Permutation entropy-based 2D feature extraction for bearing fault diagnosis

Landauskas, Mantas; Cao, Maosen; Ragulskis, Minvydas

doi:10.1007/s11071-020-06014-6

Cited by 49 publications

(20 citation statements)

References 39 publications

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“…As such, they are widely applied for the feature extraction of various mechanical signals. Said technology mainly includes approximate entropy, sample entropy, permutation entropy, fuzzy entropy, and dynamic symbolic entropy [15][16][17][18][19].…”

Section: Entropy-based Feature Extraction Approachmentioning

confidence: 99%

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An effective multi-channel fault diagnosis approach for rotating machinery based on multivariate generalized refined composite multi-scale sample entropy

et al. 2021

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Fault diagnosis of critical rotating machinery components is necessary to ensure safe operation. However, the commonly used fault diagnosis methods for rotating machinery are generally based on the single-channel signal processing method, which is not suitable for processing multi-channel signals. Thus, to extract features and carry out the intelligent diagnosis of multi-channel signals, a novel method for rotating machinery fault diagnosis is proposed.Firstly, a novel non-linear dynamics technique, named the multi-variate generalized refined composite multi-scale sample entropy, is presented and applied to extract fusion entropy features of multi-channel signals. Secondly, a practical manifold learning known as supervised isometric mapping is introduced to map the high-dimensional fusion entropy features in a low-dimensional space. In a third step, the Harris hawks optimization-based support vector machine is utilized to achieve intelligent fault recognition. Finally, aiming to verify the effectiveness of the proposed method and present its advantages, it was applied to analyze the rotating machinery system bearing and gear data. The experimental results have shown that the method at hand can accurately identify various bearing and gear faults. Furthermore, in addition to being suitable for multi-channel signal fault diagnosis, it displayed higher recognition accuracy when compared with other multi-channel or single-channel methods.

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Section: Entropy-based Feature Extraction Approachmentioning

confidence: 99%

“…Fig 17. The 3D result obtained using the SIM The 3D visualization results (shown in Fig.17) show that SIM thoroughly separates the four-state samples, completely avoiding sample overlap.…”

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confidence: 93%

An effective multi-channel fault diagnosis approach for rotating machinery based on multivariate generalized refined composite multi-scale sample entropy

et al. 2021

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“…Now, permutation entropy, an effective method for identifying abrupt change points, is often applied to extract the features of different faults. Landauskas constructed the patterns of permutation entropy by using non-uniform embedding of the vibration signal and detected the early faults of rolling bearing [13]. Sharma combined permutation entropy and variation mode decomposition to detect gear faults effectively [14].…”

Section: Introductionmentioning

confidence: 99%

Gear compound fault detection method based on improved multiscale permutation entropy and local mean decomposition

Yang¹,

Chen²,

Dai³

2021

J. vibroeng.

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The traditional multiscale entropy algorithm shows inconsistency because some points are ignored when the signal is coarsened. To solve this problem, this paper proposes an improved multiscale permutation entropy (IMSPE). Firstly, the fault signal is decomposed into several product functions (PF) by local mean decomposition (LMD). Secondly, IMSPE is proposed to extract fault features of product functions. IMSPE integrates the information of multiple coarse sequences and solves problems of entropy inconsistency. Finally, the proposed method based on LMD and IMSPE is applied into gear fault diagnosis system. The experiment shows the proposed method can distinguish different gear fault types with a higher accuracy than traditional methods.

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“…Currently, PeEn has found its application in various research fields [24][25][26]. However, PeEn may lack the capability to fully describe dynamics of complex signals since compressing all the information into a single parameter [27]. Afterwards, multiscale PeEn (MPeEn) was put forward for coping with nonstationarity, outliers and artifacts emerging in complex signals [28].…”

Section: Introductionmentioning

confidence: 99%

Feature extraction based on generalized permutation entropy for condition monitoring of rotating machinery

2021

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Defective rotating machinery usually exhibits complex dynamic behavior. Therefore, feature representation of machinery vibration signals is always critical for condition monitoring of rotating machinery. Permutation entropy (PeEn), an adaptive symbolic description, can measure complexities of signals. However, PeEn may lack the capability to fully describe dynamics of complex signals since compressing all the information into a single parameter. Afterwards, multiscale PeEn (MPeEn) is put forward for coping with nonstationarity, outliers and artifacts emerging in complex signals. In MPeEn, a set of parameters serve to describe dynamics of a complex signal in different time scales. Nonetheless, an average procedure in MPeEn may withhold local information of a complex signal. To overcome deficiencies of PeEn and MPeEn, this paper proposes generalized PeEn (GPeEn) by introducing different time lags and orders into PeEn. In GPeEn, a complex signal is compressed into one matrix rather than a single parameter. Moreover, minimal, maximal and average values of a matrix obtained by GPeEn serve to briefly describe conditions of rotating machinery. Next, a numerical experiment proves that GPeEn outperforms PeEn and MPeEn in characterizing conditions of a Lorenz model. Subsequently, the performance of GPeEn is benchmarked against that of PeEn and MPeEn by investigating gear and roll-bearing vibration signals containing different types and severity of faults. The results show that the proposed method has a clear advantage over PeEn and MPeEn in condition monitoring of rotating machinery.

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Permutation entropy-based 2D feature extraction for bearing fault diagnosis

Cited by 49 publications

References 39 publications

An effective multi-channel fault diagnosis approach for rotating machinery based on multivariate generalized refined composite multi-scale sample entropy

An effective multi-channel fault diagnosis approach for rotating machinery based on multivariate generalized refined composite multi-scale sample entropy

Gear compound fault detection method based on improved multiscale permutation entropy and local mean decomposition

Feature extraction based on generalized permutation entropy for condition monitoring of rotating machinery

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