Mode determination in variational mode decomposition and its application in fault diagnosis of rolling element bearings

Ensemble empirical mode decomposition (EEMD) represents a valuable aid in empirical mode decomposition (EMD) and has been widely used in fault diagnosis of rolling element bearings. However, the intrinsic mode functions (IMFs) generated by EEMD often contain residual noise. In addition, adding different white Gaussian noise to the signal to be analyzed probably produces a different number of IMFs, and different number of IMFs makes difficult the averaging. To alleviate these two drawbacks, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) was previously presented. Utilizing the advantages of CEEMDAN in extracting weak characteristics from noisy signals, a new fault diagnosis method of rolling element bearings based on CEEMDAN is proposed. With this method, a particular noise is added at each stage and after each IMF extraction, a unique residue is computed. In this way, this method solves the problem of the final averaging and obtains IMFs with less noise. A simulated signal is used to illustrate the effectiveness of the proposed method, and the decomposition results show that the method obtains more accurate IMFs than the EEMD. To further demonstrate the proposed method, it is applied to fault diagnosis of locomotive rolling element bearings. The diagnosis results prove that the method based on CEEMDAN may reveal the fault characteristic information of rolling element bearings better.

Download Full-text

Fault Diagnosis of Rolling Element Bearings with a Two-Step Scheme Based on Permutation Entropy and Random Forests

Entropy ◽

10.3390/e21010096 ◽

2019 ◽

Vol 21 (1) ◽

pp. 96 ◽

Cited By ~ 9

Author(s):

Xiaoming Xue ◽

Chaoshun Li ◽

Suqun Cao ◽

Jinchao Sun ◽

Liyan Liu

Keyword(s):

Random Forests ◽

Classification Model ◽

Permutation Entropy ◽

Rolling Element Bearings ◽

Variational Mode Decomposition ◽

Statistical Classification ◽

Vibration Signals ◽

Mode Decomposition ◽

Single Scale ◽

Rolling Element

This study presents a two-step fault diagnosis scheme combined with statistical classification and random forests-based classification for rolling element bearings. Considering the inequality of features sensitivity in different diagnosis steps, the proposed method utilizes permutation entropy and variational mode decomposition to depict vibration signals under single scale and multiscale. In the first step, the permutation entropy features on the single scale of original signals are extracted and the statistical classification model based on Chebyshev’s inequality is constructed to detect the faults with a preliminary acquaintance of the bearing condition. In the second step, vibration signals with fault conditions are firstly decomposed into a collection of intrinsic mode functions by using variational mode decomposition and then multiscale permutation entropy features derived from each mono-component are extracted to identify the specific fault types. In order to improve the classification ability of the characteristic data, the out-of-bag estimation of random forests is firstly employed to reelect and refine the original multiscale permutation entropy features. Then the refined features are considered as the input data to train the random forests-based classification model. Finally, the condition data of bearings with different fault conditions are employed to evaluate the performance of the proposed method. The results indicate that the proposed method can effectively identify the working conditions and fault types of rolling element bearings.

Download Full-text

Selecting effective intrinsic mode functions of empirical mode decomposition and variational mode decomposition using dynamic time warping algorithm for rolling element bearing fault diagnosis

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331218790788 ◽

2018 ◽

Vol 41 (7) ◽

pp. 1923-1932 ◽

Cited By ~ 9

Author(s):

Prem Shankar Kumar ◽

Lakshmi Annamalai Kumaraswamidhas ◽

Swarup Kumar Laha

Keyword(s):

Fault Diagnosis ◽

Dynamic Time Warping ◽

Rolling Element Bearing ◽

Variational Mode Decomposition ◽

Intrinsic Mode Functions ◽

Time Warping ◽

Mode Decomposition ◽

Rolling Element ◽

Element Bearing ◽

Dynamic Time

Empirical Mode Decomposition (EMD) and Variational Mode Decomposition (VMD) are data-driven self-adaptive signal processing methods to decompose a complex signal into different modes of separate spectral bands, in to a number of Intrinsic Mode Functions (IMFs). While the EMD extracts modes recursively and empirically, the VMD extracts modes non-recursively and concurrently. In this paper, both the EMD and the VMD have been applied to examine their efficacy in fault diagnosis of rolling element bearing. However, all the IMFs do not contain necessary information regarding fault characteristic signature of the bearing. In order to select the effective IMF, the Dynamic Time Warping (DTW) algorithm has been employed here, which gives a measurement of similarity index between two signals. Also, correlation analysis has been carried out to select the appropriate IMFs. Finally, out of the selected IMFs, bearing characteristic fault frequencies have been determined with the envelope spectrum.

Download Full-text

Mode Selection in Variational Mode Decomposition and Its Application in Fault Diagnosis of Rolling Element Bearing

Reliability, Safety and Hazard Assessment for Risk-Based Technologies - Lecture Notes in Mechanical Engineering ◽

10.1007/978-981-13-9008-1_56 ◽

2019 ◽

pp. 663-670

Author(s):

Pradip Yadav ◽

Shivani Chauhan ◽

Prashant Tiwari ◽

S. H. Upadhyay ◽

Pawan Kumar Rakesh

Keyword(s):

Fault Diagnosis ◽

Mode Selection ◽

Rolling Element Bearing ◽

Variational Mode Decomposition ◽

Mode Decomposition ◽

Rolling Element ◽

Element Bearing

Download Full-text

Adaptive Multiclass Mahalanobis Taguchi System for Bearing Fault Diagnosis under Variable Conditions

Sensors ◽

10.3390/s19010026 ◽

2018 ◽

Vol 19 (1) ◽

pp. 26 ◽

Cited By ~ 5

Author(s):

Ning Wang ◽

Zhipeng Wang ◽

Limin Jia ◽

Yong Qin ◽

Xinan Chen ◽

...

Keyword(s):

Fault Diagnosis ◽

Binary Classification ◽

Operating Conditions ◽

Normal Operation ◽

Measurement Scale ◽

Rolling Element Bearings ◽

Classification Problems ◽

Mode Decomposition ◽

Rolling Element ◽

Value Decomposition

Bearings are vital components in industrial machines. Diagnosing the fault of rolling element bearings and ensuring normal operation is essential. However, the faults of rolling element bearings under variable conditions and the adaptive feature selection has rarely been discussed until now. Thus, it is essential to develop a practicable method to put forward the disposal of the fault under variable conditions. Considering these issues, this paper uses the method based on the Mahalanobis Taguchi System (MTS), and overcomes two shortcomings of MTS: (1) MTS is an effective tool to classify faults and has strong robustness to operating conditions, but it can only handle binary classification problems, and this paper constructs the multiclass measurement scale to deal with multi-classification problems. (2) MTS can determine important features, but uses the hard threshold to select the features, and this paper selects the proper feature sequence instead of the threshold to overcome the lesser adaptivity of the threshold configuration for signal-to-noise gain. Hence, this method proposes a novel method named adaptive Multiclass Mahalanobis Taguchi system (aMMTS), in conjunction with variational mode decomposition (VMD) and singular value decomposition (SVD), and is employed to diagnose the faults under the variable conditions. Finally, this method is verified by using the signal data collected from Case Western Reserve University Bearing Data Center. The result shows that it is accurate for bearings fault diagnosis under variable conditions.

Download Full-text