Bearing diagnostics under strong electromagnetic interference based on Integrated Spectral Coherence

Mauricio, Alexandre; Qi, Junyu; Smith, Wade A.; Sarazin, Mathieu; Randall, R.B.; Janssens, Karl; Gryllias, Konstantinos

doi:10.1016/j.ymssp.2020.106673

Cited by 60 publications

(33 citation statements)

References 14 publications

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“…Cyclic spectral correlation can be the considered as double FT of the signal, which depicts the relationship of the spectral frequency f and the cyclic frequency. In view of the deficiencies of conventional cyclic spectral analysis in data processing under noisy environment, a more effective tool called cyclic spectral coherence (CSCoh) includes extra frequency dimension, with the sensitive advantage of processing cyclostationary signals [94,95,96].…”

Section: Cyclic Spectral Coherencementioning

confidence: 99%

Data Preprocessing Techniques in Convolutional Neural Network Based on Fault Diagnosis Towards Rotating Machinery

2020

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Rotating machinery plays a critical role in many significant fields. However, the unpredictable machinery faults may lead to the severe damage and losses. Hence, it is of great value to explore the precise approaches for fault diagnosis. With the development of the intelligent fault diagnosis methods based on deep learning, convolutional neural network (CNN) has aroused the attention of researchers in machinery fault diagnosis. In the light of the reduction of difficulty in feature learning and the improvement of final diagnosis accuracy, data preprocessing is necessary and crucial in CNN-based fault diagnosis methods. This review focuses on CNN-based fault diagnosis approaches in rotating machinery. Firstly, data preprocessing methods are overviewed. Then, we emphatically analyze and discuss several main techniques applied in CNN-based intelligent diagnosis, principally including the fast Fourier transform, wavelet transform, data augmentation, S-transform, and cyclic spectral analysis. Finally, the potential challenges and research objects are prospected on data preprocessing in intelligent fault diagnosis of rotary machinery. INDEX TERMS Data preprocessing, convolutional neural network, intelligent fault diagnosis, rotating machinery.

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Section: Cyclic Spectral Coherencementioning

confidence: 99%

Data Preprocessing Techniques in Convolutional Neural Network Based on Fault Diagnosis Towards Rotating Machinery

2020

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“…By integrating CSCoh and envelope spectrum, Mauricio et al developed two methods to select the optimal frequency band for the bearing fault diagnosis: improved envelope spectrum by alpha maximization (IESAM) and improved envelope spectrum via feature optimization-gram (IESFOgram), respectively [72]. The raw time signal was transformed into bispectral map in both methods.…”

Section: Applications Of Cyclostationarity Theory In Fault Diagnosis Of Rotating Machinerymentioning

confidence: 99%

Cyclostationary Analysis towards Fault Diagnosis of Rotating Machinery

2020

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In the light of the significance of the rotating machinery and the possible severe losses resulted from its unexpected defects, it is vital and meaningful to exploit the effective and feasible diagnostic methods of its faults. Among them, the emphasis of the analysis approaches for fault type and severity is on the extraction of useful components in the fault features. On account of the common cyclostationarity of vibration signal under faulty states, fault diagnosis methods based on cyclostationary analysis play an essential role in the rotatory machine. Based on it, the fundamental definition and classification of cyclostationarity are introduced briefly. The mathematical principles of the essential cyclic spectral analysis are outlined. The significant applications of cyclostationary theory are highlighted in the fault diagnosis of the main rotating machinery, involving bearing, gear, and pump. Finally, the widely-used methods on the basis of cyclostationary theory are concluded, and the potential research directions are prospected.

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“…Therefore, in fact, we have two mentioned phenomena: varying load (due to varying amount of ore to classify and unbalance of the shaft) and impulsive load considered to be impulsive background noise. As discussed in research works published already [ 11 , 12 ], for this class of machines the problem of local damage detection is challenging and may be defined as the detection of cyclic impulsive signal in presence of non-cyclic impulsive noise [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

Identification, Decomposition and Segmentation of Impulsive Vibration Signals with Deterministic Components—A Sieving Screen Case Study

Gąsior

Urbańska

Grzesiek

et al. 2020

Sensors

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Condition monitoring is a well-established field of research; however, for industrial applications, one may find some challenges. They are mostly related to complex design, a specific process performed by the machine, time-varying load/speed conditions, and the presence of non-Gaussian noise. A procedure for vibration analysis from the sieving screen used in the raw material industry is proposed in the paper. It is more for pre-processing than the damage detection procedure. The idea presented here is related to identification and extraction of two main types of components: (i) deterministic (D)—related to the unbalanced shaft(s) and (ii) high amplitude, impulsive component randomly (R) appeared in the vibration due to pieces of ore falling down of moving along the deck. If we could identify these components, then we will be able to perform classical diagnostic procedures for local damage detection in rolling element bearing. As deterministic component may be AM/FM modulated and each impulse may appear with different amplitude and damping, there is a need for an automatic procedure. We propose a method for signal processing that covers two main steps: (a) related to R/D decomposition and including signal segmentation to neglect AM/FM modulations, iterative sine wave fitting using the least square method (for each segment), signal filtering technique by subtraction fitted sine from the raw signal, the definition of the criterion to stop iteration by residuals analysis, (b) impulse segmentation and description (beginning, end, max amplitude) that contains: detection of the number of impulses in a decomposed random part of the raw signal, detection of the max value of each impulse, statistical analysis (probability density function) of max value to find regime-switching), modeling of the envelope of each impulse for samples that protrude from the signal, extrapolation (forecasting) envelope shape for samples hidden in the signal. The procedure is explained using simulated and real data. Each step is very easy to implement and interpret thus the method may be used in practice in a commercial system.

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Bearing diagnostics under strong electromagnetic interference based on Integrated Spectral Coherence

Cited by 60 publications

References 14 publications

Data Preprocessing Techniques in Convolutional Neural Network Based on Fault Diagnosis Towards Rotating Machinery

Data Preprocessing Techniques in Convolutional Neural Network Based on Fault Diagnosis Towards Rotating Machinery

Cyclostationary Analysis towards Fault Diagnosis of Rotating Machinery

Identification, Decomposition and Segmentation of Impulsive Vibration Signals with Deterministic Components—A Sieving Screen Case Study

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