Intelligent fault diagnosis of rolling element bearings using sparse wavelet energy based on overcomplete DWT and basis pursuit

Wang, Cong; Gan, Meng; Zhu, Changan

doi:10.1007/s10845-015-1056-2

Cited by 40 publications

(18 citation statements)

References 47 publications

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“…Intelligent TFI identification mainly focuses on how to extract relevant TFI features. TFI auto recognition can be applied to many image global features, such as multiscale singular value [9], gray statistical characteristics [10], textural features [11], [12], and sparse wavelet energy [13]. The bearing fault impulses are very weak compared to harmonics…”

Section: Introductionmentioning

confidence: 99%

Bearing Fault Diagnosis Based on Subband Time-Frequency Texture Tensor

Lin

Liu

et al. 2019

IEEE Access

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The texture feature tensor established from a subband time-frequency image (TFI) was extracted and used to identify the fault states of a rolling bearing. The TFI of adaptive optimal-kernel distribution was optimally partitioned into TFI blocks based on the minimum frequency band entropy. The texture features were extracted from the co-occurrence matrix of each TFI block. Based on the order of the segmented frequency bands, the texture feature tensor was constructed using the multidimensional feature vectors from all the blocks; this preserved the inherent characteristic of the TFI structure and avoided the information loss caused by vectorizing multidimensional features. The linear support higher order tensor machine based on the feature tensor was applied to identify the fault states of the rolling bearing. INDEX TERMS Texture feature tensor, frequency band entropy, linear support higher-order tensor machine, bearing fault intelligent diagnosis.

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Section: Introductionmentioning

confidence: 99%

Bearing Fault Diagnosis Based on Subband Time-Frequency Texture Tensor

Lin

Liu

et al. 2019

IEEE Access

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“…Compared with the MP algorithm, though the BP algorithm has the advantage of relatively higher calculation precision, it needs a large amount of calculation time, so the BP algorithm is used relatively rarely in vibration signal processing of rotating machinery. The current paper explored a sparse time–frequency distribution method using over-complete discrete WT and the BP to characterize the periodic transient information embedded in rolling element bearings (Wang et al., 2017), and the simulation and experimental fault signals of a rolling element bearing were used to confirm the advantages and effectiveness of the explored method. In a paper by Qin et al.…”

Section: Introductionmentioning

confidence: 99%

The application of matching pursuit based on multi feature pattern set in the signal processing of rotating machinery

Wang

Sun

2019

Journal of Vibration and Control

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The vibration signal containing the fault feature is buried by the strong background noise usually (the noise is referring to the other signals not containing the fault feature in the general sense) when a fault arises in rotating machinery, so the collected vibration signal needs to be processed normally in order to get a correct diagnosis result. Sparse representation theory is a relatively new signal processing method and matching pursuit (MP) is the classical sparse representation method. However, in the traditional MP method only one kind of atom dictionary is constructed to match the fault feature hidden in the vibration signal, which will cause an unsatisfactory analysis result usually when several different kinds of components arise in the vibration signal synchronously. This paper proposes a novel fault feature strengthening method basing on MP to solve the above problem, and the proposed method constructs several different kinds of atom dictionary based on a multi feature pattern set to match the multi-components buried in the collected vibration signal of rotating machinery. Firstly, the effectiveness and advantages of the proposed method are verified by kinds of typical simulation signals of rotating machinery. Then the proposed method is extended to preprocess the vibration signals of bearing and gearbox, and the corresponding analysis results prove that the proposed method has obviously a better fault feature extraction effect on the vibration signal of rotating machinery than the traditional MP method, and the proposed method lays the foundation for further correct fault analysis.

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“…Rapid growth in this recent industry has delivered increasingly complex machines. Diagnosis and checking of fault for current mechanical machinery is progressively more crucial in order to maintain a strategic distance from financial loss [1,2,3].One of the major causes in machinery failure is defects in rolling element bearings and have gotten extensive interest [4].…”

Section: Introductionmentioning

confidence: 99%

Early Fault Detection in Bearing with Fault Seeded on Outer Raceway at Three Different Position: Orthogonal, Centered and Opposite

Omran*,

Hassan Ali,

Shankar

et al. 2019

IJRTE

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Rotating machine such as a small low voltage motor or a power plant generator is an essential asset to the industrial applications. The execution and efficiency of these rotating machines are being reduced due to faulty rotating machinery parts. The faulty parts also generate various forces, thus increases the amplitude of vibration as well as energy consumption. Early fault detection and diagnosis have been widely used with various methods as they were able to reduce accidents and machine breakdowns along with economic losses. This study aims to present the faulty bearings which were seeded in the bearings. The fault size are ranging from 0.007 inches to 0.021 inches in diameter. Among the methods, vibration signal data is one of the champions. In this study, early fault detection was focused on bearing using the time domain technique and the data were analyzed. Particularly, the fault was introduced on the outer raceway at three different positions; orthogonal (3 o’clock), centered (6 o’clock) and opposite (12 o’clock). The MATLAB software was used to determine the time domain parameters, comprising of the standard deviation, Root Mean Square (RMS), skewness and shape factor as the representation of the best reflection of the failure. The time domain parameters for healthy and faulty bearing were plotted and compared in graphical presentation. The result shows all the four parameters have greater value in contrast with the healthy bearing value except for skewness data in the opposite (12 o’clock) position.

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Intelligent fault diagnosis of rolling element bearings using sparse wavelet energy based on overcomplete DWT and basis pursuit

Cited by 40 publications

References 47 publications

Bearing Fault Diagnosis Based on Subband Time-Frequency Texture Tensor

Bearing Fault Diagnosis Based on Subband Time-Frequency Texture Tensor

The application of matching pursuit based on multi feature pattern set in the signal processing of rotating machinery

Early Fault Detection in Bearing with Fault Seeded on Outer Raceway at Three Different Position: Orthogonal, Centered and Opposite

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