scholarly journals Intelligent Fault Diagnosis of Machines Based on Adaptive Transfer Density Peaks Search Clustering

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Meng Li ◽  
Yanxue Wang ◽  
Chuyuan Wei
Keyword(s):  
Fault Diagnosis ◽  
Clustering Algorithm ◽  
Wavelet Packet ◽  
Rotating Machinery ◽  
Computational Time ◽  
Intelligent Fault Diagnosis ◽  
Computational Accuracy ◽  
Density Peak ◽  
Density Peaks ◽  
Peak Search

Intelligent fault diagnosis technology of the rotating machinery is an important way to guarantee the safety of industrial production. To enhance the accuracy of autonomous diagnosis using unlabelled mechanical faults data, a novel intelligent diagnosis algorithm has been developed for rotating machinery based on adaptive transfer density peak search clustering. Combined with the wavelet packet energy feature extraction algorithm, the proposed algorithm can enhance the computational accuracy and reduce the computational time consumption. The proposed adaptive transfer density peak search clustering algorithm can adaptively adjust the classification parameters and mark the categories of unlabelled experimental data. Results of bearing experimental analysis demonstrated that the proposed technique is suitable for machinery fault diagnosis using unlabelled data, compared with other traditional algorithms.

Intelligent fault diagnosis of rotating machinery based on multiple relevance vector machines with variance radial basis function kernel

2011 ◽  
Vol 225 (7) ◽  
pp. 1718-1729 ◽  
Author(s):  
C He ◽  
C Liu ◽  
Y Li ◽  
J Yuan
Keyword(s):  
Fault Diagnosis ◽  
Radial Basis Function ◽  
Basis Function ◽  
Wavelet Packet ◽  
Rotating Machinery ◽  
Intelligent Fault Diagnosis ◽  
Relevance Vector Machines ◽  
Vector Machines ◽  
Radial Basis ◽  
Diagnosis Model

Automatic and accurate fault diagnosis is very important for condition-based maintenance. In this study, an intelligent fault diagnosis method based on relevance vector machines (RVM) is proposed for automatic fault diagnosis of rotating machinery. First, the global optimal features from all node energies of full wavelet packet tree are obtained by combining wavelet packet transform with an improved Fisher feature selection method. Individual salient feature subsets are selected for each pair of classes separately. Then, RVM method is adopted to train the intelligent fault diagnosis model. The multi-class RVM classifier is constructed by combining several RVM binary classifiers using ‘max-probability-win’ strategy. Moreover, improved from Gaussian radial basis function, a new kernel function denoted variance radial basis function is developed and used for RVM to adaptively balance the difference between the scales of different features. The proposed method was carried out to develop a multi-class bearing fault diagnosis model under varying load conditions, resulting in high accuracy around 99.58 per cent. Experimental results demonstrate that the proposed method is promising for intelligent fault diagnosis of rotating machinery.

Construction of a deep sparse filtering network for rotating machinery fault diagnosis

2021 ◽  
pp. 095440702110148
Author(s):  
Chun Cheng ◽  
Wei Zou ◽  
Weiping Wang ◽  
Michael Pecht
Keyword(s):  
Fault Diagnosis ◽  
Back Propagation ◽  
Feature Learning ◽  
Back Propagation Neural Network ◽  
Rotating Machinery ◽  
Fine Tuning ◽  
Local Optimum ◽  
Back Propagation Algorithm ◽  
Intelligent Fault Diagnosis ◽  
Sparse Filtering

Deep neural networks (DNNs) have shown potential in intelligent fault diagnosis of rotating machinery. However, traditional DNNs such as the back-propagation neural network are highly sensitive to the initial weights and easily fall into the local optimum, which restricts the feature learning capability and diagnostic performance. To overcome the above problems, a deep sparse filtering network (DSFN) constructed by stacked sparse filtering is developed in this paper and applied to fault diagnosis. The developed DSFN is pre-trained by sparse filtering in an unsupervised way. The back-propagation algorithm is employed to optimize the DSFN after pre-training. Then, the DSFN-based intelligent fault diagnosis method is validated using two experiments. The results show that pre-training with sparse filtering and fine-tuning can help the DSFN search for the optimal network parameters, and the DSFN can learn discriminative features adaptively from rotating machinery datasets. Compared with classical methods, the developed diagnostic method can diagnose rotating machinery faults with higher accuracy using fewer training samples.

scholarly journals Fault Diagnosis Approach for Rotating Machinery Based on Feature Importance Ranking and Selection

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Zong Yuan ◽  
Taotao Zhou ◽  
Jie Liu ◽  
Changhe Zhang ◽  
Yong Liu
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Rotating Machinery ◽  
Ranking And Selection ◽  
Fault Features ◽  
Feature Importance ◽  
Importance Ranking ◽  
Common Signal ◽  
Diagnosis Approach

The key to fault diagnosis of rotating machinery is to extract fault features effectively and select the appropriate classification algorithm. As a common signal decomposition method, the effect of wavelet packet decomposition (WPD) largely depends on the applicability of the wavelet basis function (WBF). In this paper, a novel fault diagnosis approach for rotating machinery based on feature importance ranking and selection is proposed. Firstly, a two-step principle is proposed to select the most suitable WBF for the vibration signal, based on which an optimized WPD (OWPD) method is proposed to decompose the vibration signal and extract the fault information in the frequency domain. Secondly, FE is utilized to extract fault features of the decomposed subsignals of OWPD. Thirdly, the categorical boosting (CatBoost) algorithm is introduced to rank the fault features by a certain strategy, and the optimal feature set is further utilized to identify and diagnose the fault types. A hybrid dataset of bearing and rotor faults and an actual dataset of the one-stage reduction gearbox are utilized for experimental verification. Experimental results indicate that the proposed approach can achieve higher fault diagnosis accuracy using fewer features under complex working conditions.

scholarly journals Improving Density Peak Clustering by Automatic Peak Selection and Single Linkage Clustering

Symmetry ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1168
Author(s):  
Jun-Lin Lin ◽  
Jen-Chieh Kuo ◽  
Hsing-Wang Chuang
Keyword(s):  
Clustering Algorithm ◽  
Academic Community ◽  
Performance Study ◽  
Potential Density ◽  
Cluster Assignment ◽  
Density Peak ◽  
Single Linkage ◽  
Density Peaks ◽  
Assignment Strategy ◽  
Density Peak Clustering

Density peak clustering (DPC) is a density-based clustering method that has attracted much attention in the academic community. DPC works by first searching density peaks in the dataset, and then assigning each data point to the same cluster as its nearest higher-density point. One problem with DPC is the determination of the density peaks, where poor selection of the density peaks could yield poor clustering results. Another problem with DPC is its cluster assignment strategy, which often makes incorrect cluster assignments for data points that are far from their nearest higher-density points. This study modifies DPC and proposes a new clustering algorithm to resolve the above problems. The proposed algorithm uses the radius of the neighborhood to automatically select a set of the likely density peaks, which are far from their nearest higher-density points. Using the potential density peaks as the density peaks, it then applies DPC to yield the preliminary clustering results. Finally, it uses single-linkage clustering on the preliminary clustering results to reduce the number of clusters, if necessary. The proposed algorithm avoids the cluster assignment problem in DPC because the cluster assignments for the potential density peaks are based on single-linkage clustering, not based on DPC. Our performance study shows that the proposed algorithm outperforms DPC for datasets with irregularly shaped clusters.

Research on the Imbalance-Crack Coupling Fault Diagnosis Based on Wavelet Packet and Energy Spectrum Analysis

2011 ◽  
Vol 143-144 ◽  
pp. 675-679 ◽  
Author(s):  
Fu Ze Xu ◽  
Xue Jun Li ◽  
Guang Bin Wang ◽  
Da Lian Yang
Keyword(s):  
Mechanical Properties ◽  
Fault Diagnosis ◽  
Energy Spectrum ◽  
Spectrum Analysis ◽  
Wavelet Packet ◽  
Rotating Machinery ◽  
Vibration Acceleration ◽  
Acceleration Signal ◽  
Coupling Fault ◽  
Fault Information

It is common for the imbalance-crack coupling fault in rotating machinery, while the crack information is often overshadowed by unbalanced fault information, which is difficult to extract the crack signal. In order to extract the crack signal of the imbalance-crack coupling fault, and realize the fault diagnosis, the paper mainly analyzes its mechanical properties, and then use wavelet packet to de-nosing, decomposing and reconstructing the acquisition of vibration acceleration signal, and then analyzing the characteristics of frequency domain of the fault signal by using the energy spectrum. So the experiment proved that analyze and dispose the acquisition of the fault signal by using the method of the energy spectrum and the wavelet packet, which can effectively distinguish between the crack signal and unbalanced signals in imbalance-crack coupling faults .It also can provide some reference for the diagnosis and prevention for such fault.

Sign in / Sign up

Export Citation Format

Share Document