Rolling Bearing Fault Diagnosis Approach Research Articles

Cross-domain fault diagnosis method for rolling bearings based on contrastive universal domain adaptation

To address the unknown spatial relationship between source and target domain labels, which leads to poor fault diagnosis accuracy, a contrastive universal domain adaptation model and rolling bearing fault diagnosis approach are proposed. The approach introduces bootstrap your own latent network to mine the data-specific structure of the target domain and proposes rejecting unknown class samples using an entropy separation strategy. Simultaneously, a source class weighting mechanism is designed to improve the transferable semantics augmentation method by assigning various class-level weights to source categories, which improves the alignment of the feature distributions in the shared label space to further construct fault diagnosis models. Experimental validation on two rolling bearing datasets confirmed the superior fault diagnosis accuracy of the proposed method under diverse working conditions.

The Method of Rolling Bearing Fault Diagnosis Based on Multi-Domain Supervised Learning of Convolution Neural Network

The rolling bearing is a critical part of rotating machinery and its condition determines the performance of industrial equipment; it is necessary to detect rolling bearing faults as early as possible. The traditional methods of fault diagnosis are not efficient and are time-consuming. With the help of deep learning, the convolution neural network (CNN) plays a huge role in the data-driven methods of bearing fault diagnosis. However, the vibration signal is non-stationary, contains high noise, and is one-dimensional, which is difficult to analyze directly by the CNN model. Considering the multi-domain learning as an advantage of deep learning, this paper proposes a novel rolling bearing fault diagnosis approach using an improved one-dimensional (1D) and two-dimensional (2D) convolution neural network (CNN) of two-domain information learning. The constructed fault diagnosis model combining 1D and 2D CNN extracts the fault features from the two-domain information of bearing fault samples. The padding and dropout technology are utilized to fully extract features from the raw data and reduce over-fitting. To prove the validity of the proposed method, this paper performs two tests with two bearing datasets, the Case Western Reserve University (CWRU) bearing dataset and the Dalian University of Technology (DUT) vibration laboratory dataset. The experimental results show that our proposed method achieves high recognition accuracy of rolling bearing fault states via two-domain learning of monitoring data, and there is no manual experience necessary. Vibration data under strong noise were also used to test the method, and the results show the superiority and robustness of the proposed method.

A Multi-scale Fuzzy Measure Entropy and Infinite Feature Selection Based Approach for Rolling Bearing Fault Diagnosis

Fuzzy measure entropy (FuzzyMEn) is a recently improved non-linear dynamic parameter for evaluating the signals’ complexity. In comparison with fuzzy entropy (FuzzyEn), which only emphasizes the local characteristics of the signal but neglects its global trend, FuzzyMEn can reflect not only the local but also the global characteristics of the signal. Therefore, by calculating the FuzzyMEn values in different scales, the multi-scale fuzzy measure entropy (MFME) method is put forward in this paper and used for extracting the fault features from vibration signals of rolling bearing. After the feature extraction, the newly developed infinite feature selection (Inf-FS) method is employed to choose the most representative features from the original ones of high dimension. Finally, a new rolling bearing fault diagnosis approach is presented based on MFME, Inf-FS and support vector machine (SVM). The experimental analysis indicates that the presented approach can realize the rolling bearing fault diagnosis effectively.

Rolling Bearing Diagnosis Based on Composite Multiscale Weighted Permutation Entropy.

In this paper, composite multiscale weighted permutation entropy (CMWPE) is proposed to evaluate the complexity of nonlinear time series, and the advantage of the CMWPE method is verified through analyzing the simulated signal. Meanwhile, considering the complex nonlinear dynamic characteristics of fault rolling bearing signal, a rolling bearing fault diagnosis approach based on CMWPE, joint mutual information (JMI) feature selection, and k-nearest-neighbor (KNN) classifier (CMWPE-JMI-KNN) is proposed. For CMWPE-JMI-KNN, CMWPE is utilized to extract the fault rolling bearing features, JMI is applied for sensitive features selection, and KNN classifier is employed for identifying different rolling bearing conditions. Finally, the proposed CMWPE-JMI-KNN approach is used to analyze the experimental dataset, the analysis results indicate the proposed approach could effectively identify different fault rolling bearing conditions.

Study on rolling bearing fault diagnosis approach based on improved generalized fractal box-counting dimension and adaptive gray relation algorithm

Aiming at the nonlinear and nonstationary characteristics of bearing vibration signals as well as the complexity of condition-indicating information distribution in the signals, a novel rolling element bearing fault diagnosis approach based on improved generalized fractal box-counting dimension and adaptive gray relation algorithm was proposed in this article. First, an improved generalized fractal box-counting dimension algorithm was developed to extract the characteristic vectors of fault features from the bearing vibration signals, to offer more useful and distinguishing information imaging different bearing health status in comparison with traditional fractal box-counting dimension. After feature extraction by improved generalized fractal box-counting dimension algorithm, an adaptive gray relation algorithm, in which the concept of weight coefficient and adaptive distinguishing coefficient was introduced into the calculation of the relation degree, was employed to fulfill an intelligent bearing fault diagnosis. The experimental results demonstrate that the proposed approach can more effectively and accurately identify different bearing fault types as well as severities compared with the existing intelligent methods, and it can solve the learning problem with an extremely small number of samples.

Rolling Bearing Fault Diagnosis Based on Wavelet Packet Decomposition and Multi-Scale Permutation Entropy

This paper presents a rolling bearing fault diagnosis approach by integrating wavelet packet decomposition (WPD) with multi-scale permutation entropy (MPE). The approach uses MPE values of the sub-frequency band signals to identify faults appearing in rolling bearings. Specifically, vibration signals measured from a rolling bearing test system with different defect conditions are decomposed into a set of sub-frequency band signals by means of the WPD method. Then, each sub-frequency band signal is divided into a series of subsequences, and MPEs of all subsequences in corresponding sub-frequency band signal are calculated. After that, the average MPE value of all subsequences about each sub-frequency band is calculated, and is considered as the fault feature of the corresponding sub-frequency band. Subsequently, MPE values of all sub-frequency bands are considered as input feature vectors, and the hidden Markov model (HMM) is used to identify the fault pattern of the rolling bearing. Experimental study on a data set from the Case Western Reserve University bearing data center has shown that the presented approach can accurately identify faults in rolling bearings.

Mutual Information-Assisted Wavelet Function Selection for Enhanced Rolling Bearing Fault Diagnosis

This paper presents an enhanced rolling bearing fault diagnosis approach, based on optimized wavelet packet transform (WPT) assisted with quantitative wavelet function selection. Mutual information is utilized as a quantitative measure to select the most suitable wavelet function for the WPT-based vibration analysis. Energy features from coefficients of an optimal set of orthogonal wavelet subspaces which resulted from the WPT-based vibration analysis are input to different classifiers. The fault states of the rolling bearings can then be identified. Experiment studies conducted on a rolling bearing test system have verified the effectiveness of the proposed approach for rolling bearing fault diagnosis.

Rolling bearing fault diagnosis approach using probabilistic principal component analysis denoising and cyclic bispectrum

A new approach for bearing fault diagnosis is proposed based on probabilistic principal component analysis and cyclic bispectrum with optimal cycle frequency. Generally, there are two procedures to accomplish the bearings fault diagnosis. The first one is signal denoising using probabilistic principal component analysis, which transfers the original signal to a principal component model. This procedure can keep a useful component of the signal completely and increase the signal-to-noise ratio. The second employs cyclic bispectrum to extract fault frequency. Because the cyclic frequency is a vital parameter, the optical cyclic frequency is investigated and found to be equal to the bearing center frequency. The effectiveness of the proposed method is demonstrated by numerical simulation and experimental investigation of a rolling bearing with an outer race fault. The authors’ analyses also indicate that the proposed method can be used for fault diagnosis of rolling bearings.

Rolling Bearing Fault Diagnosis Based on Second Generation Wavelet Denoising and Improved EEMD

A new rolling bearing fault diagnosis approach is proposed. The original vibration signal is purified using the second generation wavelet denoising. The purified signal is further decomposed by an improved ensemble empirical mode decomposition (EEMD) method. A new selection criterion, including correlation analysis and the first two intrinsic mode functions (IMFs) with the maximum energy, is put forward to eliminate the pseudo low-frequency components. Experimental investigation show that the rolling bearing fault features can be effectively extracted.

Rolling Bearing Fault Diagnosis Approach Based on PPCA Denoising and Cyclic Bispectrum Method

A new method for bearing fault diagnosis is proposed based on Probabilistic Principal Component Analysis (PPCA) and Cyclic Bispectrum (CB). The first procedure is signal de-noised using PPCA and the second procedure is the CB analysis. The effectiveness of the proposed method is demonstrated by numerical simulation and experimental investigation of a rolling bearing with outer race fault.

Multiscale Permutation Entropy Based Rolling Bearing Fault Diagnosis

A new rolling bearing fault diagnosis approach based on multiscale permutation entropy (MPE), Laplacian score (LS), and support vector machines (SVMs) is proposed in this paper. Permutation entropy (PE) was recently proposed and defined to measure the randomicity and detect dynamical changes of time series. However, for the complexity of mechanical systems, the randomicity and dynamic changes of the vibration signal will exist in different scales. Thus, the definition of MPE is introduced and employed to extract the nonlinear fault characteristics from the bearing vibration signal in different scales. Besides, the SVM is utilized to accomplish the fault feature classification to fulfill diagnostic procedure automatically. Meanwhile, in order to avoid a high dimension of features, the Laplacian score (LS) is used to refine the feature vector by ranking the features according to their importance and correlations with the main fault information. Finally, the rolling bearing fault diagnosis method based on MPE, LS, and SVM is proposed and applied to the experimental data. The experimental data analysis results indicate that the proposed method could identify the fault categories effectively.

A rolling bearing fault diagnosis approach based on LCD and fuzzy entropy

When the fault happens, the complexity of the intrinsic oscillation from the mechanical system will change. Fuzzy entropy (FuzzyEn), which is defined to measure the complexity and self-similarity of the time series, can be utilized to measure the complexity of vibration signal and reflect changes of complexity of the intrinsic oscillation. Since the changes distribute in different scales, a new non-stationary signal analysis method, local characteristic-scale decomposition (LCD), is proposed and used to decompose the vibration signal adaptively into series of intrinsic scale components (ISC) in different scales. And then a new rolling bearing fault diagnosis approach based on LCD and FuzzyEn is proposed. Namely, firstly, by using the LCD rolling bearing vibration signal is decomposed into numbers of ISCs; then the FuzzyEns of the first few ISCs that contain main failure information are extracted. Thirdly, the FuzzyEns obtained are taken as the inputs to the adaptive neuro-fuzzy inference systems (ANFIS) classifier. Finally, the proposed method is applied to experimental data, and the analysis results show that the proposed method performs effectively for the rolling bearing fault diagnosis.