Fault Diagnosis Method of Time Domain and Time-Frequency Domain Based on Information Fusion

2013 ◽  
Vol 300-301 ◽  
pp. 635-639 ◽  
Author(s):  
Jiang Zhao ◽  
Jiao Wang ◽  
Meng Shang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Information Fusion ◽  
Time Domain ◽  
Wavelet Packet ◽  
Network Information ◽  
Time Frequency ◽  
Index Data ◽  
Diagnosis Method ◽  
Frequency Band Energy

On account of the problem that traditional pipe leakage diagnosis method is not highly accuracy .this paper come up with a method that based on pipe leakage diagnosis method of neural network information fusion. Giving the stress wave time domain feature extraction index data algorithm and wavelet packet extraction each the frequency band energy algorithm, by comparing with these results of the pressure wave time domain feature index data, time-frequency extraction energy values and fault diagnosis of both information fusion ,which show the neural network information fusion method that is used for pipe leakage diagnosis that is feasible and effective.

The Fault Diagnosis Method of Diesel Based on Wavelet Pocket and BP Neural Network

2014 ◽  
Vol 635-637 ◽  
pp. 910-913 ◽  
Author(s):  
Hong Hui Sun ◽  
Jun Xu ◽  
Qing Hua Zhang ◽  
Hong Xia Wang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Adaptive Learning ◽  
Bp Neural Network ◽  
Wavelet Packet ◽  
Vibration Signals ◽  
Signal Energy ◽  
Time Frequency ◽  
Diagnosis Method ◽  
Neural Network Algorithm

Because of the well time-frequency spectrum disposal capability of wavelet packet, the wavelet packet algorithm is used to analyze the time - frequency characteristics of diesel vibration signals. The signal energy distributing characteristics based on wavelet packet transform. are extracted and taken as diagnostic characteristic vector, then improved BP neural network algorithm that connects additional momentum with self-adaptive learning rate was used to classify and recognize faults of diesel valves. The experimental results show the fault diagnosis method of diesel based on wavelet pocket and BP neural network is effective and feasible.

scholarly journals A Diagnosis Method for the Compound Fault of Gearboxes Based on Multi-Feature and BP-AdaBoost

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 461 ◽  
Author(s):  
Yangyang Zhang ◽  
Yunxian Jia ◽  
Weiyi Wu ◽  
Zhonghua Cheng ◽  
Xiaobo Su ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Time Domain ◽  
Failure Modes ◽  
Wavelet Packet ◽  
Rotating Machinery ◽  
Adaboost Algorithm ◽  
Feature Parameters ◽  
Reconstructed Signal ◽  
Diagnosis Method ◽  
Frequency Band Energy

Gearbox is an important structure of rotating machinery, and the accurate fault diagnosis of gearboxes is of great significance for ensuring efficient and safe operation of rotating machinery. Aiming at the problem that there is little common compound fault data of gearboxes, and there is a lack of an effective diagnosis method, a gearbox fault simulation experiment platform is set up, and a diagnosis method for the compound fault of gearboxes based on multi-feature and BP-AdaBoost is proposed. Firstly, the vibration signals of six typical states of gearbox are obtained, and the original signals are decomposed by empirical mode decomposition and reconstruct the new signal to achieve the purpose of noise reduction. Then, perform the time domain analysis and wavelet packet analysis on the reconstructed signal, extract three time domain feature parameters with higher sensitivity, and combine them with eight frequency band energy feature parameters obtained by wavelet packet decomposition to form the gearbox state feature vector. Finally, AdaBoost algorithm and BP neural network are used to build the BP-AdaBoost strong classifier model, and feature vectors are input into the model for training and verification. The results show that the proposed method can effectively identify the gearbox failure modes, and has higher accuracy than the traditional fault diagnosis methods, and has certain reference significance and engineering application value.

Application of Integrated Neural Network Information Fusion in Fault Diagnosis of one Missile Launching Control Unit

2013 ◽  
Vol 427-429 ◽  
pp. 2808-2812
Author(s):  
Xu De Cheng ◽  
Hong Li Wang ◽  
Bing Xu ◽  
Xue Dong Xue
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Information Fusion ◽  
Preliminary Analysis ◽  
Control Unit ◽  
Fusion Technology ◽  
Network Information ◽  
Diagnosis Rate ◽  
Fault Diagnosis System ◽  
Made In

Research and development of fault diagnosis system in application of integrated neural network information fusion is based on information fusion technology, with which preliminary analysis of equipment fault is made in different perspectives in terms of neural network, so as to identify the fault on the basis of fusion outcome. This technique is applied in fault diagnosis of one type of missile launching control unit, leading to sufficient use of various information and substantially increased fault diagnosis rate.

scholarly journals An SDP Characteristic Information Fusion-Based CNN Vibration Fault Diagnosis Method

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Xiaoxun Zhu ◽  
Jianhong Zhao ◽  
Dongnan Hou ◽  
Zhonghe Han
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Information Fusion ◽  
Fault Identification ◽  
High Complexity ◽  
Test Rig ◽  
Rotor Vibration ◽  
Vibration Signals ◽  
Image Identification ◽  
Diagnosis Method

This study proposes a symmetrized dot pattern (SDP) characteristic information fusion-based convolutional neural network (CNN) fault diagnosis method to resolve issues of high complexity, nonlinearity, and instability in original rotor vibration signals. The method was used to conduct information fusion of real modal components of vibration signals and SDP image identification using CNN in order to achieve vibration fault diagnosis. Compared with other graphic processing methods, the proposed method more fully expressed the characteristics of different vibration signals and thus presented variations between different vibration states in a simpler and more intuitive way. The proposed method was experimentally investigated using simulation signals and rotor test-rig signals, and its validity and advancements were demonstrated using experimental analysis. By using CNN through deep learning to adaptively extract SDP characteristic information, vibration fault identification was ultimately realized.

Vibration-Based Fault Diagnosis of Pump Using WPT

2008 ◽  
Author(s):  
Pan Hong ◽  
Zheng Yuan
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Accurate Method ◽  
Singularity Analysis ◽  
Good Time ◽  
Signal Denoising ◽  
Good Tool ◽  
Time Frequency ◽  
Diagnosis Method

A vibration-based fault diagnosis method of pump units based on wavelet packet transform (WPT) is proposed in this paper. Compared with Fourier transform (FT) and wavelet transform (WT), WPT can subdivide the whole time-frequency domain. It can perform signals with good time resolution at high frequency and vice versa. WPT is considered as a good tool to signal denoising, accounting for its perfect ability in decomposing and reconstructing signal and its characteristic of no redundancy and divulges after denoising. In addition, WPT modulus maximal coefficient provides a simple but accurate method in calculating the Lipschitz exponents, which is the measurement of signal singularity. According to the singularity analysis results of vibration signal, we can recognize the fault pattern of pump units. This paper makes a detail research on signal denoising and singularity analysis based on WPT. Taking the main shaft and thrust bearing vibration signal for example, the experimental results show that WPT is effectively in the fault diagnosis system of pump unit.

Research of Automobile Engine Fault Diagnosis Based on Wavelet Packet

2012 ◽  
Vol 472-475 ◽  
pp. 795-798
Author(s):  
Min Yong Tong
Keyword(s):  
Fault Diagnosis ◽  
Frequency Band ◽  
Signal Analysis ◽  
Energy Analysis ◽  
Wavelet Packet ◽  
Automobile Engine ◽  
Band Energy ◽  
Diagnosis Method ◽  
S Model ◽  
Frequency Band Energy

A diagnosis method using wavelet packet, frequency band energy analysis and neural network was presented for the automobile engine fault diagnosis. Fault signal of automobile engine was decomposed at different frequency band by wavelet packet. According to the change of frequency band energy, fault frequency band of the automobile engine was found. Fault diagnosis knowledge is described by means of applying T-S model. Results from the experimental signal analysis show that the proposed method is effective in diagnosing the automobile engine faults.

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