Sparse classification of rotating machinery faults based on compressive sensing strategy

Tang, Gang; Yang, Qin; Wang, Huaqing; Luo, Ganggang; Ma, Jianwei

doi:10.1016/j.mechatronics.2015.04.006

Cited by 59 publications

(25 citation statements)

References 21 publications

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“…Cui et al [118] used the sparse decomposition to analyze the early-stage bearing fault via adaptive impulse dictionary construction. Tang et al [119] used the compressive sensing technique for dimensionality reduction, and then the sparse representation classification algorithm was applied for rotating machinery fault recognition. Li et al [120] used resonance-based sparse signal decomposition (RSSD) to decompose the signal into high resonance component and low resonance component.…”

Section: Sparse Representation Methodsmentioning

confidence: 99%

“…Lv et al [116] Atomic sparse decomposition + genetic algorithm Li et al [120] Resonance-based sparse signal decomposition + principal component analysis Tang et al [115] Shift-invariant sparse coding Mo et al [117] Delayed correlation envelope+ sparse decomposition Cui et al [118] Sparse decomposition + adaptive impulse dictionary Tang et al [119] Sparse representation + compressive sensing…”

Section: Authors Methodologiesmentioning

confidence: 99%

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A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery

Wei

et al. 2019

Entropy

171

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Rotating machinery is widely applied in various types of industrial applications. As a promising field for reliability of modern industrial systems, early fault diagnosis (EFD) techniques have attracted increasing attention from both academia and industry. EFD is critical to provide appropriate information for taking necessary maintenance actions and thereby prevent severe failures and reduce financial losses. A massive amounts of research work has been conducted in last two decades to develop EFD techniques. This paper reviews and summarizes the research works on EFD of gears, rotors, and bearings. The main purpose of this paper is to serve as a guidemap for researchers in the field of early fault diagnosis. After a brief introduction of early fault diagnosis techniques, the applications of EFD of rotating machine are reviewed in two aspects: fault frequency-based methods and artificial intelligence-based methods. Finally, a summary and some new research prospects are discussed.

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Section: Sparse Representation Methodsmentioning

confidence: 99%

Section: Authors Methodologiesmentioning

confidence: 99%

A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery

Wei

et al. 2019

Entropy

171

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“…Rolling element bearing (REB) is one of the most widely used elements in rotating machinery and sudden bearing failures may cause system outage [1]. Statistics show that faulty bearings contribute to about 30% of the failures in rotating machinery [2,3].…”

Section: Introductionmentioning

confidence: 99%

Peak-Based Mode Decomposition for Weak Fault Feature Enhancement and Detection of Rolling Element Bearing

Tang

He³

2020

Shock and Vibration

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Rolling element bearings are widely used in rotating machinery to support shafts, whose failures may affect the health of the whole system. However, strong noise interferences often make the bearing fault features submerged and difficult to be identified. Peak-based wavelet method is such a way to reduce certain noise and enhance the fault features by increasing the sparsity of monitored signals. But peak-based wavelet parameters need to be optimized due to the determined basis function and constant resolution, which will affect the efficiency of vibration signal analysis. To address these problems, a peak-based mode decomposition is proposed for weak bearing fault feature enhancement and detection. Firstly, to enhance the differences between repetitive transients and high-frequency noise, a peak-based piecewise recombination is used to convert the middle frequency parts into low-frequency ones. Then, the recombined signal is processed by empirical mode decomposition, combining with a criterion of cross-correlation coefficients and kurtosis. Subsequently, a backward peak transformation is performed to obtain the enhanced signal. Finally, the fault diagnosis is implemented by the squared envelope spectrum, whose normalized squared magnitude is used as a bearing fault indicator. The analysis results of the simulated signals and the experimental signals show that the proposed method can enhance and identify the weak repetitive transient features. The superiority of the proposed method for faint repetitive transient detection is also verified by comparing with the peak-based wavelet method.

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“…In the field of fault diagnosis, SRC is rarely studied. A typical application appeared with a good result in [17], where compressive sensing theory was implied to reduce the dimension of original vibration signals and SRC was used to classify the low-dimensional signals.…”

Section: Introductionmentioning

confidence: 99%

Transform-domain sparse representation based classification for machinery vibration signals

Yu¹,

Fan²,

Wang³

et al. 2018

J. vibroeng.

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The working state of machinery can be reflected by vibration signals. Accurate classification of these vibration signals is helpful for the machinery fault diagnosis. A novel classification method for vibration signals, named Transform Domain Sparse Representation-based Classification (TDSRC), is proposed. The method achieves high classification accuracy by three steps. Firstly, time-domain vibration signals, including training samples and test samples, are transformed to another domain, e.g. frequency-domain, wavelet-domain etc. Then, the transform coefficients of the training samples are combined as a dictionary and the transform coefficients of the test samples are sparsely coded on the dictionary. Finally, the class label of the test samples is identified by their minimal reconstruction errors. Although the proposed method is very similar to the Sparse Representation-based Classification (SRC), experimental results illustrates its performance is far superior to SRC in the classification of vibration signals. These experiments include: frequency-domain classification of bearing vibration data from the Case Western Reserve University (CWRU) Bearing Data Center and wavelet-domain classification of six fault-types gearbox vibration data from our rotating machinery experimental platform.

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Sparse classification of rotating machinery faults based on compressive sensing strategy

Cited by 59 publications

References 21 publications

A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery

A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery

Peak-Based Mode Decomposition for Weak Fault Feature Enhancement and Detection of Rolling Element Bearing

Transform-domain sparse representation based classification for machinery vibration signals

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