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

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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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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Multi-Fault Diagnosis of Gearbox Based on Improved Multipoint Optimal Minimum Entropy Deconvolution

Entropy ◽

10.3390/e20080611 ◽

2018 ◽

Vol 20 (8) ◽

pp. 611 ◽

Cited By ~ 2

Author(s):

Fuhe Yang ◽

Xingquan Shen ◽

Zhijian Wang

Keyword(s):

White Noise ◽

Signal To Noise Ratio ◽

Ensemble Empirical Mode Decomposition ◽

Original Signal ◽

Intrinsic Mode Functions ◽

Minimum Entropy ◽

Feature Extraction Method ◽

Mode Decomposition ◽

Fault Features ◽

Selection Of

Under complicated conditions, the extraction of a multi-fault in gearboxes is difficult to achieve. Due to improper selection of methods, leakage diagnosis or misdiagnosis will usually occur. Ensemble Empirical Mode Decomposition (EEMD) often causes energy leakage due to improper selection of white noise during signal decomposition. Considering that only a single fault cycle can be extracted when MOMED (Multipoint Optimal Minimum Entropy Deconvolution) is used, it is necessary to perform the sub-band processing of the compound fault signal. This paper presents an adaptive gearbox multi-fault-feature extraction method based on Improved MOMED (IMOMED). Firstly, EEMD decomposes the signal adaptively and selects the intrinsic mode functions with strong correlation with the original signal to perform FFT (Fast Fourier transform); considering the mode-mixing phenomenon of EEMD, reconstruct the intrinsic mode functions with the same timescale, and obtain several intrinsic mode functions of the same scale to improve the entropy of fault features. There is a lot of white noise in the original signal, and EEMD can improve the signal-to-noise ratio of the original signal. Finally, through the setting of different noise-reduction intervals to extract fault features through MOMED. The proposed method is compared with EEMD and VMD (Variational Mode Decomposition) to verify its feasibility.

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Fault diagnosis of rolling bearing’s early weak fault based on minimum entropy de-convolution and fast Kurtogram algorithm

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/0954406214564692 ◽

2014 ◽

Vol 229 (16) ◽

pp. 2890-2907 ◽

Cited By ~ 18

Author(s):

Hongchao Wang ◽

Jin Chen ◽

Guangming Dong

Keyword(s):

Feature Extraction ◽

Early Stage ◽

Ensemble Empirical Mode Decomposition ◽

Minimum Entropy ◽

Signal Attenuation ◽

Mode Decomposition ◽

Weak Fault ◽

Simulation Signal ◽

Environment Noise ◽

Fault Feature Extraction

The rolling bearing’s early stage fault feature is very weak for reasons of the signal attenuation phenomenon between the fault source and the sensor collecting the fault signal and the interference of environment noise such as the rotor rotating frequency and its harmonics and so on. The feature extraction of rolling bearing’s early weak fault is not only very important but also very hard. The minimum entropy de-convolution and Fast Kurtogram algorithm are combined in the paper for rolling bearing’s early stage weak fault feature extraction. The effect of transmission path is de-convolved effectively, and the impulses are clarified using minimum entropy de-convolution technique firstly. Then the obtained signal by minimum entropy de-convolution is handled by the Fast Kurtogram algorithm and an optimal filter is established. At last the envelope de-modulation is applied on the filtered signal and better feature extraction result is obtained compared with the other methods such as wavelet transform, frequency slice wavelet transformation and ensemble empirical mode decomposition. The effectiveness and advantages of the proposed method are verified through simulation signal and experiment.

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Combined Failure Diagnosis of Slewing Bearings Based on MCKD-CEEMD-ApEn

Shock and Vibration ◽

10.1155/2018/6321785 ◽

2018 ◽

Vol 2018 ◽

pp. 1-13 ◽

Cited By ~ 4

Author(s):

Fengtao Wang ◽

Chenxi Liu ◽

Wensheng Su ◽

Zhigang Xue ◽

Qingkai Han ◽

...

Keyword(s):

Approximate Entropy ◽

Ensemble Empirical Mode Decomposition ◽

Support Vector ◽

Intrinsic Mode Functions ◽

Heavy Load ◽

Heavy Equipment ◽

Mode Decomposition ◽

Fault Features ◽

Slewing Bearings ◽

Combined Failure

Large-size and heavy-load slewing bearings, which are mainly used in heavy equipment, comprise a subgroup of rolling bearings. Owing to the complexity of the structures and working conditions, it is quite challenging to effectively diagnose the combined failure and extract fault features of slewing bearings. In this study, a method was proposed to denoise and classify the combined failure of slewing bearings. First, after removing the mean, the vibration signals were denoised by maximum correlated kurtosis deconvolution. The signals were then decomposed into several intrinsic mode functions (IMFs) by complementary ensemble empirical mode decomposition (CEEMD). Appropriate IMFs were selected based on the correlation coefficient and kurtosis. The approximate entropy values of the selected IMFs were regarded as the characteristic vectors and then inputted into the support vector machine (SVM) based on multiclass classification for training. The practical combined failure signals of the 3 conditions were finally recognized and classified using SVMs. The study also compared the proposed method with 5 other methods to demonstrate the superiority and effectiveness of the proposed method.

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A Fault Feature Extraction Method for Rolling Bearing Based on Intrinsic Time-Scale Decomposition and AR Minimum Entropy Deconvolution

Shock and Vibration ◽

10.1155/2021/6673965 ◽

2021 ◽

Vol 2021 ◽

pp. 1-19

Author(s):

Jiakai Ding ◽

Liangpei Huang ◽

Dongming Xiao ◽

Lingli Jiang

Keyword(s):

Time Scale ◽

Vibration Signal ◽

Rolling Bearing ◽

Sample Entropy ◽

Minimum Entropy ◽

Feature Extraction Method ◽

Intrinsic Time ◽

Time Scale Decomposition ◽

Weak Fault ◽

Feature Frequency

It is very difficult to extract the feature frequency of the vibration signal of the rolling bearing early weak fault and in order to extract its feature frequency quickly and accurately. A method of extracting early weak fault vibration signal feature frequency of the rolling bearing by intrinsic time-scale decomposition (ITD) and autoregression (AR) minimum entropy deconvolution (MED) is proposed in this paper. Firstly, the original early weak fault vibration signal of the rolling bearing is decomposed by the ITD algorithm to proper rotations (PRs) with fault feature frequency. Then, the sample entropy value of each PR is calculated to find the largest PRs of the sample entropy. Finally, the AR-MED filtering algorithm is utilized to filter and reduce the noise of the largest PRs of the sample entropy value, and the early weak fault vibration signal feature frequency of the rolling bearing is accurately extracted. The results show that the ITD-AR-MED method can extract the early weak fault vibration signal feature frequency of the rolling bearing accurately.

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Improvement of Fast Kurtogram Combined with PCA for Multiple Weak Fault Features Extraction

Processes ◽

10.3390/pr8091059 ◽

2020 ◽

Vol 8 (9) ◽

pp. 1059

Author(s):

Yongxing Song ◽

Jingting Liu ◽

Linhua Zhang ◽

Dazhuan Wu

Keyword(s):

Principal Component Analysis ◽

Signal To Noise Ratio ◽

Principal Component ◽

Component Analysis ◽

Rotating Machinery ◽

Features Extraction ◽

Signal To Noise ◽

Fault Features ◽

Weak Fault ◽

Noise Ratio

Demodulation plays an important role in fault feature extraction for rotating machinery. The fast kurtogram method was proved to be effective for rotating machinery demodulation. However, the demodulation effectiveness of fast kurtogram was poor for multiple fault features extraction under low signal-to-noise ratio. In this paper, an improved method of fast kurtogram, called P-kurtogram, is presented. The proposed method extracted the multiple weak fault features from multiple envelope signals-based principal component analysis. Compared with extracting features from one envelope signal of fast kurtogram, P-kurtogram showed a better demodulation performance for multiple faults. Combined with principal component analysis method, the proposed method also showed a good performance under low signal-to-noise ratio(SNR). By simulation analysis, the P-kurtogram method showed good performance for multiple modulation features extraction and robust performance in demodulation under low SNR. Then, the proposed method was demonstrated by applications of bearing faults detection and propeller detection. The results verified that the P-kurtogram has a better demodulation performance than fast kurtogram for multiple weak fault features extraction, especially under low signal-to-noise ratio. The proposed method provides a reliable basis for multiple weak fault features extraction of rotating machinery.

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

Entropy ◽

10.3390/e20110850 ◽

2018 ◽

Vol 20 (11) ◽

pp. 850 ◽

Cited By ~ 6

Author(s):

Huer Sun ◽

Chao Wu ◽

Xiaohua Liang ◽

Qunfeng Zeng

Keyword(s):

Noise Suppression ◽

Optimal Filter ◽

Permutation Entropy ◽

Signal Denoising ◽

Minimum Entropy ◽

Multiple Faults ◽

Fault Features ◽

Weak Fault ◽

The Impact ◽

Fault Source

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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MultiStep Ahead Forecasting for Hourly PM10 and PM2.5 Based on Two-Stage Decomposition Embedded Sample Entropy and Group Teacher Optimization Algorithm

Atmosphere ◽

10.3390/atmos12010064 ◽

2021 ◽

Vol 12 (1) ◽

pp. 64

Author(s):

Feng Jiang ◽

Yaqian Qiao ◽

Xuchu Jiang ◽

Tianhai Tian

Keyword(s):

Optimization Algorithm ◽

Hybrid Approach ◽

Forecast Accuracy ◽

Ensemble Empirical Mode Decomposition ◽

Sample Entropy ◽

Air Pollutant ◽

Intrinsic Mode Functions ◽

Concentration Data ◽

Two Stage ◽

Pm10 And Pm2.5

The randomness, nonstationarity and irregularity of air pollutant data bring difficulties to forecasting. To improve the forecast accuracy, we propose a novel hybrid approach based on two-stage decomposition embedded sample entropy, group teaching optimization algorithm (GTOA), and extreme learning machine (ELM) to forecast the concentration of particulate matter (PM10 and PM2.5). First, the improvement complementary ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is employed to decompose the concentration data of PM10 and PM2.5 into a set of intrinsic mode functions (IMFs) with different frequencies. In addition, wavelet transform (WT) is utilized to decompose the IMFs with high frequency based on sample entropy values. Then the GTOA algorithm is used to optimize ELM. Furthermore, the GTOA-ELM is utilized to predict all the subseries. The final forecast result is obtained by ensemble of the forecast results of all subseries. To further prove the predictable performance of the hybrid approach on air pollutants, the hourly concentration data of PM2.5 and PM10 are used to make one-step-, two-step- and three-step-ahead predictions. The empirical results demonstrate that the hybrid ICEEMDAN-WT-GTOA-ELM approach has superior forecasting performance and stability over other methods. This novel method also provides an effective and efficient approach to make predictions for nonlinear, nonstationary and irregular data.

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