Multiscale Diversity Entropy: A Novel Dynamical Measure for Fault Diagnosis of Rotating Machinery

Wang, Xianzhi; Si, Shubin; Li, Yongbo

doi:10.1109/tii.2020.3022369

Cited by 78 publications

(41 citation statements)

References 24 publications

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“…In this section, the three types of Noted that the embedding dimension, tolerance, and symbols are set as the best parameters which can be referred to Ref. [3], [4], [5], and [17]. To have a fair comparison, the scale =31  and layer 4 k  .…”

Section: Simulation Results and Analysismentioning

confidence: 99%

Hierarchical Diversity Entropy for the Early Fault Diagnosis of Rolling Bearing

Wang

2021

Preprint

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Intelligent fault diagnosis provides great convenience for the prognostic and health management of the rotating machinery. Recently, the entropy-based feature extraction method has aroused researchers’ attentions due to its independence with prior knowledge, unnecessary of preprocessing, and easy to perform. The multiscale diversity entropy has been proven to be a promising feature extraction method for the intelligent fault diagnosis. Compared to the existing entropy methods, the multiscale diversity entropy has advantages of high consistency, strong robustness and high calculation efficiency. However, the multiscale diversity entropy encounters the challenge to extract features for early fault diagnosis due to the weak fault symptoms and strong noise. This can be attributed to the multiscale diversity entropy only concerns the fault information embedded in the low frequency, which ignores the information hidden in the high frequency. To address this defect, the hierarchical diversity entropy (HDE) is proposed, which can synchronously extract fault information hidden in both high and low frequency. Based on HDE and random forest, a novel intelligent fault diagnosis frame has been proposed. The effectiveness of the proposed method has been evaluated through simulated and experimental bearing signals. The results show that the proposed HDE has the best feature extraction ability compare to multiscale sample entropy, multiscale permutation entropy, multiscale fuzzy entropy, and multiscale diversity entropy.

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Section: Simulation Results and Analysismentioning

confidence: 99%

Hierarchical Diversity Entropy for the Early Fault Diagnosis of Rolling Bearing

Wang

2021

Preprint

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“…A novel entropy-based feature extraction method is proposed, which analyzes the dynamic complexity characteristics of the arbitrary time series. Compared with the method in [34], the entropy value of the proposed method is more stability for the short time series and this method can keep more information in low frequency.…”

Section: Introductionmentioning

confidence: 99%

“…Recent years, Wang. et al proposed a novel entropy method named diversity entropy (MDiEn) [34], which considers the dynamic complexity of the time series. However, the MDiEn method is instability for short time series, and only focus on the low frequency parts.…”

Section: Introductionmentioning

confidence: 99%

Generalized Composite Multiscale Diversity Entropy and Its Application for Fault Diagnosis of Rolling Bearing in Automotive Production Line

Liang

Chen

2021

IEEE Access

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This paper considers the entropy based feature extraction method for the fault diagnosis of rolling bearings in automobile production line, where the fault information is difficult to identify due to the strong nonlinear and non-stationary characteristics of the fault vibration signals. In our work, a novel entropy based method called generalized composite multiscale diversity entropy (GCMDiEn) is developed. This method can effectively track the inside pattern changes of the time series by the description of cosine similarity between adjacent orbits. Unlike most of the existing entropy based results which concentrate on the static orderliness, we analyze the dynamic complexity characteristics of the arbitrary time series. Moreover, compared with the multiscale diversity entropy, GCMDiEn calculates all coarse-grained time series entropy value with the same scale and extends the first-order moment to second-order moment to mitigate the shortcomings of the short time series instability and losing part of the frequency region information. This paper proposed a new rolling bearing fault diagnosis framework which combined the GCMDiEn with the empirical wavelet transform (EWT), Laplacian score (LS), and particle swarm optimization-based support vector machine (PSO-SVM). Finally, the simulation results show the superiority of the GCMDiEn method over the multiscale diversity entropy method. The proposed framework has a higher fault recognition rate (99.38%) than the existing methods.

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“…Li et al established an INS/ADS fault detection model based on kinematic equations, and combined an unscented Kalman filter (UKF) with Runge-Kutta to deal with the non-linear and discretization problem [3]. Second, some research aims at extracting the fault features by constructing more effective signal processing methods, such as the feature extraction method based on entropy value [4,5], the feature extraction method based on spectral kurtosis time (Spectral Kurtosis, SK) [6], or the Frequency domain feature extraction method [7]. To fully excavate the features of the momentum wheel telemetry signal, this paper uses a combination of time domain features, frequency domain features and complexity features for feature extraction.…”

Section: Introductionmentioning

confidence: 99%

A Novel Framework for Anomaly Detection for Satellite Momentum Wheel Based on Optimized SVM and Huffman-Multi-Scale Entropy

Lei

Liu³

et al. 2021

Entropy

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The health status of the momentum wheel is vital for a satellite. Recently, research on anomaly detection for satellites has become more and more extensive. Previous research mostly required simulation models for key components. However, the physical models are difficult to construct, and the simulation data does not match the telemetry data in engineering applications. To overcome the above problem, this paper proposes a new anomaly detection framework based on real telemetry data. First, the time-domain and frequency-domain features of the preprocessed telemetry signal are calculated, and the effective features are selected through evaluation. Second, a new Huffman-multi-scale entropy (HMSE) system is proposed, which can effectively improve the discrimination between different data types. Third, this paper adopts a multi-class SVM model based on the directed acyclic graph (DAG) principle and proposes an improved adaptive particle swarm optimization (APSO) method to train the SVM model. The proposed method is applied to anomaly detection for satellite momentum wheel voltage telemetry data. The recognition accuracy and detection rate of the method proposed in this paper can reach 99.60% and 99.87%. Compared with other methods, the proposed method can effectively improve the recognition accuracy and detection rate, and it can also effectively reduce the false alarm rate and the missed alarm rate.

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Multiscale Diversity Entropy: A Novel Dynamical Measure for Fault Diagnosis of Rotating Machinery

Cited by 78 publications

References 24 publications

Hierarchical Diversity Entropy for the Early Fault Diagnosis of Rolling Bearing

Hierarchical Diversity Entropy for the Early Fault Diagnosis of Rolling Bearing

Generalized Composite Multiscale Diversity Entropy and Its Application for Fault Diagnosis of Rolling Bearing in Automotive Production Line

A Novel Framework for Anomaly Detection for Satellite Momentum Wheel Based on Optimized SVM and Huffman-Multi-Scale Entropy

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