scholarly journals Composite Fault Diagnosis of Rolling Bearing Based on Optimized Wavelet Packet AR Spectrum Energy Entropy Combined with Adaptive No Velocity Term PSO-SOM-BPNN

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Hongwei Fan ◽  
Yang Yan ◽  
Xuhui Zhang ◽  
Xiangang Cao ◽  
Jiateng Ma
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Back Propagation ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Back Propagation Neural Network ◽  
Spectral Energy ◽  
Self Organizing Map ◽  
Learning Factor ◽  
Energy Entropy

Aiming at the problem of low diagnosis efficiency and accuracy, due to noise and cross aliasing among various faults when diagnosing composite faults of rolling bearing under actual working conditions, a composite fault diagnosis method of rolling bearing based on optimized wavelet packet autoregressive (AR) spectral energy entropy and adaptive no velocity term particle swarm optimization-self organizing map-back propagation neural network (ANVTPSO-SOM-BPNN) is proposed. The energy entropy feature is extracted from the bearing vibration signal through wavelet packet AR spectrum, and SOM and BPNN are combined to form a series network. For PSO, the velocity term is discarded and the inertia weight and learning factor are adaptively adjusted. Finally, the Dempster-Shafer (D-S) evidence fusion diagnosis is carried out. To get closer to the application condition, the data are collected near and far away from the fault point for the composite fault diagnosis, which verifies the effectiveness of the proposed method.

Fault Diagnosis Method for the Rolling Bearing Based on Information Fusion and BP Neural Network

2012 ◽  
Vol 538-541 ◽  
pp. 1956-1961 ◽  
Author(s):  
Jin Min Zhang ◽  
Yin Hua Huang ◽  
Si Ming Wang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Information Fusion ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Back Propagation Neural Network ◽  
Weighted Fusion ◽  
Diagnosis Method

Abstract. In order to diagnose the fault of rolling bearing by the vibration signal, a new method of fault diagnosis based on weighted fusion and BP (Back Propagation) neural network was put forward. At first, the vibration signal from the sensors was wave filtered through the method of correlation function, then the fused signal was obtained by the classical adaptive weighted fusion method, the multi-type characteristics parameters was to be as a neural network input. Finally, the fault diagnosis of rolling bearing was realized by the BP neural network, and the results show that the multi-sensor information fusion fault diagnosis method can be proved effectively to achieve the fault diagnosis of rolling bearing.

Enhanced deep residual network with multilevel correlation information for fault diagnosis of rotating machinery

2020 ◽  
pp. 107754632094971 ◽  
Author(s):  
Shoucong Xiong ◽  
Shuai He ◽  
Jianping Xuan ◽  
Qi Xia ◽  
Tielin Shi
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Learning Algorithms ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Classification Performance ◽  
Residual Network ◽  
Time Frequency ◽  
Correlation Information

Modern machinery becomes more precious with the advance of science, and fault diagnosis is vital for avoiding economical losses or casualties. Among massive diagnosis methods, deep learning algorithms stand out to open an era of intelligent fault diagnosis. Deep residual networks are the state-of-the-art deep learning models which can continuously improve performance by deepening the network structures. However, in vibration-based fault diagnosis, the transient property instability of vibration signal usually calls for time–frequency analysis methods, and the characters of time–frequency matrices are distinct from standard images, which brings some natural limitations for the diagnosis performance of deep learning algorithms. To handle this issue, an enhanced deep residual network named the multilevel correlation stack-deep residual network is proposed in this article. Wavelet packet transform is used to preprocess the sensor signal, and then the proposed multilevel correlation stack-deep residual network uses kernels with different shapes to fully dig various kinds of useful information from any local regions of the processed input. Experiments on two rolling bearing datasets are carried out. Test results show that the multilevel correlation stack-deep residual network exhibits a more satisfactory classification performance than original deep residual networks and other similar methods, revealing significant potentials for realistic fault diagnosis applications.

Fault Diagnosis to a Rolling Bearing in a Blowing Machine Based on the Theories of EMD and Intrinsic Modal Energy Entropy

2011 ◽  
Vol 284-286 ◽  
pp. 2461-2464
Author(s):  
Hai Lan Liu ◽  
Xiao Ping Li ◽  
Yan Nian Rui
Keyword(s):  
Fault Diagnosis ◽  
Classification System ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
New Method ◽  
Support Vector ◽  
Rolling Bearings ◽  
System A ◽  
Energy Entropy

Based on the research of the theory and the experiment of EMD and Intrinsic Modal Energy Entropy，the vibration signal of a rolling bearing in a Blowing Machine of a certain factory was measured when working. Then the signal was decomposed by EMD, its Intrinsic Modal Energy Entropy was calculated and used as fault feature. Finally, using a Support Vector Classification System, a satisfied effect of fault diagnosis of a rolling bearing in a Blowing Machine was got. The experiment had confirmed that the method was advanced, reliable and practical. A new method was provided for fault diagnosis of rolling bearings in some Blowing Machines.

Fault Diagnosis of Water Pump Based on Probabilistic Neural Network

2011 ◽  
Vol 338 ◽  
pp. 421-424
Author(s):  
Tie Jun Li ◽  
Yan Chun Zhao ◽  
Xin Li ◽  
Cheng Shi Zhu ◽  
Jian Rong Ning
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Basic Principle ◽  
Probabilistic Neural Network ◽  
Back Propagation ◽  
Vibration Signal ◽  
Back Propagation Neural Network ◽  
Signal Spectrum ◽  
Water Pump ◽  
Time Diagnosis

The basic principle of probabilistic neural network (PNN) is introduced, which is used in the fault diagnosis of water pump in this paper. The multiple and fractional frequencies in the fault vibration signal spectrum are taken as the feature vectors, and the samples of the fault are established. The probabilistic neural network is trained based on the symptom diagnosis. The result shows that probabilistic neural network can overcome the local optimization of back propagation neural network (BPNN) and meet the requirements for fast diagnosis and high precision diagnosis during fault diagnosis process, so probabilistic neural network can be used in the real time diagnosis, and the fault diagnosis based on probabilistic neural network is feasible.

scholarly journals A Novel Rolling Bearing Fault Diagnosis and Severity Analysis Method

2019 ◽  
Vol 9 (11) ◽  
pp. 2356 ◽  
Author(s):  
Yinsheng Chen ◽  
Tinghao Zhang ◽  
Zhongming Luo ◽  
Kun Sun
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Rolling Bearing ◽  
Relevance Vector Machine ◽  
Sample Entropy ◽  
Analysis Method ◽  
Bearing Fault Diagnosis ◽  
Energy Entropy ◽  
Fault Severity ◽  
Fault Features

To improve the fault identification accuracy of rolling bearing and effectively analyze the fault severity, a novel rolling bearing fault diagnosis and severity analysis method based on the fast sample entropy, the wavelet packet energy entropy, and a multiclass relevance vector machine is proposed in this paper. A fast sample entropy calculation method based on a kd tree is adopted to improve the real-time performance of fault detection in this paper. In view of the non-linearity and non-stationarity of the vibration signals, the vibration signal of the rolling bearing is decomposed into several sub-signals containing fault information by using a wavelet packet. Then, the energy entropy values of the sub-signals decomposed by the wavelet packet are calculated to generate the feature vectors for describing different fault types and severity levels of rolling bearings. The multiclass relevance vector machine modeled by the feature vectors of different fault types and severity levels is used to realize fault type identification and a fault severity analysis of the bearings. The proposed fault diagnosis and severity analysis method is fully evaluated by experiments. The experimental results demonstrate that the fault detection method based on the sample entropy can effectively detect rolling bearing failure. The fault feature extraction method based on the wavelet packet energy entropy can effectively extract the fault features of vibration signals and a multiclass relevance vector machine can identify the fault type and severity by means of the fault features contained in these signals. Compared with some existing bearing rolling fault diagnosis methods, the proposed method is excellent for fault diagnosis and severity analysis and improves the fault identification rate reaching as high as 99.47%.

scholarly journals Experimental Investigation for Fault Diagnosis Based on a Hybrid Approach Using Wavelet Packet and Support Vector Classification

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Pengfei Li ◽  
Yongying Jiang ◽  
Jiawei Xiang
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Hybrid Approach ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Support Vector ◽  
Gear Fault ◽  
Mechanical Faults ◽  
Energy Ratios ◽  
Robust To Noise

To deal with the difficulty to obtain a large number of fault samples under the practical condition for mechanical fault diagnosis, a hybrid method that combined wavelet packet decomposition and support vector classification (SVC) is proposed. The wavelet packet is employed to decompose the vibration signal to obtain the energy ratio in each frequency band. Taking energy ratios as feature vectors, the pattern recognition results are obtained by the SVC. The rolling bearing and gear fault diagnostic results of the typical experimental platform show that the present approach is robust to noise and has higher classification accuracy and, thus, provides a better way to diagnose mechanical faults under the condition of small fault samples.

scholarly journals Applying Energy-Equal Entropy of Wavelet Packet to Diagnose Circuit Breaker Faults

2014 ◽  
Vol 8 (1) ◽  
pp. 445-452
Author(s):  
Liu Mingliang ◽  
Wang Keqi ◽  
Sun Laijun ◽  
Zhang Jianfeng
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Wavelet Packet ◽  
Circuit Breaker ◽  
Back Propagation ◽  
Vibration Signal ◽  
Characteristic Parameter ◽  
Vibration Signals

Aiming to better reflect features of machinery vibration signals of high-voltage (HV) circuit breaker (CB), a new method is proposed on the basis of energy-equal entropy of wavelet packet(WP). First of all, three-layer wavelet packet decomposes vibration signal, reconstructing 8 nodes of signals in the 3rd layer. Then, the vector is extracted with energy-equal entropy of reconstructed signals. At last, the simple back-propagation (BP) neural network for fault diagnosis contributes to classification of the characteristic parameter. This technology is the basis of a number of patents and patents pending, which is experimentally demonstrated by the significant improvement of diagnose faults.

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