Research on Degradation State Recognition of Planetary Gear Based on Multiscale Information Dimension of SSD and CNN

Chen, Xihui; Peng, Liping; Luo, Chengming

doi:10.1155/2019/8716979

Cited by 12 publications

(12 citation statements)

References 27 publications

(31 reference statements)

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“…SSD inherits the signal separation function of singular spectrum analysis (SSA) and does not need to manually select parameters. The effectiveness of SSD for processing vibration signals of rotating machinery and its superiority over the previous signal separation algorithms has been fully illustrated in previous studies [ 30 , 31 , 32 ]. Thus, it is combined HT for TFA in this work.…”

Section: Introductionmentioning

confidence: 92%

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Gearbox Fault Diagnosis Based on Hierarchical Instantaneous Energy Density Dispersion Entropy and Dynamic Time Warping

Tang

Pang

et al. 2019

Entropy

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The accurate fault diagnosis of gearboxes is of great significance for ensuring safe and efficient operation of rotating machinery. This paper develops a novel fault diagnosis method based on hierarchical instantaneous energy density dispersion entropy (HIEDDE) and dynamic time warping (DTW). Specifically, the instantaneous energy density (IED) analysis based on singular spectrum decomposition (SSD) and Hilbert transform (HT) is first applied to the vibration signal of gearbox to acquire the IED signal, which is designed to reinforce the fault feature of the signal. Then, the hierarchical dispersion entropy (HDE) algorithm developed in this paper is used to quantify the complexity of the IED signal to obtain the HIEDDE as fault features. Finally, the DTW algorithm is employed to recognize the fault types automatically. The validity of the two parts that make up the HIEDDE algorithm, i.e., the IED analysis for fault features enhancement and the HDE algorithm for quantifying the information of signals, is numerically verified. The proposed method recognizes the fault patterns of the experimental data of gearbox accurately and exhibits advantages over the existing methods such as multi-scale dispersion entropy (MDE) and refined composite MDE (RCMDE).

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Section: Introductionmentioning

confidence: 92%

“…Previous studies have indicated that SSD has stronger signal decomposition capability than the traditional signal decomposition approaches such as EMD and EEMD [ 30 , 31 , 32 ]. Thus, it is integrated with HT to make the time-frequency analysis.…”

Section: Hierarchical Instantaneous Energy Density Dispersion Entrmentioning

confidence: 99%

Gearbox Fault Diagnosis Based on Hierarchical Instantaneous Energy Density Dispersion Entropy and Dynamic Time Warping

Tang

Pang

et al. 2019

Entropy

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“…Step 2: with the Grey modeling mechanism based on moving window, GM(1, 1) can be established to predict the IDI series segments. According to equations (11) to (13), the coefficients, i.e., a g and b g , can be obtained.…”

Section: Improved Grey-markov Model With Moving Windowmentioning

confidence: 99%

“…Because of these advantages, it gradually becomes one of the most commonly used maintenance pattern and attracts more and more focuses of researchers [9,10]. More speci cally, degradation tendency prediction plays an important role in the implementation of CBM, which is helpful to discover abnormal operation states before fault occurs and e ectively decrease the failure rate and maintenance costs [11,12].…”

Section: Introductionmentioning

confidence: 99%

Multistep Degradation Tendency Prediction for Aircraft Engines Based on CEEMDAN Permutation Entropy and Improved Grey–Markov Model

et al. 2019

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As an essential component and core power source of aircraft, the operational stability of aeroengine has important impact on system safety and reliability. Accurate degradation tendency prediction on an engine can not only improve its operational stability but also significantly reduce the maintenance costs. In this paper, a novel forecasting method that combines CEEMDAN permutation entropy and improved Grey–Markov model is proposed to perform multistep degradation tendency prediction of aircraft engines. In order to accurately quantify the degradation level of engines, a new integrated degradation index (IDI) is innovatively designed by multidimensional sensory data. And then, because of high speed and excellent performance, CEEMDAN algorithm is specifically employed to decompose the generated IDI series to eliminate the potential influence of stochastic fluctuations. Aiming at the complexity of intrinsic mode functions (IMFs) generated by CEEMDAN, an IMFs reconstruction strategy based on permutation entropy is developed to better characterize the degradation states. Finally, on the basis of above achievements and for higher forecasting efficiency and accuracy, an improved Grey–Markov model combined with the moving window algorithm, which is unique, is constructed to realize multistep degradation trend prediction of engines. The proposed method is applied to the degradation tendency prediction of aircraft engines. The experimental results validate the effectiveness and superiority of the proposed method, and it is more suitable for engineering applications in comparison with other methods.

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“…The recent progress of the vibrationbased fault diagnosis can be reviewed from Ref. [8][9][10][11]. However, the vibration-based method confronts challenges when the rotating machinery operates under varying-speed conditions [12][13][14] because the obtained vibration signal represents complex nonstationary characteristics, such as frequency modulation, amplitude modulation, and phase modulation, resulting in the difficulty of fault frequency detection.…”

Section: Introductionmentioning

confidence: 99%

A New Intelligent Fault Diagnosis Method of Rotating Machinery under Varying‐Speed Conditions Using Infrared Thermography

Wang

et al. 2019

Complexity

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A novel systematic framework, infrared thermography- (IRT-) based method, for rotating machinery fault diagnosis under nonstationary running conditions is presented in this paper. In this framework, IRT technique is first applied to obtain the thermograph. Then, the fault features are extracted using bag-of-visual-word (BoVW) from the IRT images. In the end, support vector machine (SVM) is utilized to automatically identify the fault patterns of rotating machinery. The effectiveness of proposed method is evaluated using lab experimental signal of rotating machinery. The diagnosis results show that the IRT-based method has certain advantages in classification rotating machinery faults under nonstationary running conditions compared with the traditional vibration-based method.

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Research on Degradation State Recognition of Planetary Gear Based on Multiscale Information Dimension of SSD and CNN

Cited by 12 publications

References 27 publications

Gearbox Fault Diagnosis Based on Hierarchical Instantaneous Energy Density Dispersion Entropy and Dynamic Time Warping

Gearbox Fault Diagnosis Based on Hierarchical Instantaneous Energy Density Dispersion Entropy and Dynamic Time Warping

Multistep Degradation Tendency Prediction for Aircraft Engines Based on CEEMDAN Permutation Entropy and Improved Grey–Markov Model

A New Intelligent Fault Diagnosis Method of Rotating Machinery under Varying‐Speed Conditions Using Infrared Thermography

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