Relevant bibliographies by topics / Condition monitoring and vibration analysis

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Condition monitoring and vibration analysis'

Author: Grafiati
Published: 4 June 2021
Last updated: 15 February 2022

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

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Condition monitoring and vibration analysis"

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Al-Arbi, Salem. "Condition monitoring of gear systems using vibration analysis." Thesis, University of Huddersfield, 2012. http://eprints.hud.ac.uk/id/eprint/17821/.

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It is often impractical to measure vibrations directly at /or close to their sources when condition monitoring gearbox systems. It is common to measure the vibration distant from the source due to limited access to the component which is to be monitored. In addition, operating the gearbox under different loads and speeds also produces vibration signals within different components. Vibration measured in this way may be significantly distorted by the effect of signal transmission paths and interference from other sources. Therefore, suppression of distortions is a key issue for remote measurement based condition monitoring. In this research work, the influences of transducer locations and operating conditions on the vibration signal have been investigated on a typical gearbox transmission system for the detection of faults induced within the gearbox. Vibration signals corresponding to a healthy (baseline) and faulty conditions on two-stage helical gearbox at various load and speed levels were recorded. The baseline vibration data were examined using conventional methods in the time, frequency and the joint time-frequency domains, and are referenced for comparison with more advanced methods. Several parameters have been proposed for monitoring gear condition locally (gearbox casing) including time, frequency, and joint time-frequency domain representation. The results show that traditional signal processing techniques were insufficient for revealing fault detection information due to the low signal to noise ratio (SNR). This research also presents a mathematical model for the simulation of vibration signals in order to further understand the source of the vibration. The model represents a two stage gear system using a suitable stiffness function to represent the forces acting between each pair of gears. Rotational stiffness and damping are also used to simulate the angular motion of the gears and shafts. Results show that the frequency spectrum of acceleration outputs from the model take the expected form with peaks at the meshing frequency and associated harmonics. Furthermore, if the stiffness function between the first pair of gears is simulated with a broken tooth, and various degrees of damage, outputs from the simulation have similar sideband effects to the signals produced in the experimental investigation. In addition, the model also demonstrates that variation of load and speed produces a corresponding effect to that seen in the experiments. Consequently, although relatively simple, the mathematical model can be used to explain vibration mechanisms in real gearbox systems used in condition monitoring. Time synchronous averaging (TSA) has been applied to the vibration signals from the gearbox to remove random noise combined with the raw signal. The angular domain signal, the order spectrum and the order-frequency presentation were used to characterise gearbox vibration in these new domains in more detail. Results obtained following TSA were compared with those obtained through conventional analysis from waveform characteristics, spectrum patterns and corresponding feature parameters under different operating loads and fault conditions. In addition, continuous wavelet transform (CWT) of TSA was also compared with the conventional CWT results of raw signals to further characterise vibrations. As part of this research study, the vibration transmission path has been estimated using the frequency response function (FRF) technique. A response based estimation method has been developed to revise the base path and adapted to operating conditions for more accurate fault estimation. Both theoretical analysis and test results showed that improved diagnosis when the path information was included in vibration signal processing and feature selection. Finally, the vibration data recorded from the two accelerometers located on the gearbox casing and motor flange were analyzed using different signal processing methods to investigate the effect of path transmission (transducer location) on the detection and diagnosis of the seeded gear tooth faults. Results from the angular domain, the order spectrum and the order-frequency analysis are presented to demonstrate use of these techniques for fault detection in gearboxes and that the effect of path transmissions can be observed on the vibration signals. Results showed that CWT of the TSA signal could be used to detect and indicate the severity of the gear damage effectively even if vibration signals originated from a remote motor flange.
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Moussa, Wael. "Thermography-Assisted Bearing Condition Monitoring." Thesis, Université d'Ottawa / University of Ottawa, 2014. http://hdl.handle.net/10393/31379.

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Abstract Despite the large amount of research work in condition based maintenance and condition monitoring methods, there is still a need for more reliable and accurate methods. The clear evidence of that need is the continued dependence on time based maintenance, especially for critical applications such as turbomachinery and airplane engines. The lack of accurate condition monitoring systems could lead to not only the unexpected failures as well as the resulting hazards and repair costs, but also a huge waste of material and time because of unnecessary replacement due to false alarms and unnecessary repair and maintenance. Temperature change is a phenomenon that accompanies every dynamic activity in the universe. However, it has not been adequately exploited for mechanical system condition monitoring. The reason is the slow response of current temperature monitoring systems compared to other condition monitoring methods such as vibration analysis. Many references inferred that the change in temperature is not sensible until approaching the end of the monitored component life and even the whole system life (Kurfess, et al., 2006; Randall, 2011; Patrick, et al., March 7-14, 2009). On the other hand, the most commonly used condition monitoring method, i.e., vibration analysis, is not free from pitfalls. Although vibration analysis has shown success in detecting some bearing faults, for other faults like lubrication problems and gradual wear it is much less effective. Also, it does not give a reliable indication of fault severity for many types of bearing faults. The advancement of thermography as a temperature monitoring tool encourages the reconsideration of temperature monitoring for mechanical system fault detection. In addition to the improved accuracy and responsiveness, it has the advantage of non-contact monitoring which eliminates the need for complex sensor mounting and wiring especially for rotating components. Therefore, in current studies the thermography-based monitoring method is often used either as a distinct method or as a complementary tool to vibration analysis in an integrated condition monitoring system. The main objectives of this study are hence to: 1. Define heat sources in the rolling element bearings and overview two of the most famous bearing temperature calculation methods. 2. Setup a bearing test rig that is equipped with both vibration and temperature monitoring systems. 3. Develop a temperature calculation analytical model for rolling element bearing that include both friction calculation and heat transfer models. The friction calculated by the model will be compared to that calculated using the pre-defined empirical methods. The heat transfer model is used for bearing temperature calculation that will be compared to the experimental measurement using different temperature monitoring devices. 4. Propose a new in-band signal enhancement technique, based on the synchronous averaging technique, Autonomous Time Synchronous Averaging (ATSA) that does not need an angular position measuring device. The proposed method, in addition to the Spectral Kurtosis based band selection, will be used to enhance the bearing envelope analysis. 5. Propose a new method for classification of the bearing faults based on the fault severity and the strength of impulsiveness in vibration signals. It will be used for planning different types of tests using both temperature and vibration methods. 6. Develop and experimentally test a new technique to stimulate the bearing temperature transient condition. The technique is supported by the results of finite element modeling and is used for bearing temperature condition monitoring when the bearing is already running at thermal equilibrium condition.
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Esu, Ozak O. "Vibration-based condition monitoring of wind turbine blades." Thesis, Loughborough University, 2016. https://dspace.lboro.ac.uk/2134/21679.

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Significant advances in wind turbine technology have increased the need for maintenance through condition monitoring. Indeed condition monitoring techniques exist and are deployed on wind turbines across Europe and America but are limited in scope. The sensors and monitoring devices used can be very expensive to deploy, further increasing costs within the wind industry. The work outlined in this thesis primarily investigates potential low-cost alternatives in the laboratory environment using vibration-based and modal testing techniques that could be used to monitor the condition of wind turbine blades. The main contributions of this thesis are: (1) the review of vibration-based condition monitoring for changing natural frequency identification; (2) the application of low-cost piezoelectric sounders with proof mass for sensing and measuring vibrations which provide information on structural health; (3) the application of low-cost miniature Micro-Electro-Mechanical Systems (MEMS) accelerometers for detecting and measuring defects in micro wind turbine blades in laboratory experiments; (4) development of an in-service calibration technique for arbitrarily positioned MEMS accelerometers on a medium-sized wind turbine blade. This allowed for easier aligning of coordinate systems and setting the accelerometer calibration values using samples taken over a period of time; (5) laboratory validation of low-cost modal analysis techniques on a medium-sized wind turbine blade; (6) mimicked ice-loading and laboratory measurement of vibration characteristics using MEMS accelerometers on a real wind turbine blade and (7) conceptualisation and systems design of a novel embedded monitoring system that can be installed at manufacture, is self-powered, has signal processing capability and can operate remotely. By applying the conclusions of this work, which demonstrates that low-cost consumer electronics specifically MEMS accelerometers can measure the vibration characteristics of wind turbine blades, the implementation and deployment of these devices can contribute towards reducing the rising costs of condition monitoring within the wind industry.
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Abboud, Dany. "Vibration-based condition monitoring of rotating machines in nonstationary regime." Thesis, Lyon, INSA, 2015. http://www.theses.fr/2015ISAL0106/document.

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Dans les dernières décennies, la surveillance vibratoire des machines tournantes a acquis un intérêt particulier fournissant une aide efficace pour la maintenance dans l'industrie. Aujourd'hui, de nombreuses techniques efficaces sont bien établies, ancrées sur des outils puissants offerts notamment par la théorie des processus cyclostationnaires. Cependant, toutes ces techniques reposent sur l'hypothèse d’un régime de fonctionnement (c.à.d. vitesse et/ou charge) constant ou éventuellement fluctuant d’une façon stationnaire. Malheureusement, la plupart des machines surveillées dans l'industrie opèrent sous des régimes non stationnaires afin de remplir les tâches pour lesquelles elles ont été conçues. Dans ce cas, ces techniques ne parviennent pas à analyser les signaux vibratoires produits. Ce problème a occupé la communauté scientifique dans la dernière décennie et des techniques sophistiquées de traitement du signal ont été conçues pour faire face à la variabilité du régime. Mais ces tentatives restent limitées, dispersées et généralement peu soutenues par un cadre théorique. Le principal objectif de cette thèse est de combler partiellement cette lacune sur la base d'une formalisation théorique du sujet et d’un développement systématique de nouveaux outils de traitement du signal. Dans ce travail, la non-stationnarité du régime est limitée à celle de la vitesse— c.à.d. vitesse variable et charge constante— supposée connue a priori. Afin d'atteindre cet objectif, la méthodologie adoptée consiste à étendre le cadre cyclostationnaire avec ses outils dédiés. Nous avons élaboré cette stratégie en distinguant deux types de signatures. Le premier type comprend des signaux déterministes connus comme cyclostationnaires au premier ordre. La solution proposée consiste à généraliser la classe cyclostationnaire au premier ordre à la classe cyclo-non-stationnaire au premier ordre qui comprend des signaux déterministes en vitesse variable. Le second type comprend des signaux aléatoires périodiquement corrélés connus comme cyclostationnaires au deuxième ordre. Trois visions différentes mais complémentaires ont été proposées pour traiter les variations induites par la non-stationnarité de la vitesse de fonctionnement. La première adopte une approche cyclostationnaire angle\temps, la seconde une solution basée sur l'enveloppe et la troisième une approche cyclo-non-stationnaire (au second ordre). De nombreux outils ont été conçus dont les performances ont été testées avec succès sur des signaux vibratoires réels et simulés
In the last decades, vibration-based condition monitoring of rotating machine has gained special interest providing an efficient aid for maintenance in the industry. Nowadays, many efficient techniques are well-established, rooted on powerful tools offered in particular by the theory of cyclostationary processes. However, all these techniques rely on the assump-tion of constant— or possibly fluctuating but stationary— operating regime (i.e. speed and/or load). Unfortunately, most monitored machines used in the industry operate under nonstationary regimes in order to fulfill the task for which they have been designed. In this case, these techniques fail in analyzing the produced vibration signals. This issue, therefore, has occupied the scientific committee in the last decade and some sophisticated signal processing techniques have been conceived to deal with regime variability. But these works remain limited, dispersed and generally not supported by theoretical frameworks. The principal goal of this thesis is to partially fill in this gap on the basis of a theoretical formalization of the subject and a systematic development of new dedicated signal processing tools. In this work, the nonstationarity of the regime is confined to that of the speed— i.e. variable speed and constant load, assumed to be known a priori. In order to reach this goal, the adopted methodology consists in extending the cyclostationary framework together with its dedicated tools. We have elaborated this strategy by distinguishing two types of signatures. The first type includes deterministic waveforms known as first-order cyclostationary. The proposed solution consists in generalizing the first-order cyclostationary class to the more general first-order cyclo-non-stationary class which enfolds speed-varying deterministic signals. The second type includes random periodically-correlated waveforms known as second-order cyclostationary. Three different but complementary visions have been proposed to deal with the changes induced by the nonstationarity of the operating speed. The first one adopts an angle\time cyclostationary approach, the second one adopts an envelope-based solution and the third one adopts a (second-order) cyclo-non-stationary approach. Many tools have been conceived whose performances have been successfully tested on simulated and real vibration signals
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Mirhadizadeh, S. A. "Monitoring hydrodynamic bearings with acoustic emission and vibration analysis." Thesis, Cranfield University, 2012. http://dspace.lib.cranfield.ac.uk/handle/1826/7888.

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Acoustic emission (AE) is one of many available technologies for condition health monitoring and diagnosis of rotating machines such as bearings. In recent years there have been many developments in the use of Acoustic Emission technology (AET) and its analysis for monitoring the condition of rotating machinery whilst in operation, particularly on high speed machinery. Unlike conventional technologies such as oil analysis, motor current signature analysis (MCSA) and vibration analysis, AET has been introduced due to its increased sensitivity in detecting the earliest stages of loss of mechanical integrity. This research presents an experimental investigation that is aimed at developing a mathematical model and experimentally validating the influence of operational variables such as film thickness, rotational speed, load, power loss, and shear stress for variations of load and speed conditions, on generation of acoustic emission in a hydrodynamic bearing. It is concluded that the power losses of the bearing are directly correlated with acoustic emission levels. With exponential law, an equation is proposed to predict power losses with reasonable accuracy from an AE signal. This experimental investigation conducted a comparative study between AE and Vibration to diagnose the rubbing at high rotational speeds in the hydrodynamic bearing. As it is the first known attempt in rotating machines. It has been concluded, that AE parameters such as amplitude, can perform as a reliable and sensitive tool for the early detection of rubbing between surfaces of a hydrodynamic bearing and high speed shaft. The application of vibration (PeakVue) analysis was introduced and compared with demodulation. The results observed from the demodulation and PeakVue techniques were similar in the rubbing simulation test. In fact, some defects on hydrodynamic bearings would not have been seen in a timely manner without the PeakVue analysis.In addition, the application of advanced signal processing and statistical methods was established to extract useful diagnostic features from the acquired AE signals in both time and frequency domain. It was also concluded that the use of different signal processing methods is often necessary to achieve meaningful diagnostic information from the signals. The outcome would largely contribute to the development of effective intelligent condition monitoring systems which can significantly reduce the cost of plant maintenance. To implement these main objectives, the Sutton test rig was modified to assess the capability of AET and vibration analysis as an effective tool for the detection of incipient defects within high speed machine components (e.g. shafts and hydrodynamic bearings). The first chapter of this thesis is an introduction to this research and briefly explains motivation and the theoretical background supporting this research. The second and third chapters, summarise the relevant literature to establish the current level of knowledge of hydrodynamic bearings and acoustic emission, respectively. Chapter 4 describes methodologies and the experimental arrangements utilized for this investigation. Chapter 5 discusses different NDT diagnosis. Chapter 6 reports on an experimental investigation applied to validate the relationship between AET on operational rotating machines, such as film thickness, speed, load, power loss, and shear stress. Chapter 7 details an investigation which compares the applicability of AE and vibration technologies in monitoring a rubbing simulation on a hydrodynamic bearing.
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Chen, Zhidong. "Machine condition monitoring based on the analysis of transient vibration signals." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0016/NQ58400.pdf.

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Barbini, Leonardo. "Techniques for condition monitoring using cyclo-non-stationary signals." Thesis, University of Bath, 2018. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.761025.

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Condition based maintenance is becoming increasingly popular in many industrial contexts, offering substantial savings and minimising accidental damage. When applied to rotating machinery, its most common tool is vibration analysis, which relies on well-established mathematical models rooted in the theory of cyclo-non-stationary processes. However, the extraction of diagnostic information from the real world vibration signals is a delicate task requiring the application of sophisticated signal processing techniques, tailored for specific machines operating under restricted conditions. Such difficulty in the current state of the art of vibration analysis forces the industry to apply methods with reduced diagnostic capabilities but higher adaptability. However in doing so most of the potential of vibration analysis is lost and advanced techniques become of use only for academic endeavours. The aim of this document is to reduce the gap between industrial and academic applications of condition monitoring, offering ductile and automated tools which still show high detection capabilities. Three main lines of research are presented in this document. Firstly, the implementation of stochastic resonance in an electrical circuit to enhance directly the analog signal from an accelerometer, in order to lower the computational requirements in the next digital signal processing step. Secondly, the extension of already well-established digital signal processing techniques, cepstral prewhitening and spectral kurtosis, to a wider range of operating conditions, proving their effectiveness in the case of non-stationary speeds. Thirdly, the main contribution of the thesis: the introduction of two novel techniques capable of separating the vibrations of a defective component from the overall vibrations of the machine, by means of a threshold in the amplitude spectrum. After the separation, the cyclic content of the vibration signal is extracted and the thresholded signals provide an enhanced detection. The two proposed methods, phase editing and amplitude cyclic frequency decomposition, are both intuitive and of low computational complexity, but show the same capabilities as more sophisticated state of the art techniques. Furthermore, all these tools have been successfully tested on numerically simulated signals as well as on real vibration data from different machinery, lasting from laboratory test rigs to wind turbines drive-trains and aircraft engines. So in conclusion, the proposed techniques are a promising step toward the full exploitation of condition based maintenance in industrial contexts.
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Calabrese, Francesca. "Vibration Monitoring and Intelligent Diagnosis Tools for Condition-Based Maintenance." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017.

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Ogni impianto di produzione è caratterizzato da periodi di operatività, nei quali funziona correttamente, e da periodi di fermo, dovuti alla presenza di guasti o all’esigenza di effettuare attività volte a ristabilire il suo normale comportamento. L’obiettivo principale della funzione manutenzione è minimizzare i periodi di fermo impianto, al fine di renderlo il più disponibile possibile. Attualmente, la manutenzione basata su condizione (CBM) è una delle più politiche più efficaci adottate dalle industrie. Essa è basata sul monitoraggio di diversi parametri della macchina che ne riflettono lo stato di salute. Tra i parametri più utilizzati si trovano i segnali di vibrazione. La CBM può essere implementata attraverso quattro passi principali: raccolta dati, analisi dei segnali, diagnostica e prognostica. Tale procedura prende il nome di Prognostic Health Monitoring (PHM). La necessità di analizzare la grande mole di dati raccolta attraverso il vibration monitoring richiede l’utilizzo di metodi sviluppati nell’ambito della teoria statistica e del data mining, che si pongono l’obiettivo di riconoscere andamenti regolari all’interno di grandi insiemi di dati, al fine di generare conoscenza funzionale al processo decisionale manutentivo. In particolare, i modelli di classificazione, come alberi decisionali, algoritmi K-NN, reti neurali e Support Vector Machine, costituiscono un potente strumento per la diagnostica. Tali modelli, sulla base del PHM, vengono applicati dopo la fase di analisi dei segnali, che consiste principalmente nell’estrazione di features sia nel dominio del tempo che nel dominio tempo-frequenza. Il risultato principale ottenuto consiste nell’aver verificato un incremento delle performance, in termini di accuratezza, della classificazione dello stato di salute di un componente, dovuto all’introduzione dell’analisi nel dominio tempo-frequenza e allo sviluppo dei nuovi metodi “intelligenti”.
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Sihra, Tarsem Singh. "The application of dual channel analysis techniques for on-line vibration monitoring of mining processes." Thesis, University of the West of Scotland, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.386687.

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Nembhard, Adrian. "On-bearing vibration response integration for condition monitoring of rotating machinery." Thesis, University of Manchester, 2015. https://www.research.manchester.ac.uk/portal/en/theses/onbearing-vibration-response-integration-for-condition-monitoring-of-rotating-machinery(f713f156-11f3-4e10-846e-0b9b709f0ff9).html.

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Vibration-based fault diagnosis (FD) with a simple spectrum can be complex, especially when considering FD of rotating machinery with multiple bearings like a multi-stage turbine. Various studies have sought to better interpret fault spectra, but the process remains equivocal. Consequently, it has been accepted that the simple spectra requires support from additional techniques, such as orbit analysis. But even orbit analysis can be inconclusive. Though promising, attempts at developing viable methods that rival the failure coverage of spectrum analysis without gaining computational complexity remain protracted. Interestingly, few researchers have developed FD methods for transient machine operation, however, these have proven to be involved. Current practices limit vibration data to a single machine, which usually requires a large unique data history. However, if sharing of data between similar machines with different foundations was possible, the need for unique histories would be mitigated. From readily available works, this has not been encountered. Therefore, a simple but robust vibration-based approach is warranted. In light of this, a novel on-bearing vibration response integration approach for condition monitoring of shaft-related faults irrespective of speed and foundation type is proposed in the present study. Vibration data are acquired at different speeds for: a baseline, unbalance, bow, crack, looseness, misalignment, and rub conditions on three laboratory rigs with dynamically different foundations, namely: rigid, flexible support 1 (FS1) and flexible support 2 (FS2). Testing is done on the rigid rig set up first, then FS1, and afterwards FS2. Common vibration features are computed from the measured data to be input to the proposed approach for further processing. First, the proposed approach is developed through its application to a machine at a steady speed in a novel Single-speed FD technique which exploits a single vibration sensor per bearing and fusion of features from different bearings for FD. Initially, vibration features are supplemented with bearing temperature readings with improved classification compared to vibration features alone. However, it is observed that temperature readings are insensitive to faults on the FS1 and FS2 rigs, when compared to vibration features, which are standardised for consistent classification on the different rigs tested. Thus, temperature is not included as a final feature. The observed fault classifications on the different rigs at different speeds with the standardised vibration features are encouraging. Thereafter, a novel Unified Multi-speed FD technique that is based on the initial proposed approach and which works by fusion of vibration features from different bearings at different speeds in a single analysis step for FD is proposed. Experiments on the different rigs repeatedly show the novel Multi-speed technique to be suitable for transient machine operation. Then, a novel generic Multi-foundation Technique (also based on the proposed approach) that allows sharing of vibration data of a wide range of fault conditions between two similarly configured machines with similar speed operation but different foundations is implemented to further mitigate data requirements in the FD process. Observations made with the rigs during steady and transient speed tests show this technique is applicable in situations where data history is available on one machine but lacking on the other. Comparison of experimental results with results obtained from theoretical simulations indicates the approach is consistent. Thus, the proposed approach has the potential for practical considerations.
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Books on the topic "Condition monitoring and vibration analysis"

1

Liu, Chao-Shih. Analysis, approach and assessment of vibration criteria in shipboard machinery condition monitoring and diagnostics. Monterey, Calif: Naval Postgraduate School, 1993.

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Liu, Chao-Shih. Analysis, approach and assessment of vibration criteria in shipboard machinery condition monitoring and diagnostics. Monterey, Calif: Naval Postgraduate School, 1993.

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Randall, Robert Bond. Vibration-based Condition Monitoring. Chichester, UK: John Wiley & Sons, Ltd, 2011. http://dx.doi.org/10.1002/9780470977668.

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Vibratory condition monitoring of machines. Boca Raton, Fla: CRC Press, 2000.

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Barszcz, Tomasz. Vibration-Based Condition Monitoring of Wind Turbines. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05971-2.

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Vibration-based condition monitoring: Industrial, aerospace, and automotive applications. Chichester, West Sussex, U.K: Wiley, 2011.

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Randall, Robert Bond. Vibration-based condition monitoring: Industrial, aerospace, and automotive applications. Chichester, West Sussex, U.K: Wiley, 2010.

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Thomson, William T., and Ian Culbert. Current Signature Analysis for Condition Monitoring of Cage Induction Motors. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781119175476.

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Eisenmann, Robert C. Machinery malfunction diagnosis and correction: Vibration analysis and troubleshooting for the process industries. Upper Saddle River, N.J: Prentice Hall PTR, 1998.

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1970-, Eisenmann Robert C., ed. Machinery malfunction diagnosis and correction: Vibration analysis and troubleshooting for the process industries. Upper Saddle River, N.J: PTR Prentice Hall, 1998.

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Book chapters on the topic "Condition monitoring and vibration analysis"

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Barszcz, Tomasz. "Standard Vibration Analysis Methods." In Applied Condition Monitoring, 33–86. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05971-2_2.

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Kruczek, Piotr, Norbert Gomolla, Agnieszka Wyłomańska, and Radosław Zimroz. "Cyclostationary Approach for Long Term Vibration Data Analysis." In Applied Condition Monitoring, 373–81. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11220-2_38.

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Merzoug, Mustapha, Khalid Ait-Sghir, Abdelhamid Miloudi, Jean Paul Dron, and Fabrice Bolaers. "Early Detection of Gear Failure by Vibration Analysis." In Applied Condition Monitoring, 69–79. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14532-7_8.

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Grajales, Jairo A., Héctor F. Quintero, Carlos A. Romero, and Edison Henao. "Engine Diagnosis Based on Vibration Analysis Using Different Fuel Blends." In Applied Condition Monitoring, 267–74. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61927-9_25.

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Bouslema, Marwa, Taher Fakhfakh, Rachid Nasri, and Mohamed Haddar. "Vibration Analysis of Planetary Gear Connected by Flexible Coupling Using Substructuring Methodology." In Applied Condition Monitoring, 198–206. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85584-0_20.

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Saadat, Boulanouar, Abdallah Kouzou, Ahmed Hafaifa, and Mouloud Guemana. "Gas Turbine Supervision Based on Vibration Analysis and Measurement: Gas Compression Station Investigation." In Applied Condition Monitoring, 1–14. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62042-8_1.

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Łubiński, Jacek, and Krzysztof Druet. "The Application of Vibration Recording and Analysis in Tribological Research on Sliding Friction." In Applied Condition Monitoring, 203–13. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62042-8_19.

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Hamza, Ghazoi, Moncef Hammadi, Maher Barkallah, Jean-Yves Choley, Alain Riviere, Jamel Louati, and Mohamed Haddar. "Analytical Based Approach for Vibration Analysis in Modelica: Application to the Bridge Crane System." In Applied Condition Monitoring, 83–91. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94616-0_8.

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Sinha, Jyoti K. "Experimental Modal Analysis." In Industrial Approaches in Vibration-Based Condition Monitoring, 159–91. Boca Raton : CRC Press, 2020.: CRC Press, 2020. http://dx.doi.org/10.1201/9781315147222-8.

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Sawalhi, Nader, and Suri Ganeriwala. "Analysis and Signal Processing of a Gearbox Vibration Signal with a Defective Rolling Element Bearing." In Applied Condition Monitoring, 71–85. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20463-5_6.

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Conference papers on the topic "Condition monitoring and vibration analysis"

1

Petty, D. J. "Analysis of bearing vibration monitoring." In IEE Colloquium Understanding Your Condition Monitoring. IEE, 1999. http://dx.doi.org/10.1049/ic:19990654.

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Liu, Zhiyuan, Xinqian Xia, Shengchang Ji, Yuhang Shi, Fan Zhang, Yu Fu, and Zhitong Jiang. "Fault Diagnosis of OLTC Based on Time-Frequency Image Analysis of Vibration Signal." In 2018 Condition Monitoring and Diagnosis (CMD). IEEE, 2018. http://dx.doi.org/10.1109/cmd.2018.8535918.

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Zhan, Cao, Shengchang Ji, Yong Liu, Lingyu Zhu, Yuhang Shi, and Fuqiang Ren. "Winding Mechanical Fault Diagnosis Technique of Power Transformer Based on Time-Frequency Vibration Analysis." In 2018 Condition Monitoring and Diagnosis (CMD). IEEE, 2018. http://dx.doi.org/10.1109/cmd.2018.8535963.

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Minaiepour, Hamidreza, Hamid Sarabeigi, and Ehsan Heydari. "Surveying compressor C-2501 vibration problem by analysis of vibration and its analytical report." In 2012 IEEE International Conference on Condition Monitoring and Diagnosis (CMD). IEEE, 2012. http://dx.doi.org/10.1109/cmd.2012.6416244.

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Sewak, Ram, Rajesh Ranjan, and A. K. L. Rao. "Intricate aspects Of turbogenerator endwinding vibration monitoring based on data analysis." In 2008 International Conference on Condition Monitoring and Diagnosis. IEEE, 2008. http://dx.doi.org/10.1109/cmd.2008.4580247.

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Ma Qixiao, Ding Dengwei, Xue Zhihang, and Li Weiwei. "Abnormal vibration analysis of GIS based on time frequency detection." In 2016 International Conference on Condition Monitoring and Diagnosis (CMD). IEEE, 2016. http://dx.doi.org/10.1109/cmd.2016.7757889.

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Munir, Buyung Sofiarto, Johan J. Smit, and I. G. M. Rama Rinaldi. "Diagnosing winding and core condition of power transformer by vibration signal analysis." In 2012 IEEE International Conference on Condition Monitoring and Diagnosis (CMD). IEEE, 2012. http://dx.doi.org/10.1109/cmd.2012.6416170.

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Albarbar, A., R. Gennish, F. Gu, and A. Ball. "Lubricating Oil Condition Monitoring Using Vibration and Air-Borne Acoustic Measurements." In ASME 7th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2004. http://dx.doi.org/10.1115/esda2004-58360.

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Modern diesel engine maintenance programs incorporate various methods and techniques for early fault detection and diagnosis to maintain efficiency, low pollution and high reliability and to avoid catastrophic failures. This study has been conducted aiming at engine oil condition monitoring and quality evaluation by analysing the engine block vibration and its induced noise. The vibration signals were measured using an accelerometer mounted on the thrust side of the first cylinder in a four cylinder diesel engine, and the noise was recorded using a microphone facing the cylinder. The signals are then band pass filtered and transformed to the frequency domain, where the amplitudes of the different frequency components of the vibration and noise waveforms are analysed and compared to the vibration and noise baseline signatures. The mean amplitudes of the spectral components in the frequency band 900 Hz to 2.5 kHz were found linearly proportional to the engine speed and load. It was also found that the RMS values of this frequency band are affected by the oil viscosity. These results show that it is possible to use vibration and airborne acoustics to predict the quality of lubrication.
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Tsypkin, Mikhail. "Induction motor condition monitoring: Vibration analysis technique - A practical implementation." In Drives Conference (IEMDC). IEEE, 2011. http://dx.doi.org/10.1109/iemdc.2011.5994629.

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Zhanling Li, Shunong Zhang, and Wenwen Shi. "An integrated method to detect the incipient degradation of bearings by vibration analysis and feature extraction." In 2016 International Conference on Condition Monitoring and Diagnosis (CMD). IEEE, 2016. http://dx.doi.org/10.1109/cmd.2016.7757914.

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Reports on the topic "Condition monitoring and vibration analysis"

1

Sheng, Shuangwen. Wind Turbine Gearbox Condition Monitoring Round Robin Study - Vibration Analysis. Office of Scientific and Technical Information (OSTI), July 2012. http://dx.doi.org/10.2172/1048981.

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Yacout, A. M., and Y. Orechwa. Material degradation analysis and maintenance decisions based on material condition monitoring during in-service inspections. Office of Scientific and Technical Information (OSTI), March 1996. http://dx.doi.org/10.2172/380364.

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Upadhyaya, Belle, J. Wesley Hines, Brian Damiano, Chaitanya Mehta, Price Collins, Matthew Lish, Brian Cady, Victor Lollar, Dane de Wet, and Duygu Bayram. In-situ Condition Monitoring of Components in Small Modular Reactors Using Process and Electrical Signature Analysis. Final report, volume 1. Development of experimental flow control loop, data analysis and plant monitoring. Office of Scientific and Technical Information (OSTI), December 2015. http://dx.doi.org/10.2172/1183686.

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Dodd, Hope, David Peitz, Gareth Rowell, Janice Hinsey, David Bowles, Lloyd Morrison, Michael DeBacker, Jennifer Haack-Gaynor, and Jefrey Williams. Protocol for Monitoring Fish Communities in Small Streams in the Heartland Inventory and Monitoring Network. National Park Service, April 2021. http://dx.doi.org/10.36967/nrr-2284726.

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Fish communities are an important component of aquatic systems and are good bioindicators of ecosystem health. Land use changes in the Midwest have caused sedimentation, erosion, and nutrient loading that degrades and fragments habitat and impairs water quality. Because most small wadeable streams in the Heartland Inventory and Monitoring Network (HTLN) have a relatively small area of their watersheds located within park boundaries, these streams are at risk of degradation due to adjacent land use practices and other anthropogenic disturbances. Shifts in the physical and chemical properties of aquatic systems have a dramatic effect on the biotic community. The federally endangered Topeka shiner (Notropis topeka) and other native fishes have declined in population size due to habitat degradation and fragmentation in Midwest streams. By protecting portions of streams on publicly owned lands, national parks may offer refuges for threatened or endangered species and species of conservation concern, as well as other native species. This protocol describes the background, history, justification, methodology, data analysis and data management for long-term fish community monitoring of wadeable streams within nine HTLN parks: Effigy Mounds National Monument (EFMO), George Washington Carver National Monument (GWCA), Herbert Hoover National Historic Site (HEHO), Homestead National Monument of America (HOME), Hot Springs National Park (HOSP), Pea Ridge National Military Park (PERI), Pipestone National Monument (PIPE), Tallgrass Prairie National Preserve (TAPR), and Wilson's Creek national Battlefield (WICR). The objectives of this protocol are to determine the status and long-term trends in fish richness, diversity, abundance, and community composition in small wadeable streams within these nine parks and correlate the long-term community data to overall water quality and habitat condition (DeBacker et al. 2005).
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Simms, Janet, Benjamin Breland, and William Doll. Geophysical investigation to assess condition of grouted scour hole : Old River Control Complex—Low Sill Concordia Parish, Louisiana. Engineer Research and Development Center (U.S.), September 2021. http://dx.doi.org/10.21079/11681/41863.

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Geophysical surveys, both land-based and water-borne, were conducted at the Old River Control Complex‒Low Sill, Concordia Parish, LA. The purpose of the surveys was to assess the condition of the grout within the scour region resulting from the 1973 flood event, including identification of potential voids within the grout. Information from the ground studies will also be used for calibration of subsequent marine geophysical data and used in stability analysis studies. The water-borne survey consisted of towed low frequency (16-80 MHz) ground penetrating radar (GPR), whereas the land-based surveys used electrical resistivity and seismic refraction. The GPR survey was conducted in the Old River Channel on the upstream side of the Low Sill structure. The high electrical conductivity of the water (~50 mS/m) precluded penetration of the GPR signal; thus, no useful data were obtained. The land-based surveys were performed on both northeast and southeast sides of the Low Sill structure. Both resistivity and seismic surveys identify a layered subsurface stratigraphy that corresponds, in general, with available borehole data and constructed geologic profiles. In addition, an anomalous area on the southeast side was identified that warrants future investigation and monitoring.
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