Relevant bibliographies by topics / Non stationary rotating machinery surveilance

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Non stationary rotating machinery surveilance'

Author: Grafiati
Published: 6 June 2023

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Journal articles on the topic "Non stationary rotating machinery surveilance"

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Isham, M. Firdaus, M. Salman Leong, M. H. Lim, and M. K. Zakaria. "A Review on Variational Mode Decomposition for Rotating Machinery Diagnosis." MATEC Web of Conferences 255 (2019): 02017. http://dx.doi.org/10.1051/matecconf/201925502017.

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Signal processing method is very important in most diagnosis approach for rotating machinery due to non-linearity, non-stationary and noise signals. Recently, a new adaptive signal decomposition method has been proposed by Dragomiretskiy and Zosso known as variational mode decomposition (VMD). The VMD method has merit in solving mode mixing problem in most conventional signal decomposition method. This paper aims to review the applications of the VMD method in rotating machinery diagnosis. The advantages and limitations of the VMD method are discussed. Current solution on VMD limitation also have been review and discussed. Lastly, the future research suggestion has been pointed out in order to enhance the performance of the VMD method on rotating machinery diagnosis.
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Peeters, Cédric, Andreas Jakobsson, Jérôme Antoni, and Jan Helsen. "Improved Time-Frequency Representation for Non-stationary Vibrations of Slow Rotating Machinery." PHM Society European Conference 7, no. 1 (June 29, 2022): 401–9. http://dx.doi.org/10.36001/phme.2022.v7i1.3363.

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The short-time Fourier transform (STFT) is a staple analysis tool for vibration signal processing due to it being a robust, non-parametric, and computationally efficient technique to analyze non-stationary signals. However, despite these beneficial properties, the STFT suffers from high variance, high sidelobes, and a low resolution. This paper investigates an alternative non-parametric method, namely the sliding-window iterative adaptive approach, to use for time-frequency representations of non-stationary vibrations. This method reduces the sidelobe levels and allows for high resolution estimates. The performance of the method is evaluated on both simulated and experimental vibration data of slow rotating machinery such as a multi-megawatt wind turbine gearbox. The results indicate significant benefits as compared to the STFT with regard to accuracy, readability, and versatility.
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Chen, Chih-Hao, Rong-Juin Shyu, and Chih-Kao Ma. "A New Fault Diagnosis Method of Rotating Machinery." Shock and Vibration 15, no. 6 (2008): 585–98. http://dx.doi.org/10.1155/2008/203621.

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This paper presents a new fault diagnosis procedure for rotating machinery using the wavelet packets-fractal technology and a radial basis function neural network. The faults of rotating machinery considered in this study include imbalance, misalignment, looseness and imbalance combined with misalignment conditions. When such faults occur, they usually induce non-stationary vibrations to the machine. After measuring the vibration signals, the wavelet packets transform is applied to these signals. The fractal dimension of each frequency bands is extracted and the box counting dimension is used to depict the failure characteristics of the vibration signals. The failure modes are then classified by a radial basis function neural network. An experimental study was performed to evaluate the proposed method and the results show that the method can effectively detect and recognize different kinds of faults of rotating machinery.
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Komorska, Iwona, and Andrzej Puchalski. "Rotating Machinery Diagnosing in Non-Stationary Conditions with Empirical Mode Decomposition-Based Wavelet Leaders Multifractal Spectra." Sensors 21, no. 22 (November 18, 2021): 7677. http://dx.doi.org/10.3390/s21227677.

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Diagnosing the condition of rotating machines by non-invasive methods is based on the analysis of dynamic signals from sensors mounted on the machine—such as vibration, velocity, or acceleration sensors; torque meters; force sensors; pressure sensors; etc. The article presents a new method combining the empirical mode decomposition algorithm with wavelet leader multifractal formalism applied to diagnosing damages of rotating machines in non-stationary conditions. The development of damage causes an increase in the level of multifractality of the signal. The multifractal spectrum obtained as a result of the algorithm changes its shape. Diagnosis is based on the classification of the features of this spectrum. The method is effective in relation to faults causing impulse responses in the dynamic signal registered by the sensors. The method has been illustrated with examples of vibration signals of rotating machines recorded on a laboratory stand, as well as on real objects.
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Gao, Yiyuan, Wenliao Du, Xiaoyun Gong, and Dejie Yu. "Graph-domain features and their application in rotating machinery fault diagnosis." IOP Conference Series: Materials Science and Engineering 1207, no. 1 (November 1, 2021): 012008. http://dx.doi.org/10.1088/1757-899x/1207/1/012008.

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Abstract To more effectively extract the non-stationary and non-linear fault features of mechanical vibration signals, a novel fault diagnosis method for rotating machinery is proposed combining time-domain, frequency-domain with graph-domain features. Different from the conventional time-domain and frequency-domain features, the graph-domain features generated from horizontal visibility graphs can extract the fault information hidden in the graph topology. Aiming at the problem that too many features will lead to information redundancy, the Fisher score algorithm is applied to select several of sensitive features which are then fed into the support vector machine to diagnose the faults of rotating machinery. Experimental results indicate features extracted from the three domains can be used to obtain higher diagnosis accuracy than that extracted from any single domain or dual domains.
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Patel, R. K., and V. K. Giri. "Condition monitoring of induction motor bearing based on bearing damage index." Archives of Electrical Engineering 66, no. 1 (March 1, 2017): 105–19. http://dx.doi.org/10.1515/aee-2017-0008.

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Abstract The rolling element bearings are used broadly in many machinery applications. It is used to support the load and preserve the clearance between stationary and rotating machinery elements. Unfortunately, rolling element bearings are exceedingly prone to premature failures. Vibration signal analysis has been widely used in the faults detection of rotating machinery and can be broadly classified as being a stationary or non-stationary signal. In the case of the faulty rolling element bearing the vibration signal is not strictly phase locked to the rotational speed of the shaft and become “transient” in nature. The purpose of this paper is to briefly discuss the identification of an Inner Raceway Fault (IRF) and an Outer Raceway Fault (ORF) with the different fault severity levels. The conventional statistical analysis was only able to detect the existence of a fault but unable to discriminate between IRF and ORF. In the present work, a detection technique named as bearing damage index (BDI) has been proposed. The proposed BDI technique uses wavelet packet node energy coefficient analysis method. The well-known combination of Hilbert transform (HT) and Fast Fourier Transform (FFT) has been carried out in order to identify the IRF and ORF faults. The results show that wavelet packet node energy coefficients are not only sensitive to detect the faults in bearing but at the same time they are able to detect the severity level of the fault. The proposed bearing damage index method for fault identification may be considered as an ‘index’ representing the health condition of rotating machines.
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Xiao, Qiyang, Sen Li, Lin Zhou, and Wentao Shi. "Improved Variational Mode Decomposition and CNN for Intelligent Rotating Machinery Fault Diagnosis." Entropy 24, no. 7 (June 30, 2022): 908. http://dx.doi.org/10.3390/e24070908.

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This paper proposes an intelligent diagnosis method for rotating machinery faults based on improved variational mode decomposition (IVMD) and CNN to process the rotating machinery non-stationary signal. Firstly, to solve the problem of time-domain feature extraction for fault diagnosis, this paper proposes an improved variational mode decomposition method with automatic optimization of the number of modes. This method overcomes the problems of the traditional VMD method, in that each parameter is set by experience and is greatly influenced by subjective experience. Secondly, the decomposed signal components are analyzed by correlation, and then high correlated components with the original signal are selected to reconstruct the original signal. The continuous wavelet transform (CWT) is employed to extract the two-dimensional time–frequency domain feature map of the fault signal. Finally, the deep learning method is used to construct a convolutional neural network. After feature extraction, the two-dimensional time-frequency image is applied to the neural network to identify fault features. Experiments verify that the proposed method can adapt to rotating machinery faults in complex environments and has a high recognition rate.
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Li, Hong, Qing He, and Zhao Zhang. "Overview of Time-Frequency Analysis Techniques in Vibration Signals of Rotating Machinery." Applied Mechanics and Materials 684 (October 2014): 124–30. http://dx.doi.org/10.4028/www.scientific.net/amm.684.124.

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There is very rich fault information in vibration signals of rotating machineries. The real vibration signals are nonlinear, non-stationary and time-varying signals mixed with many other factors. It is very useful for fault diagnosis to extract fault features by using time-frequency analysis techniques. Recent researches of time-frequency analysis methods including Short Time Fourier Transform, Wavelet Transform, Wigner-Ville Distribution, Hilbert-Huang Transform, Local Mean Decomposition, and Local Characteristic-scale Decomposition are introduced. The theories, properties, physical significance and applications, advantages and disadvantages of these methods are analyzed and compared. It is pointed that algorithms improvement and combined applications of time-frequency analysis methods should be researched in the future.
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Qi, Xiao Xuan, Jian Wei Ji, and Xiao Wei Han. "Fault Diagnosis Methods of Rolling Bearing: A General Review." Key Engineering Materials 480-481 (June 2011): 986–92. http://dx.doi.org/10.4028/www.scientific.net/kem.480-481.986.

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Rolling bearing failures account for most of rotating machinery failures. Fault diagnosis of rolling bearings according to their running state is of great importance. In this paper current research situation and existing problems of fault diagnosis are summarized firstly. Then several different diagnosis approaches in terms of the measuring medium are reviewed. After analysis of fault mechanism, feature extraction based on non-stationary signal process is elaborated. Finally, the development tendencies are pointed out.
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Su, Zhou, Juanjuan Shi, Yang Luo, Changqing Shen, and Zhongkui Zhu. "Fault severity assessment for rotating machinery via improved Lempel–Ziv complexity based on variable-step multiscale analysis and equiprobable space partitioning." Measurement Science and Technology 33, no. 5 (February 18, 2022): 055018. http://dx.doi.org/10.1088/1361-6501/ac50e8.

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Abstract Fault severity assessment based on vibration measurements for rotating machinery is critical since it can reduce downtime and guarantee the reliability of the equipment. Lempel–Ziv complexity (LZC) has been widely used for the fault severity assessment. However, LZC is of a single-scale analysis and 0-1 encoding, which cannot fully explore the features of vibration signals measured from rotating machinery. This paper, thus, proposes an improved LZC based on the variable-step multiscale analysis (VSMA) and equiprobable space partitioning (ESP) strategies to fully explore features of vibrations of rotating machinery. The VSMA is proposed to overcome the drawback that the single-scale analysis fails to comprehensively uncover features and solve the problem that the traditional multiscale analysis significantly reduces the length of sequences. With the VSMA, a string of time series under different scales can be generated. The ESP is developed to transform the time series into symbolic series, with the capability of reserving the features of vibration signals and being more robust against noise, particularly for non-stationary signals. Then, the ESP based variable-step multiscale LZCs (i.e. ESP-VSMLZCs) are obtained. To fuse the obtained ESP-VSMLZCs and obtain a comprehensive indicator for fault severity assessment, Laplacian score weighting is adopted. As such, a single ESP based variable-step multiscale fusion LZC indicator can be obtained. The proposed indicator is verified by simulated data from a bearing dynamic model and experimental data measured from rotating machinery.
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Dissertations / Theses on the topic "Non stationary rotating machinery surveilance"

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Hawwari, Yasmine. "Developement of some signal processing tools for vibro-acoustic based diagnosis of aeronautic machines." Electronic Thesis or Diss., Lyon, INSA, 2022. https://theses.insa-lyon.fr/publication/2022ISAL0131/these.pdf.

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Le prétraitement des signaux de vibration dans des conditions difficiles comme celles de l'aéronautique semble être compliqué. Les conditions de fonctionnement sont nonstationnaires et le moteur présente au moins deux familles harmoniques non-linéaires liées à l'arbre à basse et haute pression. De plus, les contraintes de conception imposent un nombre réduit d'accéléromètres (généralement deux) qui est insuffisant pour détecter tous les phénomènes liés à l'arbre. Les signaux acoustiques ne sont pas soumis à cette dernière contrainte. Cependant, ils sont très bruyants par rapport aux signaux de vibration et peuvent ne pas détecter les problèmes de faible énergie. De plus, ils dépendent fortement de la position du microphone et de sa directivité. Ainsi, l'objectif de la thèse est de proposer/essayer des méthodes robustes pour principalement (i) l'interférence entre différents phénomènes linéaires et non linéaires, (ii) les conditions de fonctionnement non stationnaires et (iii) les phénomènes de bruit à large bande (lorsqu'ils ne sont pas d'intérêt). Ces difficultés scientifiques sont considérées à travers (1) une détection aveugle des pics spectraux, (2) l'estimation de la vitesse instantanée et (3) l'estimation de la composante déterministe/tonale
Pre-processing vibration signals in harsh conditions such as the aeronautic conditions seems a complicated task. The operating conditions are nonstationary and the motor exhibits at least two harmonic non-linear families related to low and high pressure shaft. Furthermore, the design constraints impose a reduced number of accelerometers (generally two) which is unfortunately insufficient to detect all the shaft related phenomena. The acoustic signals are not subjected to the latter constraint. However, they are very noisy in comparison to vibration signals and may not detect low energy problems and very low frequency phenomena. Besides, the obtained signals depend strongly on the microphone position and its directivity in addition to the problem of clipping with medium to high acoustic pressure values. Thus, the PhD objective is to propose methods robust to mainly (i) the interference between different linear and non-linearly related phenomena, (ii) the nonstationary operating conditions and (iii) the broadband noise phenomena. These scientific difficulties are considered through (1) a blind detection of spectral peaks, (2) the estimation of the instantaneous speed and (3) the estimation of the deterministic/tonal component
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Firla, Marcin. "Automatic signal processing for wind turbine condition monitoring. Time-frequency cropping, kinematic association, and all-sideband demodulation." Thesis, Université Grenoble Alpes (ComUE), 2016. http://www.theses.fr/2016GREAT006/document.

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Cette thèse propose trois méthodes de traitement du signal orientées vers la surveillance d’état et le diagnostic. Les techniques proposées sont surtout adaptées pour la surveillance d’état, effectuée à la base de vibrations, des machines tournantes qui fonctionnent dans des conditions d’opération non-stationnaires comme par exemple les éoliennes mais elles ne sont pas limitées à un tel usage. Toutes les méthodes proposées sont des algorithmes automatiques et gérés par les données.La première technique proposée permet de sélectionner la partie la plus stationnaire d’un signal en cadrant la représentation temps-fréquence d’un signal.La deuxième méthode est un algorithme pour l’association des dispositions spectrales, des séries harmoniques et des séries à bandes latérales avec des fréquences caractéristiques provennant du cinématique d'un système analysé. Cette méthode propose une approche unique dédiée à l’élément roulant du roulement qui permet de surmonter les difficultés causées par le phénomène de glissement.La troisième technique est un algorithme de démodulation de bande latérale entière. Elle fonctionne à la base d’un filtre multiple et propose des indicateurs de santé pour faciliter une évaluation d'état du système sous l’analyse.Dans cette thèse, les méthodes proposées sont validées sur les signaux simulés et réels. Les résultats présentés montrent une bonne performance de toutes les méthodes
This thesis proposes a three signal-processing methods oriented towards the condition monitoring and diagnosis. In particular the proposed techniques are suited for vibration-based condition monitoring of rotating machinery which works under highly non-stationary operational condition as wind turbines, but it is not limited to such a usage. All the proposed methods are automatic and data-driven algorithms.The first proposed technique enables a selection of the most stationary part of signal by cropping time-frequency representation of the signal.The second method is an algorithm for association of spectral patterns, harmonics and sidebands series, with characteristic frequencies arising from kinematic of a system under inspection. This method features in a unique approach dedicated for rolling-element bearing which enables to overcome difficulties caused by a slippage phenomenon.The third technique is an all-sideband demodulation algorithm. It features in a multi-rate filter and proposes health indicators to facilitate an evaluation of the condition of the investigated system.In this thesis the proposed methods are validated on both, simulated and real-world signals. The presented results show good performance of all the methods
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Book chapters on the topic "Non stationary rotating machinery surveilance"

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Khelf, Ilyes, Lakhdar Laouar, Hocine Bendjama, and Abdelaziz Mahmoud Bouchelaghem. "Combining RBF-PCA-ReliefF Filter for a Better Diagnosis Performance in Rotating Machines." In Condition Monitoring of Machinery in Non-Stationary Operations, 285–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28768-8_30.

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Bachschmid, Nicolò, and Steven Chatterton. "Dynamical Behavior of Rotating Machinery in Non-Stationary Conditions: Simulation and Experimental Results." In Lecture Notes in Mechanical Engineering, 3–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39348-8_1.

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Conference papers on the topic "Non stationary rotating machinery surveilance"

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Al-Badour, Fadi, L. Cheded, and M. Sunar. "Non-stationary vibration signal analysis of rotating machinery via time-frequency and wavelet techniques." In 2010 10th International Conference on Information Sciences, Signal Processing and their Applications (ISSPA). IEEE, 2010. http://dx.doi.org/10.1109/isspa.2010.5605563.

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Jia, Minping, and R. Du. "Fault Diagnosing of Large Rotating Machinery Using Evolutionary Spectrum." In ASME 1998 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1998. http://dx.doi.org/10.1115/imece1998-1020.

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Abstract Fault diagnosis of large rotating machinery sets is very important for many industry sectors. However, it is also difficult because of its complexity. The conventional fault diagnosis methods are based on FFT spectral analysis. While its effectiveness has been approved by many applications, it also has significant limitations. In fact, it has been found that many faults have similar spectral patterns. In order to pinpoint the cause of the faults, it may be necessary to vary the speed to get additional information. Nevertheless, the speed variation makes the vibration signal non-stationary. This in turn makes the FFT spectral analysis imperative. In this paper, it is proposed to use a new technique, called evolutionary spectrum, to analyze the non-stationary signal and hence diagnose the faults. First, following a brief introduction of wavelet transform, the evolutionary spectrum is described. Next, the implementation procedure of the evolutionary spectrum is given. Then, by means of computer simulation and experiment results analysis, it is shown that the evolutionary spectrum can effective capture the fault patterns and hence, diagnose rotor unbalance and rotor stiffness asymmetric.
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Liang, Ruijun, Wenfeng Ran, Wenlong Hao, and Yang Li. "Simulation of the dynamic cutting force loading on a rotating and feeding spindle." In 2021 7th International Conference on Condition Monitoring of Machinery in Non-Stationary Operations (CMMNO). IEEE, 2021. http://dx.doi.org/10.1109/cmmno53328.2021.9467564.

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Gryllias, Konstantinos, Simona Moschini, and Jerome Antoni. "Application of Cyclo-Non-Stationary Indicators for Bearing Monitoring Under Varying Operating Conditions." In ASME Turbo Expo 2017: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/gt2017-64443.

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Condition monitoring assesses the operational health of rotating machinery, in order to provide early and accurate warning of potential failures such that preventative maintenance actions may be taken. To achieve this target, manufacturers start taking on the responsibilities of engine condition monitoring, by embedding health monitoring systems within each engine unit and prompting maintenance actions when necessary. Several types of condition monitoring are used including oil debris monitoring, temperature monitoring and vibration monitoring. Among them, vibration monitoring is the most widely used technique. Machine vibro-acoustic signatures contain pivotal information about its state of health. The current work focuses on one part of the diagnosis stage of condition monitoring for engine bearing health monitoring as bearings are critical components in rotating machinery. A plethora of signal processing tools and methods applied at the time domain, the frequency domain, the time-frequency domain and the time-scale domain have been presented in order to extract valuable information by proposing different diagnostic features. Among others, an emerging interest has been reported on modeling rotating machinery signals as cyclostationary, which is a particular class of non-stationary stochastic processes. A process x(t) is said to be nth-order cyclostationary with period T if its nth-order moments exist and are periodic with period T. Several tools, such as the Spectral Correlation Density (SCD) and the Cyclic Modulation Spectrum (CMS) can be used in order to extract interesting information concerning the cyclic behavior of cyclostationary signals. In order to measure the cyclostationarity from order 1 to 4, concise and global indicators have been proposed. However, in a number of applications such as aircraft engines and wind turbines the characteristic vibroacoustic signatures of rotating machinery depend on the operating conditions of the rotational speed and/or the load. During the last decades fault diagnostics of rotating machinery under variable speed/load has attracted a lot of interest. The classical cyclostationary tools can be used under the assumption that the speed of machinery is constant or nearly constant, otherwise the vibroacoustic signal becomes cyclo-non-stationary. In order to overcome this limitation a generalization of both SCD and CMS functions have been proposed displaying cyclic Order versus Frequency. The goal of this paper is to propose a novel approach for the analysis of cyclo-nonstationary signals based on the generalization of indicators of cyclostationarity in order to cover the speed varying conditions. The proposed indicators of cyclo-non-stationarity (ICNS) are expected to summarize the information at various statistical orders and at lower computational cost compared to the Order-Frequency SCD or CMS. This generalization is realized by introducing a new speed-dependent angle averaging operator. The effectiveness of the approach is evaluated on an acceleration signal captured on the casing of an aircraft engine gearbox, provided by SAFRAN, in the frames of SAFRAN contest which took place at the Surveillance 8 International Conference.
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Mauricio, Alexandre, Dustin Helm, Markus Timusk, Jerome Antoni, and Konstantinos Gryllias. "Novel Cyclo-Non-Stationary Indicators for Monitoring of Rotating Machinery Operating Under Speed and Load Varying Conditions." In ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/gt2020-15245.

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Abstract Condition monitoring arises as a valuable industrial process in order to assess the health of rotating machinery, providing early and accurate warning of potential failures and allowing for the planning and effective realization of preventative maintenance actions. Nowadays machinery (gas turbines, wind turbines etc.) manufacturers adopt new business models, providing not only the equipment itself but additionally taking on responsibilities of condition monitoring, by embedding sensors and health monitoring systems within each unit and prompting maintenance actions when necessary. Among others, rolling element bearings are one of the most critical components in rotating machinery. In complex machines the failure indications of an early bearing damage are weak compared to other sources of excitations (e.g. gears, shafts, rotors etc.). Vibration analysis is most widely used and various methods have been proposed, including analysis in the time and frequency domain. In a number of applications, changes in the operating conditions (speed/load) influence the vibration sources and change the frequency and amplitude characteristics of the vibroacoustic signature, making them nonstationary. Under changing environments, where speed and load vary, the assumption of quasi-stationary is not appropriate and as a result a number of time-frequency and time-order representations have been introduced, such as the Short Time Fourier Transform and the Wavelets. Recently an emerging interest has been focused on modelling rotating machinery signals as cyclostationary, which is a particular class of non-stationary stochastic processes. The classical cyclostationary tools, such as the Cyclic Spectral Correlation Density (CSCD) and the Cyclic Modulation Spectrum (CMS), can be used in order to extract interesting information about the cyclic behavior of cyclostationary signals, only under the assumption that the speed of machinery is constant or nearly constant. Global diagnostic indicators have been proposed as a measure of cyclostationarity under steady operating conditions. In order to overcome this limitation a generalization of both SCD and CMS functions have been proposed displaying cyclic Order versus Frequency as well as diagnostic indicators of cyclo-non-stationarity in order to cover the speed varying operating conditions. The scope of this paper is to propose a novel approach for the analysis of cyclo-non-stationary signals based on the generalization of indicators of cyclo-non-stationarity in order to cover the simultaneous and independently varying speed and load operating conditions. The effectiveness of the approach is evaluated on simulated and real signals captured on a dedicated test rig.
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Yazdanianasr, Mahsa, Alexandre Mauricio, and Konstantinos Gryllias. "Evaluation of the Improved Envelope Spectrum via Feature Optimization-gram (IESFOgram) for bearing diagnostics under low rotating speeds." In 2021 7th International Conference on Condition Monitoring of Machinery in Non-Stationary Operations (CMMNO). IEEE, 2021. http://dx.doi.org/10.1109/cmmno53328.2021.9467528.

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Greenhill, Lyn M., and Linda F. Raven. "Damped Vibration Absorbers Applied to Lateral Modes of Rotating Machinery." In ASME 1999 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/detc99/vib-8292.

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Abstract Damped vibration absorbers can significantly reduce the amplitude of resonant motion. Normally, these devices are used on machinery that is non-rotating (stationary). However, as this paper demonstrates both analytically and experimentally, a damped absorber can be successfully applied on rotating equipment, particularly on vertical machines, to attenuate lateral resonances. To illustrate this application, a detailed analysis of the damped absorber is presented, focusing on mass ratio, tuning frequency, amount of damping, and speed effects. It is shown that an optimum design can be obtained for use on a rotating machine that parametrically differs from a non-rotating application. Test data is also given illustrating the effectiveness of the concept and design methodology on an actual machine. Recommendations are provided to guide the application of this technology on other rotating machines.
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Zang, Tingpeng, Guangrui Wen, and Guanghua Xu. "Application of Speed Transform to Diagnosis of Rotating Machinery With Varying Speeds." In ASME 2015 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/imece2015-50105.

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The rotor startup vibration signals carry abundant dynamic information of the machinery and are very useful for feature extraction and potential early fault diagnosis. Due to the non-stationary and transient nature of the signals in speed up process, the traditional diagnostic methods that have been put forward based on stationary assumption are no longer satisfactory. This paper proposes a new Speed Transform based method for the fault diagnosis of rotating machinery in variable speed. Speed Transform decomposes a complicated signal over a basis of elementary oscillatory functions, whose frequencies follow the speed variation. The effectiveness of the proposed method is demonstrated by both simulated signal and startup vibration signal collected from a rotor system with early rub-impact fault. Analyzed results showed that the proposed method could effectively extract fault features of the rotor under varying speed condition.
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Choi, Yeon-Sun. "Fault Diagnosis of Rotating Machinery Due to Clearance Using Hilbert-Huang Transform." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-86332.

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The faults in rotating machinery, caused by the clearance between the rotor and the stator, commonly lie on partial rub and looseness. These problems cause malfunctions in rotating machinery since they create strange vibrations coming from impact and friction. However, non-linear and non-stationary signals due to impact and friction are difficult to identify. Therefore, exact time and frequency information are needed for identifying these signals. For this purpose, a newly developed time-frequency analysis method, HHT(Hilbert-Huang Transform), is applied to the signals of partial rub and looseness from the experiment RK-4 rotor kit. Conventional signal processing methods such as FFT, STFT and CWT were compared to verify the effectiveness of fault diagnosis using HHT. The results showed that the impact signals were generated regularly when partial rub occurred but that intermittent impact and friction occurred irregularly when looseness occurred. The time and frequency information was represented exactly by using HHT in both cases, which makes clear fault diagnosis between partial rub and looseness.
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Raman, Arvind, Patricia Davies, and Anil K. Bajaj. "Analytical Prediction of Nonlinear System Response to Non-Stationary Excitations." In ASME 1993 Design Technical Conferences. American Society of Mechanical Engineers, 1993. http://dx.doi.org/10.1115/detc1993-0127.

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Abstract The study of the non-stationary response of systems has many applications in problems related to transition through resonance in rotating machinery, aerospace structures and other physical systems. In this paper, we present methods to analytically predict the response of some weakly nonlinear systems to slowly varying parameter changes. We consider systems which can be averaged and represented as two first order equations. The evolution of the solutions of such systems through critical (jump or bifurcation) points is studied using the method of matched asymptotic expansions. As an example, the method is used to predict the response of the forced Duffing’s oscillator during passage through resonance. Starting with a general system of two, first-order equations, we set up a slowly varying equilibrium or ‘outer’ solution as an asymptotic expansion about the stationary solution. This solution is seen to be invalid in a small neighborhood of the critical points — the ‘inner’ region. In this inner layer, the system of equations is transformed into the Jordan canonical form, which is easier to study. Using approximations from the center manifold theory, the problem is reduced to one first-order equation. By making appropriate scale changes, an ‘inner’ solution is developed. This solution is asymptotically matched with the outer expansion to yield a unified solution valid for all time.
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You might also be interested in the extended bibliographies on the topic 'Non stationary rotating machinery surveilance' for particular source types:
Journal articles
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