Identification of Multiple Faults in Gearbox Based on Multipoint Optional Minimum Entropy Deconvolution Adjusted and Permutation Entropy

The weak compound fault feature is difficult to extract from a gearbox because the signal components are complex and inter-modulated. An approach (that is abbreviated as MRPE-MOMEDA) for extracting the weak fault features of a transmission based on a multipoint optimal minimum entropy deconvolution adjustment (MOMEDA) and the permutation entropy was proposed to solve this problem in the present paper. The complexity of the periodic impact signal was low and the permutation entropy was relatively small. Moreover, the amplitude of the impact was relatively large. Based on these advantages, the multipoint reciprocal permutation entropy (MRPE) was proposed to track the impact fault source of the weak fault feature in gearbox compound faults. The impact fault period was indicated through MRPE. MOMEDA achieved signal denoising. The optimal filter coefficients were solved using MOMEDA. It exhibits an outstanding performance for noise suppression of gearbox signals with a periodic impact. The results from the transmission show that the proposed method can identify multiple faults simultaneously on a driving gear in the 4th gear of the transmission.

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Gearbox compound fault diagnosis based on a combined MSGMD-MOMEDA method

Measurement Science and Technology ◽

10.1088/1361-6501/ac456b ◽

2021 ◽

Author(s):

Jianqun Zhang ◽

Qing Zhang ◽

Xianrong Qin ◽

Yuantao Sun ◽

Jun Zhang

Keyword(s):

Background Noise ◽

Symplectic Geometry ◽

Original Method ◽

Minimum Entropy ◽

Mode Decomposition ◽

Accuracy And Precision ◽

Fault Features ◽

Simulation And Experiment ◽

Diagnosis Method ◽

Weak Fault

Abstract Weak fault detection is a complex and challenging task when two or more faults (compound fault) with discordant severity occur in different parts of a gearbox. The weak fault features are prone to be submerged by the severe fault features and strong background noise, which easily lead to a missed diagnosis. To solve this problem, a novel diagnosis method combining muti-symplectic geometry mode decomposition and multipoint optimal minimum entropy deconvolution adjusted (MSGMD-MOMEDA) is proposed for gearbox compound fault in this paper. Specifically, different fault components are separated by the improved symplectic geometry mode decomposition (SGMD), namely, multi-SGMD (MSGMD) method. The weak fault features are enhanced by the multipoint optimal minimum entropy deconvolution adjusted (MOMEDA). In the process of research, a new scheme of selecting key parameters of MOMEDA is proposed, which is a key step in applying MOMEDA. Compared with SGMD, the proposed MSGMD has two main improvements, including suppressing mode mixing and preventing the generation of the pseudo components. Compared with the original method of selecting parameters based on multipoint kurtosis, the proposed MOMEDA parameters selecting scheme has more merits of high accuracy and precision. The analysis results of two cases of simulation and experiment signal reveal that the MSGMD-MOMEDA method can accurately diagnose the gearbox compound fault even under strong background noise.

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A Parameter Adaptive MOMEDA Method Based on Grasshopper Optimization Algorithm to Extract Fault Features

Mathematical Problems in Engineering ◽

10.1155/2019/7182539 ◽

2019 ◽

Vol 2019 ◽

pp. 1-22 ◽

Cited By ~ 3

Author(s):

ChengJiang Zhou ◽

Jun Ma ◽

Jiande Wu ◽

Zezhong Feng

Keyword(s):

Optimization Algorithm ◽

Vibration Signal ◽

Minimum Entropy ◽

Period Range ◽

Fault Features ◽

Grasshopper Optimization Algorithm ◽

Filter Size ◽

Grasshopper Optimization ◽

The Impact ◽

Parameter Adaptive

The nonstationary components and noises contained in the bearing vibration signal make it particularly difficult to extract fault features, and minimum entropy deconvolution (MED), maximum correlated kurtosis deconvolution (MCKD), and fast spectral kurtosis (FSK) cannot achieve satisfactory results. However, the filter size and period range of multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) need to be set in advance, so it is difficult to achieve satisfactory filtering results. Aiming at these problems, a parameter adaptive MOMEDA feature extraction method based on grasshopper optimization algorithm (GOA) is proposed. Firstly, the multipoint kurtosis (MKurt) of MOMEDA filtered signal is used as the optimization objective, and the optimal filter size and periodic initial value which matched with the vibration signal can be determined adaptively through multiple iterations of GOA. Secondly, the periodic impact contained in the vibration signal is extracted by the optimized MOMEDA, and the fault features in the impact signal are extracted by Hilbert envelope demodulation. Finally, the simulation signal and bearing signal are processed by the proposed approach. The results show that the introduction of GOA not only solves the problem of parameter selection in MOMEDA, but also achieves better performance compared with other optimization methods. Meanwhile, the feasibility and superiority of the approach are fully proved by comparing it with the three methods MED, MCKD, and FSK after parameter optimization. Therefore, this approach provides a novel way and solution for fault diagnosis of the rolling bearing, gear, and shaft.

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Partly Duffing Oscillator Stochastic Resonance Method and Its Application on Mechanical Fault Diagnosis

Shock and Vibration ◽

10.1155/2016/3109385 ◽

2016 ◽

Vol 2016 ◽

pp. 1-14 ◽

Cited By ~ 3

Author(s):

Jian Dang ◽

Rong Jia ◽

Xingqi Luo ◽

Hua Wu ◽

Diyi Chen

Keyword(s):

Fault Diagnosis ◽

Mechanical System ◽

Stochastic Resonance ◽

Noise Intensity ◽

Duffing Oscillator ◽

Working Environment ◽

Permutation Entropy ◽

Weak Signals ◽

Weak Fault ◽

The Impact

Due to the fact that the slight fault signals in early failure of mechanical system are usually submerged in heavy background noise, it is unfeasible to extract the weak fault feature via the traditional vibration analysis. Stochastic resonance (SR), as a method of utilizing noise to amplify weak signals in nonlinear dynamical systems, can detect weak signals overwhelmed in the noise. However, based on the analysis of the impact of noise intensity on SR effect, it is concluded that the detection results are dramatically limited by the noise intensity of measured signals, especially for incipient fault feature of mechanical system with poor working environment. Therefore, this paper proposes a partly Duffing oscillator SR method to extract the fault feature of mechanical system. In this method, to locate the appearance of weak fault feature and decrease noise intensity, the permutation entropy index is constructed to select the measured signals for the input of Duffing oscillator system. Then, according to the regulation of system parameters, a reasonable match between the selected signals and Duffing oscillator model is achieved to produce a SR phenomenon and realize the fault diagnosis of mechanical system. Experiment results demonstrate that the proposed method achieves a better effect on the fault diagnosis of mechanical system.

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Application of Mutual Information-Sample Entropy Based MED-ICEEMDAN De-Noising Scheme for Weak Fault Diagnosis of Hoist Bearing

Entropy ◽

10.3390/e20090667 ◽

2018 ◽

Vol 20 (9) ◽

pp. 667 ◽

Cited By ~ 4

Author(s):

Fen Yang ◽

Ziming Kou ◽

Juan Wu ◽

Tengyu Li

Keyword(s):

Mutual Information ◽

Poor Performance ◽

Ensemble Empirical Mode Decomposition ◽

Sample Entropy ◽

Features Extraction ◽

Minimum Entropy ◽

Heavy Load ◽

Noise Background ◽

Fault Features ◽

Weak Fault

In this paper, a novel weak fault features extraction scheme is proposed to extract weak fault features in head sheave bearings of floor-type multi-rope friction mine hoists in strong noise environments. A mutual information-based sample entropy (MI-SE) is proposed to select the effective intrinsic mode function (IMF). The numerical simulation presented in this paper has demonstrated that the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) has a poor performance on weak signals processing under a strong noise background, and fault features cannot be identified clearly. The de-noised signal is decomposed into several IMFs by the ICEEMDAN method, with the help of the minimum entropy deconvolution (MED), which works as a pre-filter to increase the kurtosis value by about 3.2 times. The envelope spectrum of the effective IMF selected by the MI-SE method shows almost all fault features clearly. An analogous experiment system was built to verify the feasibility of the proposed scheme, whose results have also shown that the proposed hybrid scheme has better performance compared with ICEEMDAN or MED on the weak fault features extraction under a strong noise background. This paper provides a novel method to diagnose the weak faults of the slow speed and heavy load rolling bearings in a strong noise environment.

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An Enhanced Intrinsic Time-Scale Decomposition Method Based on Adaptive Lévy Noise and Its Application in Bearing Fault Diagnosis

Symmetry ◽

10.3390/sym13040617 ◽

2021 ◽

Vol 13 (4) ◽

pp. 617

Author(s):

Jianpeng Ma ◽

Shi Zhuo ◽

Chengwei Li ◽

Liwei Zhan ◽

Guangzhu Zhang

Keyword(s):

Fault Diagnosis ◽

Time Scale ◽

Lévy Noise ◽

Model Parameters ◽

Rolling Bearings ◽

Intrinsic Time ◽

Time Scale Decomposition ◽

Weak Fault ◽

Levy Noise ◽

The Impact

When early failures in rolling bearings occur, we need to be able to extract weak fault characteristic frequencies under the influence of strong noise and then perform fault diagnosis. Therefore, a new method is proposed: complete ensemble intrinsic time-scale decomposition with adaptive Lévy noise (CEITDALN). This method solves the problem of the traditional complete ensemble intrinsic time-scale decomposition with adaptive noise (CEITDAN) method not being able to filter nonwhite noise in measured vibration signal noise. Therefore, in the method proposed in this paper, a noise model in the form of parameter-adjusted noise is used to replace traditional white noise. We used an optimization algorithm to adaptively adjust the model parameters, reducing the impact of nonwhite noise on the feature frequency extraction. The experimental results for the simulation and vibration signals of rolling bearings showed that the CEITDALN method could extract weak fault features more effectively than traditional methods.

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Blind Identification of Impact Forces From Multiple Remote Vibratory Measurements

10.1115/imece2000-2185 ◽

2000 ◽

Author(s):

J. Antunes ◽

P. Izquierdo ◽

M. Paulino

Keyword(s):

Explicit Knowledge ◽

Transfer Functions ◽

Dynamical Behavior ◽

Practical Interest ◽

Operating Conditions ◽

Blind Identification ◽

Minimum Entropy ◽

Response Measurement ◽

Identification Techniques ◽

The Impact

Abstract Structures and mechanical components are often subjected to impulsive forces. There is a need for identification techniques which enable monitoring of such loads under operating conditions. For safety reasons and convenience, force identification must often be based on response motions sensed at accessible locations, remote from the impact points. In our previous work we presented techniques for the experimental identification of both isolated impacts and complex rattling forces on a beam, generated at a single and also at several impacting supports. The system dynamical behavior was modeled using traveling flexural beam waves. Although successful, these techniques obviously assume a good understanding of the system dynamic parameters. This is not always the case, a fact that highlights the practical interest of blind identification techniques. This relatively recent field, connected with higher-order statistics, avoids any explicit knowledge of the system transfer functions or impulse responses. Our previous work, based on a single response measurement, is extended in this paper to include several simultaneous responses. We develop a multi-trace version of Wiggins minimum-entropy blind deconvolution algorithm. From numerical simulations and experiments, it is shown that the robustness to noise contamination is increased by using multiple response data. These results suggest that blind identification techniques will prove very useful in practical situations.

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Research on gearbox impact feature extraction method based on improved ESMD

Insight - Non-Destructive Testing and Condition Monitoring ◽

10.1784/insi.2022.64.1.20 ◽

2022 ◽

Vol 64 (1) ◽

pp. 20-27

Author(s):

Fengfeng Bie ◽

Sheng Gu ◽

Yue Guo ◽

Gang Yang ◽

Jian Peng

Keyword(s):

Feature Vector ◽

Vibration Signal ◽

Permutation Entropy ◽

Significant Feature ◽

Support Vector ◽

Feature Extraction Method ◽

Mode Decomposition ◽

Envelope Spectrum ◽

Impact Characteristics ◽

The Impact

A gearbox vibration signal contains non-linear impact characteristics and the significant feature information tends to be overwhelmed by other interference components, which make it difficult to extract the typical fault features fully and effectively. Aiming at the key issue of how to effectively extract the impact characteristics, a fault diagnosis method based on improved extreme symmetric mode decomposition (ESMD) and a support vector machine (SVM) is proposed in this paper. The vibration signal is adaptively decomposed into multiple intrinsic mode function (IMF) components by the improved ESMD and then a certain number of components are selected with the maximum kurtosis-envelope spectrum index. The singular spectral entropy, energy entropy and permutation entropy of each component are applied to construct the feature vector set, in which the dimensionality of the set is reduced with the distance separability criterion. Finally, the dimension-reduced feature vector set is input into the SVM for pattern recognition. Dynamic simulation and experimental gearbox research show that the improved ESMD method can extract and identify gearbox fault information effectively.

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A Feature Extraction Method of Wheelset-Bearing Fault Based on Wavelet Sparse Representation with Adaptive Local Iterative Filtering

Shock and Vibration ◽

10.1155/2020/2019821 ◽

2020 ◽

Vol 2020 ◽

pp. 1-20

Author(s):

Zhan Xing ◽

Jianhui Lin ◽

Yan Huang ◽

Cai Yi

Keyword(s):

Feature Extraction ◽

Sparse Representation ◽

Extraction Method ◽

High Speed ◽

Optimal Solution ◽

Feature Extraction Method ◽

Bearing Fault ◽

Fault Features ◽

Iterative Filtering ◽

The Impact

The feature extraction of wheelset-bearing fault is important for the safety service of high-speed train. In recent years, sparse representation is gradually applied to the fault diagnosis of wheelset-bearing. However, it is difficult for traditional sparse representation to extract fault features ideally when some strong interference components are imposed on the signal. Therefore, this paper proposes a novel feature extraction method of wheelset-bearing fault based on the wavelet sparse representation with adaptive local iterative filtering. In this method, the adaptive local iterative filtering reduces the impact of interference components effectively and contributes to the extraction of sparse impulses. The wavelet sparse representation, which adopts L1-regularized optimization for a globally optimal solution in sparse coding, extracts intrinsic features of fault in the wavelet domain. To validate the effectiveness of this proposed method, both simulated signals and experimental signals are analyzed. The results show that the fault features of wheelset-bearing are sufficiently extracted by the proposed method.

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Wind Turbine Gearbox Fault Detection Based on Multifractal Analysis

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.644.312 ◽

2013 ◽

Vol 644 ◽

pp. 312-316

Author(s):

Chang Zheng Chen ◽

Ping Ping Pan ◽

Qiang Meng ◽

Yan Ling Gu

Keyword(s):

Wind Turbine ◽

Structural Vibrations ◽

Wind Turbine Gearbox ◽

Vibration Signals ◽

The Real ◽

Signal Processing Method ◽

Fault Features ◽

Incipient Faults ◽

Weak Fault ◽

Multifractal Method

The presence of irregularity in periodical vibration signals usually indicates the occurrence of wind turbine gearbox faults. Unfortunately, detecting the incipient faults is a difficult job because they are rather weak and often interfered by heavy noise and higher level macro-structural vibrations. Therefore, a proper signal processing method is necessary. We used the wavelet-based multifractal method to extract the impulsive features buried in noisy vibration signals. We first calculated the wavelet transform modulo maxima lines from the real vibration signals, then, obtained the singularity spectrum from the lines. The analysis results of the real signals showed that the proposed method can effectively extract weak fault features.

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Enhancement of Fault Feature Extraction from Displacement Signals by Suppressing Severe End Distortions via Sinusoidal Wave Reduction

Energies ◽

10.3390/en12183536 ◽

2019 ◽

Vol 12 (18) ◽

pp. 3536 ◽

Cited By ~ 1

Author(s):

Binqiang Chen ◽

Qixin Lan ◽

Yang Li ◽

Shiqiang Zhuang ◽

Xincheng Cao

Keyword(s):

Feature Extraction ◽

Wavelet Analysis ◽

Low Frequency ◽

Wavelet Packets ◽

Discrete Wavelet ◽

Sinusoidal Wave ◽

Displacement Signal ◽

Fault Features ◽

Reconstructed Signal ◽

Weak Fault

Displacement signals, acquired by eddy current sensors, are extensively used in condition monitoring and health prognosis of electromechanical equipment. Owing to its sensitivity to low frequency components, the displacement signal often contains sinusoidal waves of high amplitudes. If the digitization of the sinusoidal wave does not satisfy the condition of full period sampling, an effect of severe end distortion (SED), in the form of impulsive features, is likely to occur because of boundary extensions in discrete wavelet decompositions. The SED effect will complicate the extraction of weak fault features if it is left untreated. In this paper, we investigate the mechanism of the SED effect using theories based on Fourier analysis and wavelet analysis. To enhance feature extraction performance from displacement signals in the presence of strong sinusoidal waves, a novel method, based on the Fourier basis and a compound wavelet dictionary, is proposed. In the procedure, ratio-based spectrum correction methods, using the rectangle window as well as the Hanning window, are employed to obtain an optimized reduction of strong sinusoidal waves. The residual signal is further decomposed by the compound wavelet dictionary which consists of dyadic wavelet packets and implicit wavelet packets. It was verified through numerical simulations that the reconstructed signal in each wavelet subspace can avoid severe end distortions. The proposed method was applied to case studies of an experimental test with rub impact fault and an engineering test with blade crack fault. The analysis results demonstrate the proposed method can effectively suppress the SED effect in displacement signal analysis, and therefore enhance the performance of wavelet analysis in extracting weak fault features.

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