http://dx.doi.org/10.1155/2010/962804.

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Mechanical malfunctions such as, rotor unbalance and shaft misalignment are the most common causes of vibration in rotating machineries. Vibration is the most widely used parameter to monitor and asses the machine health condition. In this work, the Coast Down Time (CDT), which is an indicator of faults, is used to assess the condition of the rotating machine as a condition monitoring parameter. CDT is the total time taken by the system to dissipate the momentum acquired during sustained operation. Extensive experiments were conducted on Forward Curved Centrifugal Blower Test Rig at selected cutoff speeds for several combinations of combined horizontal and vertical parallel misalignment, combined parallel and angular misalignment, as well as for various unbalance conditions. As mechanical faults increase, a drastic decrease in CDT is found and this is represented as CDT reduction percentage. A specific correlation between the CDT reduction percentage, level of mechanical faults, and rotational cutoff speeds is observed. The results are analyzed and compared with vibration analysis for potential use of CDT as one of the condition monitoring parameter.

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Howard, I. M. "Higher-order spectral techniques for machine vibration condition monitoring." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 211, no. 4 (April 1, 1997): 211–19. http://dx.doi.org/10.1243/0954410971532622.

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The vibration signals measured from rotating machinery can be very complex and a number of machine malfunctions can create complicated modulation patterns which are sometimes difficult to detect and to understand. Conventional linear spectral analysis will be of limited use in particular instances when frequency components interact together to form new spectral components due to some non-linear process. Under these circumstances, various signal processing tools are available for performing sophisticated analysis of the measured vibration to detect the non-linear interaction of frequency components and hence changes in machine performance and condition. This paper presents two higher-order spectral analysis techniques, the bispectrum and the trispectrum, and demonstrates how they can be used to detect phase coherence between various frequency components. The theoretical relationship of the higher-order spectral techniques to the power spectrum is given along with the derivation of the normalized bispectrum and trispectrum using the Fourier series of phase- related signals to show that the higher-order spectral analysis techniques can detect various forms of phase coupling between frequency components as well as the strength of the phase coupling. The particular case of modulation is investigated to show the applicability of the higher-order spectral techniques to detect amplitude and phase modulation.

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Li, Wen Xing, and Meng Jia. "Vibration Monitoring System Design." Advanced Materials Research 1027 (October 2014): 325–30. http://dx.doi.org/10.4028/www.scientific.net/amr.1027.325.

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Along with the computer software technology, the virtual instrument is gradually become the development direction of the test area. The realizing vibration test which is analyzed by adopting the virtual instrument has become the development trend of vibration test. It firstly introduces relevant simulation technology theory, the principle of fault diagnosis, the significance and the development present situation. Then according to the research for the development current situation of condition monitoring and fault diagnosis at home and abroad, it puts forward the vibration test analysis system composed the necessary sensors, signal disposal device and data acquisition card. At last it uses labwindows software produced in NI to do the development platform.

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Hao, Wangs Shen, Xun Sheng Zhu, Biao Jun Tian, and M. R. Chi. "Autoregressive Spectrum Analysis of Vibration and Condition Monitoring of Self-Propelled Rotary Tool." Key Engineering Materials 329 (January 2007): 743–48. http://dx.doi.org/10.4028/www.scientific.net/kem.329.743.

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Self-propelled rotary tools are being rediscovered for their applications in machining of ‘difficult-to-machine’ materials or for general improvement in the productivity of machining operations. The vibration characteristic and the occurrence of chatter during high speed cutting will induce the deterioration of precision and machining surface, tool wear and tool life. This paper deals with the identification of the vibration in SPRT cutting system with AR time series model. The experiment and deduction method provide a sound foundation for improving the structure with high antivibration strength, which could reduce the relative vibration between tool and workpiece in the alloweable scope.

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37

Prasad, Balla Srinivasa, and Y. Ramamohan Reddy. "Analysis of real-time vibration assisted tool condition monitoring in drilling." International Journal of Manufacturing Research 14, no. 2 (2019): 101. http://dx.doi.org/10.1504/ijmr.2019.099977.

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Reddy, Y. Rama Mohan, and B. Srinivasa Prasad. "ANALYSIS OF REAL TIME VIBRATION ASSISTED TOOL CONDITION MONITORING IN DRILLING." International Journal of Manufacturing Research 14, no. 2 (2019): 1. http://dx.doi.org/10.1504/ijmr.2019.10018362.

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39

Abd-Elaal, M., M. Lotfy, T. Nabil, and A. Eldomiaty. "Condition-based monitoring of a small centrifugal pump by vibration analysis." IOP Conference Series: Materials Science and Engineering 610 (October 11, 2019): 012049. http://dx.doi.org/10.1088/1757-899x/610/1/012049.

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40

Gomathi, K., and A. Balaji. "Tool condition monitoring of PCB milling machine based on vibration analysis." Materials Today: Proceedings 45 (2021): 3386–97. http://dx.doi.org/10.1016/j.matpr.2020.12.778.

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41

EROL, S. Seçkin, and Cemal MERAN. "Inspection of artificially built mechanical failures through innovative condition monitoring techniques." ITM Web of Conferences 22 (2018): 01025. http://dx.doi.org/10.1051/itmconf/20182201025.

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In this study, the factors influence on root causes of failure initiation are examined with the original test implementation and condition monitoring techniques are emphasized. In working toward this goal, the laboratory test setups and tests which have been created by international research bodies are examined and in order to practise a new research work, a unique test setup system and a test plan is built. In this target of the root cause failure detection, vibration data at radial direction and electrical consumption data are collected through the analysis by comparing two different condition monitoring techniques. In this research, fault detection in modeling fault conditions and vibration, electrical consumption measurement have been let us examination in depth. During the tests, data are collected simultaneously in vibration by four-channel Data Acquisition Card (DAC) and electrical consumption by Motor Condition Monitoring (MCM) system which are integrated with an computer system. Respect to the study results; vibration analysis in detection of defects has been judged to be more successful in comparison with electric consumption analysis under the test conditions in perspective of condition based predictive maintenanceIn the study, it is mentioned that detection of failure initiations at sensitive levels and importance of consequent results.

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Nakandhrakumar, Ramasamay S., D. Dinakaran, S. Satishkumar, and M. Gopal. "Influence of Sensor Positioning in Tool Condition Monitoring of Drilling Process through Vibration Analysis." Advanced Materials Research 984-985 (July 2014): 564–69. http://dx.doi.org/10.4028/www.scientific.net/amr.984-985.564.

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In this study, the relationship between vibration and tool wear and also influence of sensor positioning in tool codition monitoring were investigated during drilling. For this purpose, a series of experiment were conducted in a CNC vertical milling machine using drilling cycle. A 6 mm diameter HSS drill and EN24 as workpiece material were used in these experiments. The vibration was measured in the transverse direction of sensor which is positioned on the workpiece with constant distance from the holes to be drilled for monitoring tool wear as in previous studies. But, positioning of sensor in a constant place with equal distance from all holes to be drilled is not possible for all the workpiece profiles in actual practice. Experiments show that the distance of sensor from the holes in drilling affects the vibration signals for the same state of wear.It shows that the tool wear models presented in previous studies using acceleration signals are sensor location dependent. This work presents a Variance-amplitude of the vibration signals received for tool condition monitoring which is the most sensitive statistical parameter than other statistical parameters such as Root Mean Square (RMS), Exponential, Peak, max-min, mean and standard deviation. Results showed that there was no considerable increase in the vibration amplitude of variance until flank wear value of 0.30 mm was reached, above which the vibration amplitude increased significantly.

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Shnibha, Rmdan, and Alhussein Albarbar. "Petroleum Pumps’ Current and Vibration Signatures Analysis Using Wavelet Coherence Technique." Advances in Acoustics and Vibration 2013 (June 12, 2013): 1–6. http://dx.doi.org/10.1155/2013/659650.

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Vibration analysis is widely used for rotating machinery diagnostics; however measuring vibration of operational oil well pumps is not possible. The pump’s driver’s current signatures may provide condition-related information without the need for an access to the pump itself. This paper investigates the degree of relationship between the pump’s driver’s current signatures and its induced vibration. This relationship between the driver’s current signatures (DCS) and its vibration signatures (DVS) is studied by calculating magnitude-squared coherence and phase coherence parameters at a certain frequency band using continuous wavelet transform (CWT). The CWT coherence-based technique allows better analysis of temporal evolution of the frequency content of dynamic signals and areas in the time-frequency plane where the two signals exhibit common power or consistent phase behaviour indicating a relationship between the signals. This novel approach is validated by experimental data acquired from 3 kW petroleum pump’s driver. Both vibration and current signatures were acquired under different speed and load conditions. The outcomes of this research suggest the use of DCS analysis as reliable and inexpensive condition monitoring tool, which could be implemented for oil pumps, real-time monitoring associated with condition-based maintenance (CBM) program.

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Ben-Romdhane, Mohamed. "Condition based monitoring by vibrational analysis." Journal of the Acoustical Society of America 112, no. 1 (2002): 17. http://dx.doi.org/10.1121/1.1500897.

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Ziaja, Aleksandra, Tomasz Barszcz, and Wieslaw Jerzy Staszewski. "Wavelet-Based Variance Analysis for Condition Monitoring of Gears." Key Engineering Materials 588 (October 2013): 343–51. http://dx.doi.org/10.4028/www.scientific.net/kem.588.343.

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Gears faults are one of the most common issues in rotating machinery. If not detected on time, they may result in severe dysfunction of the kinematic chain or even in catastrophic breakdown of the whole mechanism. Vibration based methods dedicated to gears monitoring are widely studied and applied techniques, that in majority rely on comparison of some spectral features. In this paper, however, the method based on fractal theory and wavelet decomposition is studied for possible application to gears diagnostics. The discussed technique may serve as a tool for signal contraction and/or signals comparison according to frequency content. Presented results are based on experimental data recorded from gradually deteriorated gearbox and gear with mechanically introduced local fault.

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Shan, Guang Kun, Hai Long Zhang, Xiao Dong Wang, and Ying Ming Liu. "Application of FastICA Algorithm in Wind Turbine Condition Monitoring." Applied Mechanics and Materials 217-219 (November 2012): 2750–53. http://dx.doi.org/10.4028/www.scientific.net/amm.217-219.2750.

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In wind turbine condition monitoring, the sensors often can not be installed to the ideal position. Compare the common signal processing method comprehensively and give the advantage of the fastICA algorithm in the wind turbine condition monitoring. Give the basic principle and mathematical model of the fastICA algorithm, while monitor and analysis the wind turbine state data based on the fastICA algorithm. The results show that this algorithm can separate the vibration characteristics of the tested compenent of the wind turbine from the vibration signals quickly and accurately.

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Kowalska-Koczwara, Alicja, Filip Pachla, Piotr Stecz, Krzysztof Stypuła, Tadeusz Tatara, Jerzy Lejk, and Marek Sokołowski. "Vibration-Based Damage Identification and Condition Monitoring of Metro Trains: Warsaw Metro Case Study." Shock and Vibration 2018 (2018): 1–14. http://dx.doi.org/10.1155/2018/8475684.

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The work concerns the monitoring of the technical condition of metro trains. For this purpose, a vibration monitoring system installed in buildings in the vicinity of the underground is used. Based on measured vibrations buildings, the identification of technical condition in metro trains is conducted. The results of measurements from the system are also used to assess the impact of vibrations on people staying in buildings through the WODL ratio. Exceeding the assumed levels of the WODL ratio is the basis for repairing trains by turning wheels. Statistical analysis of the data was implemented and trends for particular time periods of measurements were shown. With the entered monitoring system and the introduced WODL index thresholds, it is possible to better plan the costs associated with the repair and replacement of wheels of rolling stock, where there are large values of radial runout. According to the introduced monitoring procedure, the number of complaints from residents decreased and the comfort of their lives improved.

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Parhi, D. R., and A. K. Dash. "Application of neural network and finite element for condition monitoring of structures." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 225, no. 6 (May 25, 2011): 1329–39. http://dx.doi.org/10.1177/0954406210395883.

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This article analyses the dynamic behaviour of a beam structure containing multiple transverse cracks using neural network controller. The first three natural frequencies and mode shapes have been calculated using theoretical, finite-element, and experimental analysis for the cracked and un-cracked beam. Comparisons of the results among theoretical, finite-element, and experimental analysis have also been presented. The calculated vibration signatures were used to train the feed-forward multi-layered neural network controller with back-propagation technique for the prediction of cracks. Relative crack locations and relative crack depths are the output of the neural controller. Results obtained from the various analyses are validated using the developed experimental set-up. Results from neural controller have been presented for comparison with the output from theoretical, finite-element, and experimental analysis. From the evaluation of the performance of the neural network controller it is observed that the developed method can be used as a crack diagnostic tool in the domain of dynamically vibrating structures.

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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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Prasad, Balla Srinivasa, Javvadi Umamaheswara Rao, and A. Gopala Krishna. "Analysis of vibration signals to quantify displacement amplitude in the monitoring of vibration-assisted turning." Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering 233, no. 1 (November 9, 2017): 35–47. http://dx.doi.org/10.1177/0954408917742196.

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Vibration-assisted machining combines precision machining with small-amplitude tool vibration at high frequency to improve the fabrication process. It has been applied to a number of processes from turning to drilling to grinding. This work presents the validation of tool condition monitoring system based on vibration parameters. For this purpose, an experimental investigation is planned to acquire vibration signal data during the machining. This work primarily focuses on quantifying the presence of relative vibrations between the cutting tool and a workpiece during vibration-assisted turning process which helps in predicting tool life. For this purpose, an online acoustic optic emission-based vibration transducer, i.e. Laser Doppler Vibrometer, is used as a component of a novel approach. Cutting force and vibration signals were recorded and analyzed. Machine dynamic effects such as cutting force and tool wear are taken into account during the dry machining of Ti-6Al-4V alloys specimens. Identifying the correlation among tool wear, cutting forces and displacement due to vibration is a critical task in the present study. Real-time experimental findings are used to predict the evolution of displacement and tool wear in the experiment. Efficacy of a logical relationship among the process variables such as displacement, feed rate, spindle rotational speed, and depth of cut is critically examined. Results of the present study are used to establish a strategy for real-time efficient tool monitoring systems for vibration-assisted turning operation. The wear mechanisms of DNMA 432 coated carbide and uncoated carbide insert tools were examined at different combinations of feed rate, spindle speed, and depth of cut for turning of Ti-6Al-4V workpiece material.

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