Variable Condition Bearing Fault Diagnosis Based on Time-Domain and Artificial Intelligence

2012 ◽  
Vol 203 ◽  
pp. 329-333 ◽  
Author(s):  
Qing Zhong Hu ◽  
Shu Lei Zhang ◽  
Sheng Yang
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Fault Diagnosis ◽  
Frequency Domain ◽  
Time Domain ◽  
Experimental Result ◽  
Good Effect ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Variable Condition

Aim at some problem in fault diagnose: the characteristic frequency depends on the speed, the spectrum is complex , which are easy to diagnose error when in the variable conditions, and it is often difficult to identify the fault positioning in the frequency domain. the paper puts forward a new method: Variable condition bearing fault diagnosis basing on time-domain and artificial intelligence , not depend on speed and frequency domain. This method use vibration signal, calculates the kurtosis, skewness, rms etc 12 time-domain value, then these character vectors are sent to the neural network classifier to complete fault type pattern recognition, Finally, the same faults are sent to the next neural network for fault positioning and damage extent identification. The experimental result showed that using this method can obtain very good effect.

scholarly journals An Adaptive Spectral Kurtosis Method Based on Optimal Filter

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Yanli Yang ◽  
Ting Yu
Keyword(s):  
Fault Diagnosis ◽  
Frequency Domain ◽  
Time Domain ◽  
Experimental Results ◽  
Bearing Fault ◽  
Spectral Kurtosis ◽  
Bearing Fault Diagnosis ◽  
The Core ◽  
Factors Influencing ◽  
The Time Domain

As a useful tool to detect protrusion buried in signals, kurtosis has a wide application in engineering, for example, in bearing fault diagnosis. Spectral kurtosis (SK) can further indicate the presence of a series of transients and their locations in the frequency domain. The factors influencing kurtosis values are first analyzed, leading to the conclusion that amplitude, not the frequency of signals, and noise make major contribution to kurtosis values. It is helpful to detect impulsive components if the components with big amplitude are removed from composite signals. Based on this cognition, an adaptive SK algorithm is proposed in this paper. The core steps of the proposed SK algorithm are to find maxima, add window around maxima, merge windows in the frequency domain, and then filter signals according to the merged window in the time domain. The parameters of the proposed SK algorithm are varying adaptively with signals. Some experimental results are presented to demonstrate the effectiveness of the proposed algorithm.

scholarly journals Bearing Fault Diagnosis Based on Shallow Multi-Scale Convolutional Neural Network with Attention

Energies ◽  
2019 ◽  
Vol 12 (20) ◽  
pp. 3937 ◽  
Author(s):  
Tengda Huang ◽  
Sheng Fu ◽  
Haonan Feng ◽  
Jiafeng Kuang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Time Domain ◽  
Attention Mechanism ◽  
Intelligent Fault Diagnosis ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Multi Scale

Recently, deep learning technology was successfully applied to mechanical fault diagnosis. The convolutional neural network (CNN), as a prevalent deep learning model, occupies a place in intelligent fault diagnosis, which reduces the need for human feature extraction and prior knowledge, thereby achieving an end-to-end intelligent fault diagnosis model. However, the data for mechanical fault diagnosis in practical application are limited, the CNN model is too deep and too complex, making it prone to overfitting, and a model with too simple a structure and shallow layers cannot fully learn the effective features of the data. Convolutional filters with fixed window sizes are widely used in existing CNN models, which cannot flexibly select variable pivotal features. The model may be interfered with by redundant information in feature maps during training. Therefore, in this paper, a novel shallow multi-scale convolutional neural network with attention is proposed for bearing fault diagnosis. The shallow multi-scale convolutional neural network structure can fully learn the feature information of input data without overfitting. For the first time, a feature attention mechanism is developed for fault diagnosis to adaptively select features for classification more effectively, where the pivotal feature was emphasized, and the redundant feature was weakened through an attention mechanism. The time frequency representations as the input of the model were obtained from the vibration time domain signals, which contain the complete time domain and frequency domain information of the vibration signals. Compared with the current popular diagnostic methods, the results show that the proposed diagnostic method has fairly high accuracy, and its performance is superior to the existing methods. The average recognition accuracy was 99.86%, and the weak recognition rate of I-07 and I-14 labels was improved.

scholarly journals An Improved CNN-LSTM Network Based On Hierarchical Attention Mechanism For Motor Bearing Fault Diagnosis

2021 ◽  
Author(s):  
Yisha Jiao ◽  
Yaoguang Wei ◽  
Dong An ◽  
Wenshu Li ◽  
Qiong Wei
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Signal Processing ◽  
Fault Diagnosis ◽  
Attention Mechanism ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
The Neural Network ◽  
Lstm Network ◽  
Motor Bearing

Abstract Motor is widely used in industrial production, but the frequent motor bearing fault brings great safety hazard to the production. Traditional fault diagnosis methods often require prior signal processing knowledge and are inefficient. In order to solve this problem, the artificial intelligence fault diagnosis method has been applied in motor bearing fault diagnosis. With the help of the original motor running state signal collected by the sensors, non-invasive real-time detection of motor bearing fault can be realized. This paper presents an improved CNN-LSTM network based on hierarchical attention mechanism(CALSTM) for motor bearing fault diagnosis. In this artificial intelligence method, the fault characteristics of the original data can be learned by convolutional neural network, and then the importance of the features can be obtained by using hierarchical attention mechanism. Finally, the weighted results are sent to the LSTM network for time dimension selection. This method does not need signal processing and adaptively weights the features of each sample learned by the neural network, which enhances the explanatory ability of the learning process of the neural network. When carry out experiments on CWRU data set, and the experimental results indicate that, compared with several common models, CALSTM method has a better diagnosis effect, and the overall accuracy of the model reached 99.22%.

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