scholarly journals Application Combining VMD and ResNet101 in Intelligent Diagnosis of Motor Faults

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6065
Author(s):  
Shih-Lin Lin
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Numerical Control ◽  
Image Feature ◽  
Intelligent Fault Diagnosis ◽  
Data Set ◽  
Diagnosis System ◽  
Wide Range ◽  
Fault Diagnosis System

Motor failure is one of the biggest problems in the safe and reliable operation of large mechanical equipment such as wind power equipment, electric vehicles, and computer numerical control machines. Fault diagnosis is a method to ensure the safe operation of motor equipment. This research proposes an automatic fault diagnosis system combined with variational mode decomposition (VMD) and residual neural network 101 (ResNet101). This method unifies the pre-analysis, feature extraction, and health status recognition of motor fault signals under one framework to realize end-to-end intelligent fault diagnosis. Research data are used to compare the performance of the three models through a data set released by the Federal University of Rio de Janeiro (UFRJ). VMD is a non-recursive adaptive signal decomposition method that is suitable for processing the vibration signals of motor equipment under variable working conditions. Applied to bearing fault diagnosis, high-dimensional fault features are extracted. Deep learning shows an absolute advantage in the field of fault diagnosis with its powerful feature extraction capabilities. ResNet101 is used to build a model of motor fault diagnosis. The method of using ResNet101 for image feature learning can extract features for each image block of the image and give full play to the advantages of deep learning to obtain accurate results. Through the three links of signal acquisition, feature extraction, and fault identification and prediction, a mechanical intelligent fault diagnosis system is established to identify the healthy or faulty state of a motor. The experimental results show that this method can accurately identify six common motor faults, and the prediction accuracy rate is 94%. Thus, this work provides a more effective method for motor fault diagnosis that has a wide range of application prospects in fault diagnosis engineering.

scholarly journals Intelligent Fault Diagnosis and Forecast of Time-Varying Bearing Based on Deep Learning VMD-DenseNet

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7467
Author(s):  
Shih-Lin Lin
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Vibration Signal ◽  
Image Feature ◽  
Signal Acquisition ◽  
Time Varying ◽  
Intelligent Fault Diagnosis ◽  
Image Block ◽  
Wide Range

Rolling bearings are important in rotating machinery and equipment. This research proposes variational mode decomposition (VMD)-DenseNet to diagnose faults in bearings. The research feature involves analyzing the Hilbert spectrum through VMD whereby the vibration signal is converted into an image. Healthy and various faults show different characteristics on the image, thus there is no need to select features. Coupled with the lightweight network, DenseNet, for image classification and prediction. DenseNet is used to build a model of motor fault diagnosis; its structure is simple, and the calculation speed is fast. The method of using DenseNet for image feature learning can perform feature extraction on each image block of the image, providing full play to the advantages of deep learning to obtain accurate results. This research method is verified by the data of the time-varying bearing experimental device at the University of Ottawa. Through the four links of signal acquisition, feature extraction, fault identification, and prediction, a mechanical intelligent fault diagnosis system has established the state of bearing. The experimental results show that the method can accurately identify four common motor faults, with a VMD-DenseNet prediction accuracy rate of 92%. It provides a more effective method for bearing fault diagnosis and has a wide range of application prospects in fault diagnosis engineering. In the future, online and timely diagnosis can be achieved for intelligent fault diagnosis.

scholarly journals An Unsupervised Intelligent Fault Diagnosis System Based on Feature Transfer

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Nannan Lu ◽  
Songcheng Wang ◽  
Hanhan Xiao
Keyword(s):  
Fault Diagnosis ◽  
Intelligent Manufacturing ◽  
Intelligent Fault Diagnosis ◽  
Target Domain ◽  
Data Set ◽  
Diagnosis System ◽  
Time Frequency ◽  
Recognition Ability ◽  
Fault Diagnosis System ◽  
Original Feature

With the booming development of intelligent manufacturing in modern industry, intelligent fault diagnosis systems have become a necessity to equipment and machine, which have attracted many researchers’ attention. However, due to the requirements of enough labeled data for most of the current approaches, it is difficult to implement them in real industrial scenarios. In this paper, an unsupervised intelligent fault diagnosis system based on feature transfer is constructed to extract the historical labeled data of the source domain, using feature transfer to facilitate the fault diagnosis of the target domain. The original feature set is acquired by EEMD time-frequency analysis. Then, the transfer component analysis algorithm is adopted to minimize the distance between the marginal distributions of the source and target domains which reduces the discrepancy of features between the different domains. Finally, SVM is used in multiclassification to identify different categories of faults. The performance of the fault diagnosis system under different loads is tested on the CWRU bearing data set, and the experiments show that the proposed system could effectively improve the recognition ability of unsupervised fault diagnosis.

Design and Analysis of Fault Diagnosis System of Electrohydraulic Servo Valve Based on ANN

2013 ◽  
Vol 785-786 ◽  
pp. 1380-1383
Author(s):  
Yao Li ◽  
Jian Gang Yi
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fault Diagnosis ◽  
Servo System ◽  
Intelligent Fault Diagnosis ◽  
Diagnosis System ◽  
Servo Valve ◽  
Pressure Characteristic ◽  
Fault Diagnosis System ◽  
Electrohydraulic Servo System

It is a difficulty to combine artificial neural networks (ANN) with the fault diagnosis of electrohydraulic servo valve. To slolve this problem, the fault diagnosis mechanism of electrohydraulic servo system is analysed, the effecitveness of fault diagnosis based on ANN is verified, and the pressure characteristic data are used to construct ANN samples. Finally, the algorithms of RBF, BP and Elman are compared with the built system and sampled. The results show the RBF algorithm is more rapid and accurate and the proposed intelligent fault diagnosis system of electrohydraulic servo valve is valuable.

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