Research on fault detection of rolling bearings in press line by a new morphological filter based on diagonal slice spectrum lifting

Measurement ◽  
2021 ◽  
pp. 110385
Author(s):  
Tong Wang ◽  
Changzheng Chen ◽  
Yuanqing Luo ◽  
Shaohui Huang
Keyword(s):  
Fault Detection ◽  
Morphological Filter ◽  
Rolling Bearings

scholarly journals Research on a Nonlinear Dynamic Incipient Fault Detection Method for Rolling Bearings

2020 ◽  
Vol 10 (7) ◽  
pp. 2443
Author(s):  
Huaitao Shi ◽  
Jin Guo ◽  
Xiaotian Bai ◽  
Lei Guo ◽  
Zhenpeng Liu ◽  
...  
Keyword(s):  
Fault Detection ◽  
Nonlinear Dynamic ◽  
Detection Method ◽  
Normal Operation ◽  
Rolling Bearings ◽  
Detection Delay ◽  
Detection Technology ◽  
Non Linear ◽  
Incipient Fault ◽  
Incipient Fault Detection

The incipient fault detection technology of rolling bearings is the key to ensure its normal operation and is of great significance for most industrial processes. However, the vibration signals of rolling bearings are a set of time series with non-linear and timing correlation, and weak incipient fault characteristics of rolling bearings bring about obstructions for the fault detection. This paper proposes a nonlinear dynamic incipient fault detection method for rolling bearings to solve these problems. The kernel function and the moving window algorithm are used to establish a non-linear dynamic model, and the real-time characteristics of the system are obtained. At the same time, the deep decomposition method is used to extract weak fault characteristics under the strong noise, and the incipient failures of rolling bearings are detected. Finally, the validity and feasibility of the scheme are verified by two simulation experiments. Experimental results show that the fault detection rate based on the proposed method is higher than 85% for incipient fault of rolling bearings, and the detection delay is almost zero. Compared with the detection performance of traditional methods, the proposed nonlinear dynamic incipient fault detection method is of better accuracy and applicability.

scholarly journals A Comprehensive Fault Diagnosis Method for Rolling Bearings Based on Refined Composite Multiscale Dispersion Entropy and Fast Ensemble Empirical Mode Decomposition

Entropy ◽  
2019 ◽  
Vol 21 (7) ◽  
pp. 680 ◽  
Author(s):  
Zhang ◽  
Zhou
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Fault Classification ◽  
Rolling Bearings ◽  
Feature Vectors ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Candidate Feature

This study presents a comprehensive fault diagnosis method for rolling bearings. The method includes two parts: the fault detection and the fault classification. In the stage of fault detection, a threshold based on refined composite multiscale dispersion entropy (RCMDE) at a local maximum scale is defined to judge the health state of rolling bearings. If the bearing is in fault, a generalized multi-scale feature extraction method is developed to fully extract fault information by combining fast ensemble empirical mode decomposition (FEEMD) and RCMDE. Firstly, the fault vibration signals are decomposed into a set of intrinsic mode functions (IMFs) by FEEMD. Secondly, the RCMDE value of multiple IMFs is calculated to generate a candidate feature pool. Then, the maximum-relevance and minimum-redundancy (mRMR) approach is employed to select the sensitive features from the candidate feature pool to construct the final feature vectors, and the final feature vectors are fed into random forest (RF) classifier to identify different fault working conditions. Finally, experiments and comparative research are carried out to verify the performance of the proposed method. The results show that the proposed method can detect faults effectively. Meanwhile, it has a more robust and excellent ability to identify different fault types and severity compared with other conventional approaches.

scholarly journals Multiple Fault Detection of Rolling Bearing through Ensemble Empirical Mode Decomposition of Vibration Signal

2019 ◽  
Vol 9 (2) ◽  
pp. 2724-2726
Keyword(s):  
Fault Detection ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Data Driven ◽  
Rolling Bearings ◽  
Vibration Signals ◽  
Multiple Faults ◽  
Mode Decomposition

Generally, two or more faults occur simultaneously in the bearings. These Compound Faults (CF) in bearing, are most difficult type of faults to detect, by any data-driven method including machine learning. Hence, it is a primary requirement to decompose the fault vibration signals logically, so that frequencies can be grouped in parts. Empirical Mode Decomposition (EMD) is one of the simplest techniques of decomposition of signals. In this paper we have used Ensemble Empirical Mode Decomposition (EEMD) technique for compound fault detection/identification. Ensembled Empirical Mode Decomposition is found useful, where a white noise helps to detect the bearing frequencies. The graphs show clearly the capability of EEMD to detect the multiple faults in rolling bearings.

scholarly journals Weak Fault Detection for Rolling Bearings in Varying Working Conditions through the Second-Order Stochastic Resonance Method with Barrier Height Optimization

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Huaitao Shi ◽  
Yangyang Li ◽  
Peng Zhou ◽  
Shenghao Tong ◽  
Liang Guo ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Fault Detection ◽  
Barrier Height ◽  
Stochastic Resonance ◽  
Working Conditions ◽  
Potential Well ◽  
Rolling Bearings ◽  
Input Signals ◽  
Weak Fault ◽  
Fault Feature Extraction

The stochastic resonance (SR) method is widely applied to fault feature extraction of rotary machines, which is capable of improving the weak fault detection performance by energy transformation through the potential well function. The potential well functions are mostly set fixed to reduce computational complexity, and the SR methods with fixed potential well parameters have better performances in stable working conditions. When the fault frequency changes in variable working conditions, the signal processing effect becomes different with fixed parameters, leading to errors in fault detection. In this paper, an underdamped second-order adaptive general variable-scale stochastic resonance (USAGVSR) method with potential well parameters’ optimization is put forward. For input signals with different fault frequencies, the potential well parameters related to the barrier height are figured out and optimized through the ant colony algorithm. On this basis, further optimization is carried out on undamped factor and step size for better fault detection performance. Cases with diverse fault types and in different working conditions are studied, and the performance of the proposed method is validated through experiments. The results testify that this method has better performances of weak fault feature extraction and can accurately identify different fault types in the input signals. The method proves to be effective in the weak fault extraction and classification and has a good application prospect in rolling bearings’ fault feature recognition.

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