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Journal articles on the topic 'Non stationary rotating machinery surveilance'
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1
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.
3
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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Peng, Min. "Near-Field Acoustic Holography of Cyclostationary Sound Field." Advanced Materials Research 631-632 (January 2013): 1318–23. http://dx.doi.org/10.4028/www.scientific.net/amr.631-632.1318.
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The radiated sound field of rotating machinery or reciprocating machinery has a significant periodically time-variant nature. This is a kind of non-stationary sound field and called cyclostationary sound field. In the conventional planar near-field acoustic holography(PNAH), this kind of sound field is treated as stationary field, so the information relating to the change of frequency with time will be loss inevitably. In this article, the cyclic spectral density(CSD) instead of the complex sound pressure was adopted as reconstructing physical quantity in the PNAH, and the cyclostationary PNAH(CPNAH) technique was proposed. Meanwhile, focusing on the calculation complex of CSD and the accuracy of the cyclic nature extracted, the gathering slice method of CSD was proposed by referring time aliasing methods on time series. The experiment results illustrate that the cyclic nature of cyclostationary sound field may be extracted directly and the location of the source determined exactly as well.
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Farhat, Mohamed Habib, Xavier Chiementin, Fakher Chaari, Fabrice Bolaers, and Mohamed Haddar. "Order-Based Identification of Bearing Defects under Variable Speed Condition." Applied Sciences 11, no. 9 (April 27, 2021): 3962. http://dx.doi.org/10.3390/app11093962.
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Condition monitoring of rotating machinery plays an important role in reducing catastrophic failures and production losses in the 4.0 Industry. Vibration analysis has proven to be effective in diagnosing rotating machine failures. However, identifying bearing defects based on vibration analysis remains a difficult task, especially in non-stationary operation conditions. This work aims to automate the process of identifying bearing defects under variable operating speeds. Based on an order analysis technique, three frequency domain features: Spectrum peak Ratio Outer (SPRO), Spectrum peak Ratio Inner (SPRI), and Spectrum peak Ratio Rolling element (SPRR) are updated to perform with non-stationary signals. The updated features are extracted from vibration data of a real ball bearing system. They are retained to build a predictive multi-kernel support vector machine (MSVM) classification model. Therefore, the effectiveness of the proposed features is evaluated based on the performance of the constructed classifier. The updated features deployed have proven their effectiveness in identifying bearing: outer race, inner race, ball, and combined defects under variable speed conditions.
13
Ma, Shengheng, Gang Li, Changshan Chen, Xiao Wang, Xianguang Guo, and Xueli An. "A pedestal looseness fault diagnosis method based on NA-MEMD." Journal of Physics: Conference Series 2360, no. 1 (November 1, 2022): 012003. http://dx.doi.org/10.1088/1742-6596/2360/1/012003.
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When pedestal looseness fault occurs, rotor system’s vibration signals have non-stationary modulation characteristics. And traditional resonance demodulation method is difficult to determine the filter parameters. According to the fact, a pedestal looseness fault determination strategy of rotor system by using NA-MEMD (Noise-Assisted MEMD) is proposed. We use NA-MEMD to disassemble vibration signal into several stationary components. Envelope demodulation strategy is utilized to analyze each component. The vibration signal’s fault message can be acquired through this way. The displayed strategy is utilized to analyze the simulated signal and pedestal looseness fault vibration signal. The comes about appear that the displayed strategy can successfully extricate vibration signal’s fault characteristics of pedestal looseness fault in rotating machinery.
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Wang, Feng Li, and De You Zhao. "Fault Diagnosis of Rotating Machinery Based on Local Wave Decomposition and Independent Component." Advanced Materials Research 129-131 (August 2010): 301–5. http://dx.doi.org/10.4028/www.scientific.net/amr.129-131.301.
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Local rub-impact is common faults in rotating machinery and results in impact and friction between rotor and stator. The vibration signals due to impact and friction are always non-stationary which includes the rub-impact signal, the background signal and the noise signal. Local wave decomposition (LWD) is based upon the local characteristic time scale of signal and could decompose the complicated signal into a number of intrinsic mode functions (IMFs). However, because the weak rub-impact signal is always submerged in the background and noise signals. The LWD procedure would generate the components redundancy. In order to solve the problem, a novel method combining with independent component analysis (ICA) and LWD is proposed. ICA was introduced into LWD, so that the components are orthogonal to each other and the components redundancy can be removed. In the end, a much better decomposition performances can be obtained. Experimental analysis results show that the proposed method is accurate and efficient.
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Xiang, Ling, and Hao Sun. "Comparison of Methods for Time-Frequency Analysis of Oil Whip Vibration Signal." Advanced Materials Research 211-212 (February 2011): 983–87. http://dx.doi.org/10.4028/www.scientific.net/amr.211-212.983.
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The signal analysis is important in extracting fault characteristics in fault diagnosis of machinery. To deal with non-stationary signal, time-frequency analysis techniques are widely used. The experiment data of oil whip vibration fault signal were analyzed by different methods, such as short time Fourier transform (STFT), Wigner-Ville distribution (WVD), Wavelet transform (WT) and Hilbert-Huang Transform (HHT). Compared with these methods, it is demonstrated that the time-frequency resolutions of STFT and WVD were inconsistent, which were easy to cross or make signal lower. WT had distinct time-frequency distribution, but it brought redundant component. HHT time-frequency analysis can detect components of low energy, and displayed true and distinct time-frequency distribution. Therefore, it is a very effective tool to diagnose the faults of rotating machinery.
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Su, Chang Qing, and Yi Min Zhang. "Reliability Analysis for Rubbing in Cracked Rotor System." Advanced Materials Research 44-46 (June 2008): 337–44. http://dx.doi.org/10.4028/www.scientific.net/amr.44-46.337.
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The rubbing phenomenon occurs when a rotating element eventually hits a stationary part of the rotating machinery. Increasing the rotor speed and decreasing the radial clearance between the rotating and the non-rotating parts can enhance the performance of the rotating machinery. This leads to an increased risk of rubbing contact. Rotor rubbing is the source of numerous different phenomena, for example sub- and super-harmonic vibrations, amplitude jumps and rotor instability. So the reliability analysis and sensitivity analysis of rotor system with rubbing is important for design purposes. Reliability analysis can help the designer to establish acceptable tolerance on rotor system. Sensitivity analysis can help the designer to know which problem in rotor system with rubbing is being solved and how the solution may affect the design of rotor system for system correction and reanalysis. On the basis of the dynamic equations of the cracked rotor system model and with consideration of the random parameters including shaft stiffness and damping, disk damping, radial clearance and stator radial stiffness, the random responses of cracked rotor system are researched. The reliability and sensitivity analysis of the cracked rotor system with rubbing are studied. According to the discretization of random process and stress-strength interference theory, the transient reliability model of cracked rotor system with rubbing is proposed. The reliability for rubbing in cracked rotor system is obtained by way of statistical fourth moment method, Edgeworth series technique and first passage theory. Numerical results are also presented and discussed.
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Cui, Bao Zhen, Ze Bing Wang, and Hong Xia Pan. "The Study of Local Wave Noise Reduction Based on the Correlation Analysis." Advanced Materials Research 588-589 (November 2012): 707–10. http://dx.doi.org/10.4028/www.scientific.net/amr.588-589.707.
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The signal can be divided into a number of intrinsic mode components (IMF) through local wave decomposition. The decomposition process is equivalent to the adaptive filter for signal. The frequencies of each IMF have reduced with the decomposition order. This paper established judgment criteria of signal, noise and pseudo component based on the local wave decomposition, used the characteristics of cross-correlation coefficient and autocorrelation sequence, combined with the power spectral density. The dominant mode function was extracted effectively and the final effects are used gear reducer in the JZQ250 of fault rolling bearing inner ring. It provides a new method for non-stationary signal adaptive noise reduction and fault diagnosis of rotating machinery.
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Ho, Siu Ki, Harish Chandra Nedunuri, Wamadeva Balachandran, Jamil Kanfoud, and Tat-Hean Gan. "Monitoring of Industrial Machine Using a Novel Blind Feature Extraction Approach." Applied Sciences 11, no. 13 (June 22, 2021): 5792. http://dx.doi.org/10.3390/app11135792.
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Machinery with several rotating and stationary components tends to produce non-stationary and random vibration signatures due to the fluctuations in the input loads and process defects due to long hours of operation. Traditional heuristics methods are suitable for the detection of fault signatures, however, they become more complicated when the level of uncertainty or randomness exceeds beyond control. A novel methodology to identify these fault signatures using optimal filtering of vibration data is proposed to eliminate any false alarms and is expected to provide a higher probability of correct diagnosis. In this paper, a detailed pipeline of the algorithms are presented along with the results of the investigation that was carried out. These investigations are performed using open-source vibration data published by the NASA prognostics centre. The performance of these algorithms are evaluated based on the ground truth results published by NASA researchers. Based on the performance of these algorithms several parameters are fine-tuned to ensure generalisation and reliable performance.
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Ma, Jie, Shule Li, and Xinyu Wang. "Condition Monitoring of Rolling Bearing Based on Multi-Order FRFT and SSA-DBN." Symmetry 14, no. 2 (February 4, 2022): 320. http://dx.doi.org/10.3390/sym14020320.
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Owing to the symmetry of the rolling bearing structure and the rotating operation mode, the rolling bearing works in a complex environment. It is very easy to be submerged by noise and misdiagnosis. For the non-stationary signal in variable speed state, this paper presents a condition monitoring method based on deep belief network (DBN) optimized by multi-order fractional Fourier transform (FRFT) and sparrow search algorithm (SSA). Firstly, the fractional Fourier transform based on curve feature segmentation is used to filter the fault vibration signal and extract the fault feature frequency. Then, the fault features are input into the SSA-DBN model for training, and the bearing fault features are classified, identified, and diagnosed. Finally, the rotating machinery fault simulator in the laboratory of Ottawa University is taken as the practical application object to verify the effectiveness of the method. The experimental results show that the proposed method has higher recognition accuracy and stronger stability.
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An, Xueli, and Fei Zhang. "Pedestal looseness fault diagnosis in a rotating machine based on variational mode decomposition." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 231, no. 13 (March 9, 2016): 2493–502. http://dx.doi.org/10.1177/0954406216637378.
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According to the non-stationary characteristic of rotating machinery vibration signals of a rotor system with a loose pedestal fault, variational mode decomposition was applied in the pedestal looseness fault diagnosis for such a rotor system. Variational mode decomposition is used to decompose the rotor vibration signal into several stable components. This can achieve the separation of the pedestal looseness fault signal from the background signals, and extract the fault characteristic of a vibration signal from a rotor system with pedestal looseness. Experimental data from a rotor system with pedestal looseness were used to verify the proposed method. The results showed that the stable components of the rotor vibration signal obtained by variational mode decomposition have obvious amplitude modulation characteristics. The components which contain fault information were analyzed by envelope demodulation, which can extract the pedestal looseness fault features of a rotor vibration signal. Therefore, the variational mode decomposition method can be effectively applied to the pedestal looseness fault diagnosis of such a rotor system.
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Liu, Chang Liang, Xiu Mei Huang, and Xian Jin Luo. "Roller Bearing Fault Diagnosis Based on ELMD and Fuzzy C-Means Clustering Algorithm." Applied Mechanics and Materials 602-605 (August 2014): 1698–700. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.1698.
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For the non-stationary characteristics of rotating machinery fault vibration signal, proposed a fault diagnosis method that based on ensemble local mean decomposition (ELMD) to extract fault feature, and fuzzy C-means clustering (FCM) to perform the fault identification. ELMD method can effectively solve the problem of aliasing modes in LMD. Firstly, decomposing the fault vibration signal by ELMD, PF components were obtained in which the initial feature vector matrix, The PF components compose a initial feature vector matrix, and do singular value decomposition, using the singular value decomposition feature vector as the fault characteristic vectors. Finally, using FCM clustering as a fault classifier. Achieved the identification of different fault types. Experimental results show that this method can effectively achieve the bearing fault diagnosis.
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Merainani, Boualem, Chemseddine Rahmoune, Djamel Benazzouz, and Belkacem Ould-Bouamama. "A novel gearbox fault feature extraction and classification using Hilbert empirical wavelet transform, singular value decomposition, and SOM neural network." Journal of Vibration and Control 24, no. 12 (February 1, 2017): 2512–31. http://dx.doi.org/10.1177/1077546316688991.
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There are growing demands for condition monitoring and fault diagnosis of rotating machinery to lower unscheduled breakdown. Gearboxes are one of the fundamental components of rotating machinery; their faults identification and classification always draw a lot of attention. However, non-stationary vibration signals and low energy of weak faults makes this task challenging in many cases. Thus, a new fault diagnosis method which combines the Hilbert empirical wavelet transform (HEWT), singular value decomposition (SVD), and self-organizing feature map (SOM) neural network is proposed in this paper. HEWT, a new self-adaptive time-frequency analysis was applied to the vibration signals to obtain the instantaneous amplitude matrices. Then, the singular value vectors, as the fault feature vectors were acquired by applying the SVD. Last, the SOM was used for automatic gearbox fault identification and classification. An electromechanical model comprising an induction motor coupled with a single stage spur gearbox is considered where the vibration signals of four typical operation modes were simulated. The conditions include the healthy gearbox, input shaft slant crack, tooth cracking, and tooth surface pitting. Obtained results show that the proposed method effectively identifies the gearbox faults at an early stage and realizes automatic fault diagnosis. Moreover, performance evaluation and comparison between the proposed HEWT–SVD method and Hilbert–Huang transform (HHT)–SVD approach show that the HEWT–SVD is better for feature extraction.
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Sheng, Jinlu, Shaojiang Dong, and Zhu Liu. "Bearing fault diagnosis based on intrinsic time-scale decomposition and improved Support vector machine model." Journal of Vibroengineering 18, no. 2 (March 31, 2016): 849–59. http://dx.doi.org/10.21595/jve.2015.16246.
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In order to achieve the bearing fault diagnosis so as to ensure the steadiness of rotating machinery. This article proposed a model based on intrinsic time-scale decomposition (ITD) and improved support vector machine method (ISVM), so as to deal with the non-stationary and nonlinear characteristics of bearing vibration signals. Firstly, the feature extraction method intrinsic time-scale decomposition (ITD) is used and the energy entropy are extracted so as to process the vibration signal in this paper. Then, the local tangent space alignment (LTSA) method is introduced to extract the characteristic features and reduce the dimension of the selected entropy features. Finally, the features are used to train the ISVM model as to classify bearings defects. Cases of actual were analyzed. The results validate the effectiveness of the proposed algorithm.
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Gao, Kangping, Xinxin Xu, Jiabo Li, Shengjie Jiao, and Ning Shi. "Application of multi-layer denoising based on ensemble empirical mode decomposition in extraction of fault feature of rotating machinery." PLOS ONE 16, no. 7 (July 19, 2021): e0254747. http://dx.doi.org/10.1371/journal.pone.0254747.
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Aiming at the problem that the weak features of non-stationary vibration signals are difficult to extract under strong background noise, a multi-layer noise reduction method based on ensemble empirical mode decomposition (EEMD) is proposed. First, the original vibration signal is decomposed by EEMD, and the main intrinsic modal components (IMF) are selected using comprehensive evaluation indicators; the second layer of filtering uses wavelet threshold denoising (WTD) to process the main IMF components. Finally, the virtual noise channel is introduced, and FastICA is used to de-noise and unmix the IMF components processed by the WTD. Next, perform spectral analysis on the separated useful signals to highlight the fault frequency. The feasibility of the proposed method is verified by simulation, and it is applied to the extraction of weak signals of faulty bearings and worn polycrystalline diamond compact bits. The analysis of vibration signals shows that this method can efficiently extract weak fault characteristic information of rotating machinery.
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Lu, Chen, Qian Sun, Laifa Tao, Hongmei Liu, and Chuan Lu. "Bearing Health Assessment Based on Chaotic Characteristics." Shock and Vibration 20, no. 3 (2013): 519–30. http://dx.doi.org/10.1155/2013/645308.
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Vibration signals extracted from rotating parts of machinery carry a lot of useful information about the condition of operating machine. Due to the strong non-linear, complex and non-stationary characteristics of vibration signals from working bearings, an accurate and reliable health assessment method for bearing is necessary. This paper proposes to utilize the selected chaotic characteristics of vibration signal for health assessment of a bearing by using self-organizing map (SOM). Both Grassberger-Procaccia algorithm and Takens' theory are employed to calculate the characteristic vector which includes three chaotic characteristics, such as correlation dimension, largest Lyapunov exponent and Kolmogorov entropy. After that, SOM is used to map the three corresponding characteristics into a confidence value (CV) which represents the health state of the bearing. Finally, a case study based on vibration datasets of a group of testing bearings was conducted to demonstrate that the proposed method can reliably assess the health state of bearing.
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Burdzik, Rafał, Łukasz Konieczny, and Piotr Folęga. "Structural Health Monitoring of Rotating Machines in Manufacturing Processes by Vibration Methods." Advanced Materials Research 1036 (October 2014): 642–47. http://dx.doi.org/10.4028/www.scientific.net/amr.1036.642.
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The paper presents results of the active diagnostics experiments on influence of fatigue metal damage of the inner race of bearing and unbalance of rotating masses on vibration generated by the machine. Analysis of vibration related phenomena is a solution commonly applied in Structural Health Monitoring (SHM) systems. The application of vibroacoustics methods for technical condition monitoring has been developed in the past years in many systems of manufacturing processes. Vibroacoustic methods, based on the analysis of vibration or acoustic signals perceived as residual processes of non-invasive nature, is becoming more and more important in this respect. The scope of its application as well as the applicability of methods in numerous diagnostic systems also results from the capabilities of advanced methods of signal analysis and identification of numerous characteristics of technical condition. One of the most common operation damages are caused by rolling bearings wear. The scope of research contains tests on bearing damage and the unbalance of disc. The wear processes and unbalance are closely related to the vibration levels (arising from the mass loss of plastic deformation, and the fatigue damage). The research was conducted on special research test bench for vibration monitoring for rotating machinery. Structural health monitoring of machinery has to be conducted in different states and working conditions of the manufacturing system. Thus for simulating of different operating conditions the experiments have been conducted during run up of the machine which consist the transient states of working and during work on constant rotational speed of the power generate engine. For the identification of the symptoms of the machinery and equipments health monitoring the vibration signal have been analysed in time domain and frequency transformation as well. The performed signals are not stationary. Thus it is better to observe the signal simultaneously in time and frequency domains. For this purpose the spectrograms were determined. Spectrograms computes the short-time Fourier transform of a signal by default divided into segments. During the transformation the Hamming window and noverlap were used. For the comparison of the vibration of good and damage bearings signals registered for different frequencies have been presented in form of spectrograms and RMS distributions.
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Hammami, A., A. Hmida, M. T. Khabou, F. Chaari, M. Haddar, and A. Felkaoui. "Applications of Ceemdan in Dynamic Behavior of Defected Spur Gearbox Running Under Acyclism Regime." Journal of Mechanics 36, no. 6 (June 29, 2020): 825–39. http://dx.doi.org/10.1017/jmech.2020.19.
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ABSTRACTEmpirical Mode Decomposition (EMD) and its approaches are powerful techniques in signal processing especially for the diagnosis of rotating machinery running in non-stationary regime. We are interested in this paper to the dynamic behavior of a defected one stage gearbox equipped with an elastic coupling and loaded under acyclism regime generated by a combustion engine. Actually, we adopt an approach to the EMD method called Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) as a technique to perform the diagnosis of the studied system. Since the obtained signals are modulated, all obtained Intrinsic Mode Functions (IMFs) are modulated and are processed and shown by the Wigner-Ville distributions (WVD) as well as the spectrum of their envelope in order to detect defects such as cracked tooth defect in the wheel of the spur gearbox and eccentricity defect in the gear.
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Kasim, Nur Adilla, Mohd Ghafran Mohamed, and Mohd Zaki Nuawi. "Non-Stationary Vibratory Signatures Bearing Fault Detection Using Alternative Novel Kurtosis-based Statistical Analysis." Journal of Applied Science & Process Engineering 9, no. 1 (April 30, 2022): 1139–48. http://dx.doi.org/10.33736/jaspe.4594.2022.
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Vibration signature-based analysis to detect and diagnose is the commonly used technique in the monitoring of rotating machinery. Reliable features will determine the efficacy of diagnosis and prognosis results in the field of machine condition monitoring. This study intends to produce a reliable set of signal features through an alternative statistical characteristic before available relevant prediction methods. Given the above advantage of Kurtosis, a newly formed feature extraction analysis is adapted to extract a single coefficient out of EMD-based pre-processing vibration signal data for bearing fault detection monitoring. Each set of IMFs data is analyzed using the Z-rotation method to extract the data coefficient. Afterwards, the Z-rot coefficients, RZ are presented on the base of the specification of the defect vibratory signal to observe which IMF data set has the highest correlation over the specification given. Throughout the analysis studies, the RZ shows some significant non-linearity in the measured impact. For that reason, the Z-rotation method has effectively determined the strong correlation that existed in some of the IMFs components of the bearing fault. It corresponds to the first IMF for the inner race and the rolling ball specified a strong RZ coefficient with the highest correlation coefficient of R2 = 0.9653 (1750 rpm) and R2 = 0.9518 (1772 rpm), respectively. Whereas, the 4th IMF decomposition for the outer race bearing fault scored is R2 = 0.8865 (1772 rpm). Meanwhile, the average R-squared score in the correlation between RZ coefficient and bearing fault throughout the study is R2 = 0.8915. Thus, it can be utilized to be the alternative feature extraction findings for monitoring bearing conditions.
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Miao, Feng, Rongzhen Zhao, Leilei Jia, and Xianli Wang. "Fault Diagnosis of Rotating Machinery Based on Multi-Sensor Signals and Median Filter Second-Order Blind Identification (MF-SOBI)." Applied Sciences 10, no. 11 (May 28, 2020): 3735. http://dx.doi.org/10.3390/app10113735.
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Feature extraction plays a crucial role in the diagnosis of rotating machinery faults. However, the vibration signals measured are inherently complex and non-stationary and the features of faulty signals are often submerged by noise. The principle and method of blind source separation are introduced, and we point out that the blind source separation algorithm is invalid in an environment of strong impulse noise. In order to solve the problem of fast separation of multi-sensor signals in an environment of strong impulse noise, first, the window width of the median filter (MF) is calculated according to the sampling frequency, so that the impulse noise and part of the white noise can be effectively filtered out. Next, the filtered signals are separated by the improved second-order blind identification (SOBI) algorithm. At the same time, the method is tested on the strong pulse background noise and rub impact dataset. The results show that this method has higher efficiency and accuracy than the direct separation method. It is possible to apply the method to real-time signal analysis due to its speed and efficiency.
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Kumar, Ashutosh, Prabhakar Sathujoda, and Neelanchali Asija Bhalla. "Vibration signal analysis of a rotor-bearing system through wavelet transform and empirical mode decomposition." IOP Conference Series: Materials Science and Engineering 1248, no. 1 (July 1, 2022): 012027. http://dx.doi.org/10.1088/1757-899x/1248/1/012027.
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Abstract Vibration analysis is widely used for the monitoring of the health of rotating machinery. There are different methods to interpret the vibration signals like time-domain analysis and frequency domain analysis, where the conventional Fast Fourier Transform (FFT) method is applied. FFT has been used successfully to extract stationary parameters from the frequency domain data. Complex machines normally consist of many parts and their vibration response contains many non-stationary signals and nonlinear signals. The objective of this research is to explore the feasibility of utilizing the wavelet transform (WT) and empirical mode decomposition (EMD) to efficiently decompose the sophisticated vibration signals of a rotor-bearing system into a finite number of intrinsic mode functions so that the fault characteristics of the rotor-bearing system can be analysed. A test rig of a rotor-bearing system was used to perform the experiments, and the vibration signals were recorded through NI-DAQ system. Vibration signals received from the test rig were analyzed using MATLAB software to present the useful information. The analysis result showed that the proposed approach is capable of diagnosing the faults of the rotor-bearing system.
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Vacula, Jiří, and Pavel Novotný. "Identification of Aerodynamic Tonal Noise Sources of a Centrifugal Compressor of a Turbocharger for Large Stationary Engines." Applied Sciences 13, no. 10 (May 12, 2023): 5964. http://dx.doi.org/10.3390/app13105964.
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The aerodynamics of centrifugal compressors is a topical issue, as the vibrations and noise reduce the comfort of people who are in proximity to the compressor. The current trend in rotating machinery research is therefore not only concerned with performance parameters but also increasingly with the effect on humans. An analysis of aerodynamic noise based on external acoustic field measurements may be a way to assess the nature of aerodynamic excitation. In this research, the experimental measurements at 20 operating points covered the entire characteristic operating range of the selected centrifugal compressor. The dominant noise arising at blade-passing frequency (BPF) was identified at all the operational points, and the dominant effect of the buzz-saw noise was identified at the maximum rotor speed. The determination of the total sound pressure level LPA showed a trend towards an increasingly higher rotor speed and compressor surge line. In the amplitude-frequency characteristics, the sound pressure was found to be dependent on the rotor speed for BPF. On the other hand, non-monotonicity was detected between the operational points at given speed lines, confirming the complexity of the aerodynamics of rotating machines. The metric chosen to identify prominent tones determined by the tonality of individual tones in the frequency spectrum showed a clear effect of integer multiples of the rotational frequency on the overall noise. Thus, the results presented here confirm the dominant influence of BPF in terms of the psychoacoustic impact on humans.
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Pei, Mochao, Hongru Li, and He Yu. "Degradation State Identification for Hydraulic Pumps Based on Multi-scale Ternary Dynamic Analysis, NSGA-II and SVM." Measurement Science Review 21, no. 3 (June 1, 2021): 82–92. http://dx.doi.org/10.2478/msr-2021-0012.
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Abstract Degradation state identification for hydraulic pumps is crucial to ensure system performance. As an important step, feature extraction has always been challenging. The non-stationary and non-Gaussian characteristics of the vibration signal are likely to weaken the performance of traditional features. In this paper, an efficient feature extraction algorithm named multi-scale ternary dynamic analysis (MTDA) is proposed. MTDA reconstructs the phase space based on the given signal and converts each embedding vector into a ternary pattern independently, which enhances its capacity of describing the details of non-stationary signals. State entropy (SE) and state transition entropy (STE) are calculated to estimate the dynamical changes and complexity of each signal sample. The excellent performance of SE and STE in detecting frequency changes, amplitude changes, and the development process of fault is verified with the use of four simulated signals. The proposed multi-scale analysis enables them to provide a more precise estimation of entropy. Furthermore, support vector machine (SVM) and nondominated sorting genetic algorithm II (NSGA-II) are introduced to conduct feature selection and state identification. NSGA-II and SVM can conduct the joint optimization of these two goals. The details of the method proposed in this paper are tested using simulated signals and experimental data, and some studies related to the fault diagnosis of rotating machinery are compared with our method. All the results show that our proposed method has better performance, which obtains higher recognition accuracy and lower feature set dimension.
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Hernández-Muriel, José Alberto, Jhon Bryan Bermeo-Ulloa, Mauricio Holguin-Londoño, Andrés Marino Álvarez-Meza, and Álvaro Angel Orozco-Gutiérrez. "Bearing Health Monitoring Using Relief-F-Based Feature Relevance Analysis and HMM." Applied Sciences 10, no. 15 (July 28, 2020): 5170. http://dx.doi.org/10.3390/app10155170.
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Nowadays, bearings installed in industrial electric motors are constituted as the primary mode of a failure affecting the global energy consumption. Since industries’ energy demand has a growing tendency, interest for efficient maintenance in electric motors is decisive. Vibration signals from bearings are employed commonly as a non-invasive approach to support fault diagnosis and severity evaluation of rotating machinery. However, vibration-based diagnosis poses a challenge concerning the signal properties, e.g., highly dynamic and non-stationary. Here, we introduce a knowledge-based tool to analyze multiple health conditions in bearings. Our approach includes a stochastic feature selection method, termed Stochastic Feature Selection (SFS), highlighting and interpreting relevant multi-domain attributes (time, frequency, and time–frequency) related to the bearing faults discriminability. In particular, a relief-F-based ranking and a Hidden Markov Model are trained under a windowing scheme to achieve our SFS. Obtained results in a public database demonstrate that our proposal is competitive compared to state-of-the-art algorithms concerning both the number of features selected and the classification accuracy.
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Yuan, Zonghao, Zengqiang Ma, Xin Li, Dayong Gao, and Zhipeng Fu. "Bearing fault diagnosis using a speed-adaptive network based on vibro-speed data fusion and majority voting." Measurement Science and Technology 33, no. 5 (February 17, 2022): 055112. http://dx.doi.org/10.1088/1361-6501/ac46ee.
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Abstract Fault diagnosis of rolling bearings is key to maintain and repair modern rotating machinery. Rolling bearings are usually working in non-stationary conditions with time-varying loads and speeds. Existing diagnosis methods based on vibration signals only do not have the ability to adapt to rotational speed. And when the load changes, their accuracy rate will be obviously reduced. A method is put forward which fuses multi-modal sensor signals to fit speed information. Firstly, the features are extracted from raw vibration signals and instantaneous rotating speed signals, and fused by 1D-convolution neural network-based networks. Secondly, to improve the robustness of the model when the load changes, a majority voting mechanism is proposed in the diagnosis stage. Lastly, multiple variable speed samples of four bearings under three loads are obtained to evaluate the performance of the proposed method by analyzing the loss function, accuracy rate and F 1 score under different variable speed samples. It is empirically found that the proposed method achieves higher diagnostic accuracy and speed-adaptive ability than the algorithms based on vibration signal only. Moreover, a couple of ablation studies are also conducted to investigate the inner mechanism of the proposed speed-adaptive network.
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Xiaoli, Xu, Jiang Zhanglei, Wang Hongjun, Wu Guoxin, Zuo Yunbo, Chen Peng, and Wang Liyong. "Application of the state deterioration evolution based on bi-spectrum in wind turbine." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 228, no. 11 (November 13, 2013): 1958–67. http://dx.doi.org/10.1177/0954406213511964.
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Concerning the problem of large rotating machinery like wind turbine which runs in low speed and non-stationary state, this research mainly focuses on separating fault trend feature from non-fault feature and the method of state deterioration evolution based on bi-spectrum. Firstly, the experimental signal such as low-speed startup vibration signal of rotor test rig in the normal state and a plurality of unbalanced state have been collected. Bi-spectrum method is applied to extract fault feature which submerged in complex background noise. On the basis of bi-spectrum analysis, the fault feature evolutionary matrix is defined to represent the state of equipment deterioration. The eigenvalues of fault feature evolutionary matrix are computed and fitted to a normal distribution curve, from which the mean value and variance are taken as fault feature parameters. Fault feature parameters are verified effectively by experiments. Finally, depending on fault feature parameters, graphical representation of state deterioration evolution is established. It is beneficial to provide guidelines for equipment deterioration trend. This method is applied to analyze the real vibration signal of wind turbine with the type of WD646/600 KW, and actual equipment condition verified the effectiveness of the proposed method.
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Gryllias, K., H. Andre, Q. Leclere, and J. Antoni. "Condition monitoring of rotating machinery under varying operating conditions based on Cyclo-Non-Stationary Indicators and a multi-order probabilistic approach for Instantaneous Angular Speed tracking." IFAC-PapersOnLine 50, no. 1 (July 2017): 4708–13. http://dx.doi.org/10.1016/j.ifacol.2017.08.857.
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Yang, Chuanlei, Hechun Wang, Zhanbin Gao, and Xinjie Cui. "Improving rolling bearing online fault diagnostic performance based on multi-dimensional characteristics." Royal Society Open Science 5, no. 5 (May 2018): 180066. http://dx.doi.org/10.1098/rsos.180066.
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As the main cause of failure and damage to rotating machinery, rolling bearing failure can result in huge economic losses. As the rolling bearing vibration signal is nonlinear and has non-stationary characteristics, the health status information distributed in the rolling bearing vibration signal is complex. Using common time-domain or frequency-domain approaches cannot easily enable an accurate assessment of rolling bearing health. In this paper, a novel rolling bearing fault diagnostic method based on multi-dimensional characteristics was developed to meet the requirements for accurate diagnosis of different fault types and severities with real-time computational performance. First, a multi-dimensional feature extraction algorithm based on entropy characteristics, Holder coefficient characteristics and improved generalized fractal box-counting dimension characteristics was performed to extract the health status feature vectors from the bearing vibration signals. Second, a grey relation algorithm was employed to achieve bearing fault pattern recognition intelligently using the extracted multi-dimensional feature vector. This experimental study has illustrated that the proposed method can effectively recognize different fault types and severities after integration of the improved fractal box-counting dimension into the multi-dimensional characteristics, in comparison with existing pattern recognition methods.
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Deng, Wu, Hailong Liu, Shengjie Zhang, Haodong Liu, Huimin Zhao, and Jinzhao Wu. "Research on an Adaptive Variational Mode Decomposition with Double Thresholds for Feature Extraction." Symmetry 10, no. 12 (December 1, 2018): 684. http://dx.doi.org/10.3390/sym10120684.
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A motor bearing system is a nonlinear dynamics system with nonlinear support stiffness. It is an asymmetry system, which plays an extremely important role in rotating machinery. In this paper, a center frequency method of double thresholds is proposed to improve the variational mode decomposition (VMD) method, then an adaptive VMD (called DTCFVMD) method is obtained to extract the fault feature. In the DTCFVMD method, a center frequency method of double thresholds is a symmetry method, which is used to determine the decomposed mode number of VMD according to the power spectrum of the signal. The proposed DTCFVMD method is used to decompose the nonlinear and non-stationary vibration signals of motor bearing in order to obtain a series of intrinsic mode functions (IMFs) under different scales. Then, the Hilbert transform is used to analyze the envelope of each mode component and calculate the power spectrum of each mode component. Finally, the power spectrum is used to extract the fault feature frequency for determining the fault type of the motor bearing. To test and verify the effectiveness of the DTCFVMD method, the actual fault vibration signal of the motor bearing is selected in here. The experimental results show that the center frequency method of double thresholds can effectively determine the mode number of the VMD method, and the proposed DTCFVMD method can accurately extract the clear time frequency characteristics of each mode component, and obtain the fault characteristics of characteristics; frequency, rotating frequency, and frequency doubling and so on.
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Su, Xuanyuan, Hongmei Liu, and Laifa Tao. "TF Entropy and RFE Based Diagnosis for Centrifugal Pumps Subject to the Limitation of Failure Samples." Applied Sciences 10, no. 8 (April 23, 2020): 2932. http://dx.doi.org/10.3390/app10082932.
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In practical engineering, the vibration-based fault diagnosis with few failure samples is gaining more and more attention from researchers, since it is generally hard to collect sufficient failure records of centrifugal pumps. In such circumstances, effective feature extraction becomes quite vital, since there may not be enough failure data to train an end-to-end classifier, like the deep neural network (DNN). Among the feature extraction, the entropy combined with signal decomposition algorithms is a powerful choice for fault diagnosis of rotating machinery, where the latter decomposes the non-stationary signal into multiple sequences and the former further measures their nonlinear characteristics. However, the existing entropy generally aims at processing the 1D sequence, which means that it cannot simultaneously extract the fault-related information from both the time and frequency domains. Once the sequence is not strictly stationary (hard to achieve in practices), the useful information will be inevitably lost due to the ignored domain, thus limiting its performance. To solve the above issue, a novel entropy method called time-frequency entropy (TfEn) is proposed to jointly measure the complexity and dynamic changes, by taking into account nonlinear behaviors of sequences from both dimensions of time and frequency, which can still fully extract the intrinsic fault features even if the sequence is not strictly stationary. Successively, in order to eliminate the redundant components and further improve the diagnostic accuracy, recursive feature elimination (RFE) is applied to select the optimal features, which has better interpretability and performance, with the help of the supervised embedding mechanism. To sum up, we propose a novel two-stage method to construct the fault representation for centrifugal pumps, which develops from the TfEn-based feature extraction and RFE-based feature selection. The experimental results using the real vibration data of centrifugal pumps show that, with extremely few failure samples, the proposed method respectively improves the average classification accuracy by 12.95% and 33.27%, compared with the mainstream entropy-based methods and the DNN-based ones, which reveals the advantage of our methodology.
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Lipinski, Piotr, Edyta Brzychczy, and Radoslaw Zimroz. "Decision Tree-Based Classification for Planetary Gearboxes’ Condition Monitoring with the Use of Vibration Data in Multidimensional Symptom Space." Sensors 20, no. 21 (October 22, 2020): 5979. http://dx.doi.org/10.3390/s20215979.
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Monitoring the condition of rotating machinery, especially planetary gearboxes, is a challenging problem. In most of the available approaches, diagnostic procedures are related to advanced signal pre-processing/feature extraction methods or advanced data (features) analysis by using artificial intelligence. In this paper, the second approach is explored, so an application of decision trees for the classification of spectral-based 15D vectors of diagnostic data is proposed. The novelty of this paper is that by a combination of spectral analysis and the application of decision trees to a set of spectral features, we are able to take advantage of the multidimensionality of diagnostic data and classify/recognize the gearbox condition almost faultlessly even in non-stationary operating conditions. The diagnostics of time-varying systems are a complicated issue due to time-varying probability densities estimated for features. Using multidimensional data instead of an aggregated 1D feature, it is possible to improve the efficiency of diagnostics. It can be underlined that in comparison to previous work related to the same data, where the aggregated 1D variable was used, the efficiency of the proposed approach is around 99% (ca. 19% better). We tested several algorithms: classification and regression trees with the Gini index and entropy, as well as the random tree. We compare the obtained results with the K-nearest neighbors classification algorithm and meta-classifiers, namely: random forest and AdaBoost. As a result, we created the decision tree model with 99.74% classification accuracy on the test dataset.
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Liu, Xuejun, Wei Sun, Hongkun Li, Zeeshan Hussain, and Aiqiang Liu. "The Method of Rolling Bearing Fault Diagnosis Based on Multi-Domain Supervised Learning of Convolution Neural Network." Energies 15, no. 13 (June 23, 2022): 4614. http://dx.doi.org/10.3390/en15134614.
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The rolling bearing is a critical part of rotating machinery and its condition determines the performance of industrial equipment; it is necessary to detect rolling bearing faults as early as possible. The traditional methods of fault diagnosis are not efficient and are time-consuming. With the help of deep learning, the convolution neural network (CNN) plays a huge role in the data-driven methods of bearing fault diagnosis. However, the vibration signal is non-stationary, contains high noise, and is one-dimensional, which is difficult to analyze directly by the CNN model. Considering the multi-domain learning as an advantage of deep learning, this paper proposes a novel rolling bearing fault diagnosis approach using an improved one-dimensional (1D) and two-dimensional (2D) convolution neural network (CNN) of two-domain information learning. The constructed fault diagnosis model combining 1D and 2D CNN extracts the fault features from the two-domain information of bearing fault samples. The padding and dropout technology are utilized to fully extract features from the raw data and reduce over-fitting. To prove the validity of the proposed method, this paper performs two tests with two bearing datasets, the Case Western Reserve University (CWRU) bearing dataset and the Dalian University of Technology (DUT) vibration laboratory dataset. The experimental results show that our proposed method achieves high recognition accuracy of rolling bearing fault states via two-domain learning of monitoring data, and there is no manual experience necessary. Vibration data under strong noise were also used to test the method, and the results show the superiority and robustness of the proposed method.
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Snitynskyy, Volodymyr, Volodymyr Burtak, Bohdan Diveyev, Orest Horbay, Ruslan Humenyuk, and Ivan Kernytskyy. "Dynamic properties of screw-bolts connections of sowing machine." Przegląd Naukowy Inżynieria i Kształtowanie Środowiska 28, no. 4 (December 29, 2019): 584–93. http://dx.doi.org/10.22630/pniks.2019.28.4.53.
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A vehicle is a complicated system under the influence of vibration caused by an inequality of the road surface, variable speed, unbalance of the rotating elements. The main factors influencing the relaxation of threaded connections (TC) are the amplitude, frequency and gradient of vibration. Although the frequencies of these oscillations are distributed over a wide range, the general effects of dynamic loading on bolted connections are similar. Main effects: (1) loosening the nut/bolt and (2) failure due to fatigue failure. The analysis of the technological process of agricultural machinery shows that the main external factors influencing their work are the profile of the surface of the field, the hardness and moisture of the soil, the speed of the unit, the instability of the engine, the traction of the wheels of the tractor and others. To study the integrity of TC, which is tested on the stand, consider the design scheme of nonlinear oscillations of the design in the presence of gaps in the TC. The study was conducted in two modes of movement of the drill: with tightened bolts and weakened bolts. For the survey, the method of spectral analysis of multidimensional periodically non-stationary random signals was used. In the process of testing, the dynamic loading of bolted joints installed on the respective knots and components of the drill was evaluated. From the conducted research it follows that the maximum vibrations acting on the TC of the drill may be in the vicinity of high-frequency resonances of TC. In parallel, nonlinear mathematical models of the oscillations of the seeder and the weakened TC were developed. The theoretical results qualitatively correspond to the experimental data.
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Mauricio, Alexandre, Dustin M. 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." Journal of Engineering for Gas Turbines and Power, January 16, 2021. http://dx.doi.org/10.1115/1.4049778.
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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. In complex machines the failure indications of an early bearing damage are weak compared to other sources of excitations. Vibration analysis is most widely used and various methods have been proposed. 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. Recently an emerging interest has been focused on modelling rotating machinery signals as cyclostationary. Classical cyclostationary tools, such as Cyclic Spectral Correlation Density (CSCD) and Cyclic Modulation Spectrum (CMS), can be used to extract information about the cyclic behavior of cyclostationary signals, under the assumption of nearly constant rotating speed. 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 cyclononstationary signals 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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Huang, Jie, Xiaolong Cui, Chaoshun Li, Zhihuai Xiao, and Qiming Chen. "Multivariate time-varying complex signal processing framework and its application in rotating machinery rotor bearing system." Measurement Science and Technology, September 13, 2022. http://dx.doi.org/10.1088/1361-6501/ac919b.
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Abstract Affected by multi-field coupling factors, the vibration response of rotating machinery similar to the hydro-generator unit often exhibits strong time-varying frequency components, which makes rotor fault detection more challenging. The fusion analysis of the vibration signals of multiple bearing sections of the rotor has been proved to be a very effective method for rotor vibration fault diagnosis. However, how to more accurately and synchronously extract the instantaneous features of rotor non-stationary vibration signals associated with multiple sections has been unresolved. To this end, a framework for multivariate time-varying complex signal decomposition of the rotor-bearing system (RBS) is proposed, namely MCNCMD. First, the decomposition of multivariate time-varying complex signals is realized by two-stage processing. Second, instantaneous orbit features (IOFs) are obtained through the proposed framework. Finally, a three-dimensional instantaneous orbit map (3D-IOM) reflecting the time-varying process is constructed through the IOFs. The framework not only realizes the decomposition of the multi-channel time-varying complex signals of the rotor but also simultaneously extracts the instantaneous features of the multi-channel signals. In addition, it also realizes the description of the instantaneous vibration state of the RBS in the non-stationary process (such as startup and shutdown). Simulation experiments show that the framework is superior to other multi-channel signal processing methods in processing time-varying complex signals. The results based on field-measured signals show that the framework can guide the real-time analysis of the signals generated by rotating machinery, which improves the intuition of condition monitoring.
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Ma, Meng, and Zhu Mao. "Deep wavelet sequence-based gated recurrent units for the prognosis of rotating machinery." Structural Health Monitoring, July 2, 2020, 147592172093315. http://dx.doi.org/10.1177/1475921720933155.
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Prognostics and health management (PHM) is an emerging technique which aims to improve the reliability and safety of machinery systems. Remaining useful life (RUL) prediction is the key part of PHM which provides operators how long the machine keeps working without breakdowns. In this study, a novel prognostic model is proposed for RUL prediction using deep wavelet sequence-based gated recurrent units (GRU). This proposed wavelet sequence-based gated recurrent unit (WSGRU) specifically adopts a wavelet layer and generates wavelet sequences at different scales. Since vibration signals exhibit non-stationary characteristics, wavelet analysis is thereby needed to capture both the time and frequency domain information to fully identify the degradation of the rotating components. In the proposed WSGRU, the vibration signals are decomposed into different frequency sub-bands via wavelet transformation, and then a deep GRU architecture is designed to predict the RUL taking advantage of the temporal dependencies that naturally exist in the waveforms. Experimental studies have been performed for RUL prediction of bearings with collection of vibration signals during the run-to-failure tests. The prediction results show that deep WSGRU outperforms traditional models due to the multi-level feature extraction on the transformed multiscale wavelet sequences.
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Wang, Jinrui, Shanshan Ji, Baokun Han, and Huaiqian Bao. "Intelligent fault diagnosis for rotating machinery using L1/2-SF under variable rotational speed." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, October 22, 2020, 095440702096462. http://dx.doi.org/10.1177/0954407020964625.
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Sparse filtering (SF), as an effective feature extraction technique, has attracted considerable attention in the field of mechanical fault diagnosis. But the generalization ability of SF to handle non-stationary signal under variable rotational speed is still poor. When the rotating parts of mechanical transmission work at a constant speed, the collected vibration signal is strongly correlated with the fault type. However, the mappings will no longer be so simple under the condition of variable rotational speed, which brings a rigorous challenge to intelligent fault diagnosis. To overcome the aforementioned deficiency, a novel L1/2 regularized SF method ( L1/2-SF) is studied in this paper. Specifically, L1/2 regularization strategy is added to the cost function of SF, then the L1/2-SF is directly employed to extract sparse features from the raw vibration data under variable rotational speed condition. In order to understand the sparse feature extraction ability of the L1/2 regularization, a physical explanation of the sparse solution generated by the L1/2 regularization strategy is explored. Next, softmax regression is employed for fault classification connected with the output layer of L1/2-SF. The effectiveness of L1/2-SF method is verified using a planetary gearbox dataset and a bearing dataset, respectively. Experiment results show that L1/2-SF can deal well with the variable rotational speed problem and is superior to other methods.
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Wei, Kai, Xuwen Jing, Bingqiang Li, Chao Kang, Zhenhuan Dou, Jinfeng Liu, Yu Chen, and Hainan Zheng. "A combined generalized Warblet transform and second order synchroextracting transform for analyzing nonstationary signals of rotating machinery." Scientific Reports 11, no. 1 (August 20, 2021). http://dx.doi.org/10.1038/s41598-021-96343-2.
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AbstractIn recent years, considerable attention has been paid in time–frequency analysis (TFA) methods, which is an effective technology in processing the vibration signal of rotating machinery. However, TFA techniques are not sufficient to handle signals having a strong non-stationary characteristic. To overcome this drawback, taking short-time Fourier transform as a link, a TFA methods that using the generalized Warblet transform (GWT) in combination with the second order synchroextracting transform (SSET) is proposed in this study. Firstly, based on the GWT and SSET theories, this paper proposes a method combining the two TFA methods to improve the TFA concentration, named GWT–SSET. Secondly, the method is verified numerically with single-component and multi-component signals, respectively. Quantized indicators, Rényi entropy and mean relative error (MRE) are used to analyze the concentration of TFA and accuracy of instantly frequency (IF) estimation, respectively. Finally, the proposed method is applied to analyze nonstationary signals in variable speed. The numerical and experimental results illustrate the effectiveness of the GWT–SSET method.
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Inturi, Vamsi, Sai Venkatesh Balaji, Praharshitha Gyanam, Brahmini Priya Venkata Pragada, Sabareesh Geetha Rajasekharan, and Vikram Pakrashi. "An integrated condition monitoring scheme for health state identification of a multi-stage gearbox through Hurst exponent estimates." Structural Health Monitoring, May 26, 2022, 147592172210928. http://dx.doi.org/10.1177/14759217221092828.
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The vibration and acoustic signals collected from rotating machinery are often non-stationary and aperiodic, and it is a challenge to post-process and extract the defect sensitive health indicators. In this paper, we demonstrate how the estimated Hurst exponent of such measured data can be advantageous for analyzing non-stationary and aperiodic data due to its self-similarity and scale-invariance properties. To illustrate this, the paper demonstrates detection of fault diagnostics of a multi-stage gearbox operating under fluctuating speeds through estimated Hurst exponent of the raw vibration and acoustic signals as a health indicator. Thirteen health states of the gearbox are considered, and the raw vibration and acoustic signals are collected. The Hurst exponents are calculated using three different approaches: generalized Hurst exponent (q = 1, 2, 3, and 4), rescale range statistical (R/S) analysis, and dispersion analysis from the vibration and acoustic signals. Three different health indicator datasets are formulated and subjected to feature learning through conventional machine-learning (decision tree and support vector machine) and advanced machine-learning (deep-learning) classifiers. The effectiveness of these datasets while discriminating between the health states of the gearbox is investigated, yielding classification accuracies of 96.4% when compared with the individual health indicator datasets. The ability of the fault diagnosis and defect severity analysis with reduced reliance on the signal post-processing algorithms is demonstrated.
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"Signal Processing Methods for Identification of Induction Motor Bearing Fault." International Journal of Recent Technology and Engineering 8, no. 3 (September 30, 2019): 143–51. http://dx.doi.org/10.35940/ijrte.c3911.098319.
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To diagnose early faults as soon as possible, the feature extraction of vibration signals is very important in real engineering applications. Recently, the advanced signal processing-based weak feature extraction method has been becoming a hot research topic. The dominant mode of failure in rolling element bearings is spalling of the races or the rolling elements. Localized defects generate a series of impact vibrations every time whenever running roller passes over the surface of a defect. Therefore, vibration analysis is a conventional method for bearing fault detection. However, the measured vibration signals of rotating machinery often present nonlinear and non-stationary characteristics. This paper deals with the diagnosis of induction motor bearing based on vibration signal analysis. It provides a comparative study between traditional signal processing methods, such as Power Spectrum, Short Time Fourier Transform, Wavelet Transform, and Hilbert Transform. Performances of these techniques are assessed on real vibration data and compared for healthy and faulty bearing.
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Yadav, Dhananjay. "The effect of viscosity and Darcy number on the start of convective motion in a rotating porous medium layer saturated by a couple-stress fluid." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, July 16, 2020, 095440622094255. http://dx.doi.org/10.1177/0954406220942551.
Full textAbstract:
In chemical process industry, food process industry, centrifugal filtration processes, and rotating machinery, convective flows are characterized by rotation, where couple-stress fluid (a type of non-Newtonian fluid) with variable viscosity in a porous medium can act as a working fluid. In the present work, the combined effect of the temperature-dependent viscosity, the Darcy number and the uniform rotation on the arrival of convective motion in a couple-stress fluid saturated porous layer is examined applying linear stability concept. The outcome of the viscosity variation parameter Q, the rotation parameter [Formula: see text], the couple-stress parameter [Formula: see text], and the Darcy number [Formula: see text] on both stationary and oscillatory convections is investigated analytically and presented graphically in terms of the critical thermal Darcy–Rayleigh number [Formula: see text]. Below the critical value [Formula: see text], no convective motion arises in the considered system. It is recognized that the arrival of convective motion is oscillatory only if the rotation parameter [Formula: see text] surpasses a threshold value which in turn depends on other physical parameters. The impact of the viscosity variation parameter Q has a destabilizing influence, while the couple-stress parameter [Formula: see text], rotation parameter [Formula: see text], the Darcy number [Formula: see text], the Prandtl number ⪻, and the heat capacity ratio γ show stabilizing influences on the stability of arrangement.