Rolling bearing degradation condition clustering using multidimensional degradation feature and Gath–Geva fuzzy clustering algorithm

2020 ◽  
pp. 107754632095495
Author(s):  
Bing Wang ◽  
Xiong Hu ◽  
Tao X Mei ◽  
Sun D Jian ◽  
Wang Wei
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Chaotic Map ◽  
Vibration Signal ◽  
Degradation Process ◽  
Rolling Bearing ◽  
Performance Degradation ◽  
Time Parameter ◽  
Mechanical Equipment ◽  
Time Dimension

In allusion to the issue of rolling bearing degradation feature extraction and degradation condition clustering, a logistic chaotic map is introduced to analyze the advantages of C0 complexity and a technique based on a multidimensional degradation feature and Gath–Geva fuzzy clustering algorithmic is proposed. The multidimensional degradation feature includes C0 complexity, root mean square, and curved time parameter which is more in line with the performance degradation process. Gath–Geva fuzzy clustering is introduced to divide different conditions during the degradation process. A rolling bearing lifetime vibration signal from intelligent maintenance system bearing test center was introduced for instance analysis. The results show that C0 complexity is able to describe the degradation process and has advantages in sensitivity and calculation speed. The introduced degradation indicator curved time parameter can reflect the agglomeration character of the degradation condition at time dimension, which is more in line with the performance degradation pattern of mechanical equipment. The Gath–Geva fuzzy clustering algorithmic is able to cluster degradation condition of mechanical equipment such as bearings accurately.

A degradation phase division technique based on Gath–Geva fuzzy clustering

2020 ◽  
Vol 26 (15-16) ◽  
pp. 1147-1154
Author(s):  
Bing Wang ◽  
Wang Wei ◽  
Xiong Hu ◽  
Dejian Sun
Keyword(s):  
Fuzzy Clustering ◽  
Chaotic Map ◽  
Vibration Signal ◽  
Degradation Process ◽  
Rolling Bearing ◽  
Performance Degradation ◽  
Mechanical Equipment ◽  
Spectral Entropy ◽  
Variation Pattern ◽  
Intelligent Maintenance

In allusion to the issue of degradation feature extraction and degradation phase division, a logistic chaotic map is used to study the variation pattern of spectral entropy, and a technique based on Gath–Geva fuzzy clustering is proposed. The degradation features include spectral entropy, root mean square, and “curved time,” which are more in line with the performance degradation process than degradation time. Gath–Geva fuzzy clustering is introduced to divide different phases in the degradation process. The rolling bearing lifetime vibration signal from the intelligent maintenance systems (IMS) bearing test center was introduced for instance analysis. The results show that spectral entropy is able to effectively describe the complexity variation pattern in the performance degradation process and has some advantages in sensitivity and calculation speed. The introduced “curved time” is able to reflect the agglomeration character of the degradation condition on a time scale, which is more in line with the performance degradation pattern of mechanical equipment. Gath–Geva fuzzy clustering is able to divide the degradation phase of mechanical equipment such as bearings accurately.

scholarly journals Bearing condition degradation assessment based on basic scale entropy and Gath-Geva fuzzy clustering

2018 ◽  
Vol 10 (10) ◽  
pp. 168781401880353 ◽  
Author(s):  
Bing Wang ◽  
Xiong Hu ◽  
Dejian Sun ◽  
Wei Wang
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithms ◽  
Three Dimensional ◽  
Degradation Process ◽  
Performance Degradation ◽  
Mechanical Equipment ◽  
Time Aggregation ◽  
Fuzzy Clustering Method ◽  
Basic Scale ◽  
Fuzzy C Means Clustering

A method based on basic scale entropy and Gath-Geva fuzzy clustering is proposed in order to solve the issue of bearing degradation condition recognition. The evolution rule of basic scale entropy for bearing in performance degradation process is analyzed first, and the monotonicity and sensitivity of basic scale entropy are emphasized. Considering the continuity of the bearing degradation condition at the time scale, three-dimensional degradation eigenvectors are constructed including basic scale entropy, root mean square, and degradation time, and then, Gath-Geva fuzzy clustering method is used to divide different conditions in performance degradation process, thus realizing performance degradation recognition for bearing. Bearing whole lifetime data from IEEE PHM 2012 is adopted in application and discussion, and fuzzy c-means clustering and Gustafson–Kessel clustering algorithms are analyzed for comparison. The results show that the proposed basic scale entropy-Gath-Geva method has better clustering effect and higher time aggregation than the other two algorithms and is able to provide an effective way for mechanical equipment performance degradation recognition.

Performance degradation analysis of solid lubricated bearings based on Laplace wavelet filter and self-organizing map

2013 ◽  
Vol 228 (10) ◽  
pp. 1743-1753 ◽  
Author(s):  
Chao Zhang ◽  
Shaoping Wang
Keyword(s):  
Feature Vector ◽  
Vibration Signal ◽  
Degradation Process ◽  
Performance Degradation ◽  
Friction Torque ◽  
Self Organizing Map ◽  
Wavelet Filter ◽  
Degradation Analysis ◽  
Space Mechanisms ◽  
Self Organizing

Solid lubricated bearings are commonly used in space mechanisms and other appliances, and their reliability analysis has drawn more and more attention. This paper focuses on the performance degradation analysis of solid lubricated bearings. Based on the vibration and friction torque signal of solid lubricated bearings, Laplace wavelet filter is adopted to process vibration signal and feature vector is constructed by calculating time-domain parameters of filtered vibration signal and original friction torque signal. Self-organizing map is then adopted to analyze the performance degradation based on extracted feature vectors. Experimental results show that this method can describe performance degradation process effectively.

Feature Extraction of Rolling Bearing Fault Diagnosis

2012 ◽  
Vol 190-191 ◽  
pp. 993-997
Author(s):  
Li Jie Sun ◽  
Li Zhang ◽  
Yong Bo Yang ◽  
Da Bo Zhang ◽  
Li Chun Wu
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Attribute Reduction ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Mechanical Equipment ◽  
Characteristic Parameters ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Experimental Trials

Mechanical equipment fault diagnosis occupies an important position in the industrial production, and feature extraction plays an important role in fault diagnosis. This paper analyzes various methods of feature extraction in rolling bearing fault diagnosis and classifies them into two big categories, which are methods of depending on empirical rules and experimental trials and using objective methods for screening. The former includes five methods: frequency as the characteristic parameters, multi-sensor information fusion method, rough set attribute reduction method, "zoom" method and vibration signal as the characteristic parameters. The latter includes two methods: sensitivity extraction and data mining methods to select attributes. Currently, selection methods of feature parameters depend heavily on empirical rules and experimental trials, thus extraction results are be subjected to restriction from subjective level, feature extraction in the future will develop toward objective screening direction.

scholarly journals An Integrated Cumulative Transformation and Feature Fusion Approach for Bearing Degradation Prognostics

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Lixiang Duan ◽  
Fei Zhao ◽  
Jinjiang Wang ◽  
Ning Wang ◽  
Jiwang Zhang
Keyword(s):  
Feature Fusion ◽  
Model Performance ◽  
Principal Component ◽  
Vibration Signal ◽  
Degradation Process ◽  
Rolling Bearing ◽  
Kernel Principal Component Analysis ◽  
Machine Model ◽  
Monotonic Trend ◽  
Learning Machine

Aimed at degradation prognostics of a rolling bearing, this paper proposed a novel cumulative transformation algorithm for data processing and a feature fusion technique for bearing degradation assessment. First, a cumulative transformation is presented to map the original features extracted from a vibration signal to their respective cumulative forms. The technique not only makes the extracted features show a monotonic trend but also reduces the fluctuation; such properties are more propitious to reflect the bearing degradation trend. Then, a new degradation index system is constructed, which fuses multidimensional cumulative features by kernel principal component analysis (KPCA). Finally, an extreme learning machine model based on phase space reconstruction is proposed to predict the degradation trend. The model performance is experimentally validated with a whole-life experiment of a rolling bearing. The results prove that the proposed method reflects the bearing degradation process clearly and achieves a good balance between model accuracy and complexity.

Multivariable least squares support vector machine with time integral operator for the prediction of bearing performance degradation

2018 ◽  
Vol 233 (7) ◽  
pp. 2478-2490 ◽  
Author(s):  
Yao Zhang ◽  
Youguang Zhou ◽  
Gang Tang ◽  
Huaqing Wang
Keyword(s):  
Support Vector Machine ◽  
Integral Operator ◽  
Least Squares ◽  
Degradation Process ◽  
Rolling Bearing ◽  
Performance Degradation ◽  
Time Shift ◽  
Support Vector ◽  
Multiple Perspectives ◽  
Time Integral

The prediction of performance degradation is significant for the health monitoring of rolling bearing, which helps to greatly reduce the loss caused by potential faults in the entire life cycle of rotating machinery. As a new method of machine learning based on statistical learning theory, least squares support vector machine is developed and has achieved good results. However, it lacks the description of the time-sum effect and delay characteristics, which cannot fully describe the performance degradation process. To overcome the problem, a new time shift least squares support vector machine with integral operator is proposed. What is more, multivariable prediction model is introduced to describe the process from multiple perspectives. In this model, different features are extracted to construct sample pairs through a moving window. Then these features are decomposed in time domain using a set of orthogonal basis functions to simplify computation. Furthermore, the model adaptability is also improved through an iterative updating strategy. Bearing fault experiments show that the proposed model outperforms the general method.

Performance degradation assessment of slewing bearings based on deep auto-encoder and optimized particle filtering

2019 ◽  
Vol 25 (17) ◽  
pp. 2380-2394
Author(s):  
Yubin Pan ◽  
Rongjing Hong ◽  
Jie Chen ◽  
Weiwei Wu
Keyword(s):  
Particle Filtering ◽  
Health Management ◽  
Large Data ◽  
Vibration Signal ◽  
Degradation Process ◽  
Performance Degradation ◽  
Remaining Useful Life ◽  
Heavy Load ◽  
Reconstructed Signal ◽  
Slewing Bearings

Due to the low speed and heavy load conditions of slewing bearings, extracting of effective features for fault diagnosis and prediction is difficult but crucial. Moreover, challenges such as large data volumes, unlabeled and multi-source bring more difficulties for advanced prognosis and health management methods. To solve these problems, a novel method for performance degradation assessment of bearings based on raw signals is proposed. In this methodology, a combination of deep auto-encoder (DAE) algorithm and particle filter algorithm is utilized for feature extraction and remaining useful life (RUL) prediction. First, the raw vibration signal is employed to train parameters of a restricted Boltzmann machine to build the DAE model. Through encoding and decoding multi-source data, root mean square error of reconstruction error between the raw signal and reconstructed signal is employed to detect incipient faults of slewing bearings. Then, degradation trend model is established by particle filtering to predict RUL of bearings. The effectiveness of proposed method is validated using simulated and experimental vibration signals. Results illustrate that proposed method can evaluate the performance degradation process and RUL of slewing bearings.

Underdetermined Blind Separation for Bearings Faults Based on the Improved Morphological Filter

2014 ◽  
Vol 670-671 ◽  
pp. 1200-1204
Author(s):  
Zhou Jun ◽  
Wu Xing ◽  
Yi Lin Chi ◽  
Pan Nan
Keyword(s):  
Background Noise ◽  
Clustering Algorithm ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Blind Separation ◽  
Sparse Component Analysis ◽  
Simulated Annealing Genetic Algorithm ◽  
Actual Environment ◽  
Mixing Matrix ◽  
Characteristic Signal

Aiming at the condition of the strong background noise, many interference sources and unknown the number of sources in the actual industrial field, a method based on multi-scale multi-structure close-open average combination morphological filtering(C-OACMF) and sparse component analysis (SCA) was proposed to deal with the blind source separation problem of rotation machines. First, the C-OACMF was used to filter out background noise signals and extract the characteristic signal of observation signals, then using the simulated annealing genetic algorithm of fuzzy C-average clustering algorithm estimates the mixing matrix, the linear programming is finally used to estimate the source signals. Through the actual environment composite rolling bearing fault vibration signal extraction experiments verify the effectiveness and accuracy of the proposed algorithm.

An intelligent fault diagnosis method for rolling bearings based on feature transfer with improved DenseNet and joint distribution adaptation

2021 ◽  
Author(s):  
Chenhui Qian ◽  
Quansheng Jiang ◽  
Yehu Shen ◽  
Chunran Huo ◽  
Qingkui Zhang
Keyword(s):  
Fault Diagnosis ◽  
Joint Distribution ◽  
Marginal Distribution ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Mechanical Equipment ◽  
Intelligent Fault Diagnosis ◽  
Rolling Bearings ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method

Abstract Mechanical intelligent fault diagnosis is an important method to accurately identify the health status of mechanical equipment. Traditional fault diagnosis methods perform poorly in the diagnosis of rolling bearings under complex conditions. In this paper, a feature transfer learning model based on improved DenseNet and joint distribution adaptation (FT-IDJ) is proposed. With this model, we apply it to implement rolling bearing fault diagnosis. A lightweight DenseNet model is firstly proposed to extract the transferable features of the raw vibration signal. Furthermore, the parameters in the DenseNet are constrained by the domain adaptive regularization term and pseudo label learning. The marginal distribution discrepancy and the conditional distribution discrepancy of the learned transferable features are reduced by this way. The proposed method is validated by the diagnosis experiments with CWRU and Jiangnan University rolling bearing datasets. The experimental results showed that the proposed FT-IDJ has higher classification accuracy than DAN and other eight methods, which demonstrated its effectively learning transferable features from auxiliary data.

scholarly journals Fault Identification of Rolling Bearing Using Variational Mode Decomposition Multiscale Permutation Entropy and Adaptive GG Clustering

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Tianjing He ◽  
Rongzhen Zhao ◽  
Yaochun Wu ◽  
Chao Yang
Keyword(s):  
Clustering Algorithm ◽  
Optimal Parameter ◽  
Vibration Signal ◽  
Permutation Entropy ◽  
Fault Identification ◽  
Mechanical Equipment ◽  
Original Signal ◽  
Variational Mode Decomposition ◽  
Mode Decomposition ◽  
Reconstructed Signal

The nonlinear and nonstationary characteristics of vibration signal in mechanical equipment make fault identification difficult. To tackle this problem, this paper proposes a novel fault identification method based on improved variational mode decomposition (IVMD), multiscale permutation entropy (MPE), and adaptive GG clustering. Firstly, the original vibration signal is decomposed into a set of mode components adaptively by IVMD, and the mode components that are highly correlated with the original signal are selected to reconstruct the original signal. After that, the MPE values of the reconstructed signal are calculated as feature vectors which can differentiate machinery conditions. Finally, low-dimensional sensitive features obtained by principal component analysis (PCA) are fed into the adaptive GG clustering algorithm to perform fault identification. In this method, the residual energy ratio is used to find the optimal parameter K of the VMD and the PBMF function is incorporated into the GG to determine the number of clusters adaptively. Two bearing datasets are used to validate the performance of the proposed method. The results show that the proposed method can effectively identify different fault types.

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