A novel feature extraction method based on weighted multi-scale fluctuation based dispersion entropy and its application to the condition monitoring of rotary machines

Sharma, Snehsheel; Tiwari, Sanjay

doi:10.1016/j.ymssp.2022.108909

Cited by 33 publications

(19 citation statements)

References 63 publications

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“…pattern [9] . Compared with the existing methods, DispEn has the following advantages: 1) DispEn will not have unde ned results when processing short-time signals; 2) DispEn has better robustness to noise; 3) DispEn is much faster than SampEn [10][11][12] . Because of these advantages, DispEn has been successfully applied in different scienti c and engineering elds, including biomedical engineering, mechanical engineering and computer engineering [13][14][15][16] .…”

Section: Rostaghi Et Al Proposed Dispersion Entropy (Dispen) By Combi...mentioning

confidence: 99%

A new fractional fuzzy dispersion entropy and its application in muscle fatigue detection

Baohua,

Yong,

Jingsong

2023

Preprint

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Background The complexity analysis of neuroelectrophysiological signals has been widely applied in the field of biomedical engineering and muscle fatigue detection using the complexity analysis of surface electromyographic (sEMG) signals is one of the hot research topics. Recently, fuzzy dispersion entropy has attracted more and more attention as a new nonlinear dynamics method for complexity analysis which combines the advantages of both dispersion entropy(DispEn) and fuzzy entropy. However, it suffers from limitation of sensitivity to dynamic changes. In this study, fractional fuzzy dispersion entorpy (FFDispEn) is proposed based on DispEn, a new fuzzy membership function and fractional calculus to solve this limitation. Fuzzy membership function is defined based on Euclidean distance between embdding vector and dispersion pattern in this study. Methods Simulated signals generated by 1D Logistic map were used to test the sensitivity of the proposed method to dynamic changes. Moreover, ten subjects were recruited for upper limb muscle fatigue exprienment while sEMG signals of the biceps brachii muscle were recorded. Both simulated signals and sEMG signals were processed using sliding window approach. Sample entropy(SampEn), DispEn and FFDispEn were respectively used to calculate the complexity of each frame. The sensitivity of different algorithms to muscle fatigue process was analyzed using fitting parameters through linear fitting of the complexity of each frame signal. Results The results show that for simulated signals, the larger the fractional order q, the higher the sensitivity to dynamic changes. Moreover, DispEn performs poorly in the sensitivity to dynamic changes compared with FFDispEn. As for the muscle fatigue detection, FFDispEn value shows a clear declining tendency as muscle fatigue progresses and is more sensitive to muscle fatigue compared with SampEn and DispEn. Conclusions This study provides a new useful nonlinear dynamic indicator for sEMG signal preprocessing and muscle fatigue analysis. The proposed method may be useful for physiological and biomedical signal analysis.

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Section: Rostaghi Et Al Proposed Dispersion Entropy (Dispen) By Combi...mentioning

confidence: 99%

A new fractional fuzzy dispersion entropy and its application in muscle fatigue detection

Baohua,

Yong,

Jingsong

2023

Preprint

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“…Consequently, entropy-based methods have been developed in line with the advancements in nonlinear dynamics technology. These methods encompass symbolic dynamic entropy, Shannon entropy, multiscale entropy, sample entropy, permutation entropy, multiscale dispersion entropy (MDE), and multiscale fluctuation dispersion entropy (MFDE) [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

A Novel Intelligent Fault Diagnosis Method for Self-Priming Centrifugal Pumps

Zhang,

Wang,

Yao

et al. 2023

Entropy

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The real-time diagnostic monitoring of self-priming centrifugal pumps is essential to ensure their safe operation. Nevertheless, owing to the intricate structure and complex operational conditions inherent in such pumps, existing fault diagnosis methods encounter challenges in effectively extracting crucial fault feature information and accurately identifying fault types. Consequently, this paper introduces an intelligent fault diagnosis method tailored for self-priming centrifugal pumps. The approach amalgamates refined time-shift multiscale fluctuation dispersion entropy, cosine pairwise-constrained supervised manifold mapping, and adaptive chaotic Aquila optimization support vector machine techniques. To begin with, refined time-shift multiscale fluctuation dispersion entropy is employed to extract fault-related features, adeptly mitigating concerns related to entropy domain deviations and instability. Subsequently, the application of cosine pairwise-constrained supervised manifold mapping serves to reduce the dimensionality of the extracted fault features, thereby bolstering the efficiency and precision of the ensuing identification process. Ultimately, the utilization of an adaptive chaotic Aquila optimization support vector machine facilitates intelligent fault classification, leading to enhanced accuracy in fault identification. The experimental findings unequivocally affirm the efficacy of the proposed method in accurately discerning among various fault types in self-priming centrifugal pumps, achieving an exceptional recognition rate of 100%. Moreover, it is noteworthy that the average correct recognition rate achieved by the proposed method surpasses that of five existing intelligent fault diagnosis techniques by a significant margin, registering a notable increase of 15.97%.

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“…In addition, D. Pham 34 proposed an improved multiscale analysis using the time-delay process to solve this problem. These methods have been applied in mechanical system fault diagnosis 35,36 and structural health monitoring 7,8 by extracting information from complex and nonlinear time series.…”

Section: Introductionmentioning

confidence: 99%

A novel feature extraction method based on symbol-scale diversity entropy and its application for fault diagnosis of rotary machines

Wang

Zhang

et al. 2023

Structural Health Monitoring

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Multiscale entropy-based methods have made great progress in the field of health condition monitoring and fault diagnosis of machines due to their powerful feature representation capabilities. However, existing multiscale entropy methods suffer from three major obstacles: high fluctuation under large scale-factor, loss of high-frequency information, and poor robustness to noises. Thus, this work proposes a symbol-scale analysis method to deal with the above problems. In one aspect, to capture fault features from the time series over multiple time scales, time-delay process of different intervals is utilized to obtain long-term features and short-term features. In the other aspect, symbol-scale analysis introduces a symbolization procedure and maps time series into a corresponding sequence of symbols to overcome the limitation of weak fault extraction under a low-signal-to-noise ratio environment. Moreover, the symbol-scale entropy approach is developed by integrating with diversity entropy, called symbol-scale diversity entropy. The effectiveness of the proposed strategy is intensively validated using two simulated signals and experimental cases. Results demonstrate its advantages in dynamic change tracking ability and calculation efficiency by comparing it with other state-of-the-art entropy methods. Apart from diversity entropy, the versatility of incorporating the proposed symbol-scale analysis and other entropy methods is also verified using experimental data.

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A novel feature extraction method based on weighted multi-scale fluctuation based dispersion entropy and its application to the condition monitoring of rotary machines

Cited by 33 publications

References 63 publications

A new fractional fuzzy dispersion entropy and its application in muscle fatigue detection

A new fractional fuzzy dispersion entropy and its application in muscle fatigue detection

A Novel Intelligent Fault Diagnosis Method for Self-Priming Centrifugal Pumps

A novel feature extraction method based on symbol-scale diversity entropy and its application for fault diagnosis of rotary machines

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