Research and Application of the Improved DST New Method Based on Fuzzy Consistent Matrix and the Weighted Average

2014 ◽  
Vol 1030-1032 ◽  
pp. 1764-1768 ◽  
Author(s):  
Wei Xiao Xu ◽  
Ji Wen Tan ◽  
Hong Zhan
Keyword(s):  
Fault Diagnosis ◽  
Evidence Synthesis ◽  
Weighted Average ◽  
Rolling Bearing ◽  
New Method ◽  
Cnc Machine ◽  
Bearing Fault Diagnosis ◽  
Dempster Shafer Theory ◽  
High Conflict ◽  
Shafer Theory

Aiming at the existing defects of evidence dempster-shafer theory (DST) in dealing with high conflict evidence, we proposed a new method to improve DST. By introducing concept of fuzzy consistent matrix, calculate the weights of factors, and put different sources of evidence into distinguish, and finally cast more than one vote to prevent the phenomenon, the average convergence of evidence. What’s more, the improved DST new method is applied to the rolling bearing fault diagnosis of CNC machine workbench .The test results show that the improved new synthetic formula increases the accuracy of fault diagnosis Ball, the conflict of evidence synthesis results better, to achieve better results.

scholarly journals Weak Signal Detection Method Based on the Coupled Lorenz System and Its Application in Rolling Bearing Fault Diagnosis

2020 ◽  
Vol 10 (12) ◽  
pp. 4086
Author(s):  
Guozheng Li ◽  
Nanlin Tan ◽  
Xiang Li
Keyword(s):  
Fault Diagnosis ◽  
Power Spectrum ◽  
Lorenz System ◽  
Signal To Noise Ratio ◽  
Rolling Bearing ◽  
Synchronization Error ◽  
New Method ◽  
Rolling Bearings ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

Rolling bearings are widely used in rotating machinery. Their fault feature signals are often submerged in strong noise and are difficult to identify. This paper presents a new method of bearing fault diagnosis that combines the coupled Lorenz system and power spectrum technology. The process is achieved in the following three steps. First, a synchronization system based on the Lorenz system is constructed using the driving-response method. Second, when the tested signal is connected to the driving end, the synchronization error between the two sub-chaotic systems is obtained. Finally, the power spectrum density of the synchronization error is calculated and compared with the corresponding fault characteristic frequency. The coupled Lorenz system makes full use of the noise immunity and nonlinear amplification of the chaotic system. The detection characteristics and feasibility of the new method are verified by simulation and actual measured vibration data. The result shows that the noise reduction effect of the coupled Lorenz system is obvious. This method can improve the signal-to-noise ratio of the tested signal and provide a new way to perform fault diagnosis of rolling bearings.

scholarly journals Multi-Channel IoT-Based Ensemble-Features Fault Diagnosis for Machine Condition Monitoring

2020 ◽  
Author(s):  
Shang Gao ◽  
Cuicui Du
Keyword(s):  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Rolling Bearing ◽  
Classifier Fusion ◽  
Machine Condition ◽  
Rolling Ball ◽  
Dempster Shafer Theory ◽  
Machine Condition Monitoring ◽  
Shafer Theory ◽  
Fusion Approach

This paper proposes a multi-channel internet of things (IoT)-based industrial wireless sensor network (IWSN) with ensemble-features fault diagnosis for machine condition monitoring and fault diagnosis. In this paper, the rolling bearing is taken as an example of monitored industrial equipment due to its wide use in industrial processes. The rolling bearing vibration signals are measured for further processing and analysis. On-sensor node ensemble feature extraction and fault diagnosis using Back Propagation network are then investigated to address the tension between the higher system requirements of IWSNs and the resource-constrained characteristics of sensor nodes. A two-step classifier fusion approach using Dempster-Shafer theory is also explored to increase diagnosis result quality. Four rolling bearing operating in cage fracture, rolling ball spalling, inner ring spalling and outer ring spalling are monitored to evaluate the proposed system. The final fault diagnosis results using the proposed classifier fusion approach give a result certainty of at least 94.21%, proving the feasibility of the proposed method to identify the bearing-fault patterns. This paper is conducted to provide new insights into how a high-accuracy IoT-based ensemble-features fault diagnosis algorithm is designed and further giving advisable reference to more IWSNs scenarios.

Rolling Bearing Fault Diagnosis Based on Blind Source Separation

2012 ◽  
Vol 217-219 ◽  
pp. 2546-2549 ◽  
Author(s):  
Chang Zheng Chen ◽  
Qiang Meng ◽  
Hao Zhou ◽  
Yu Zhang
Keyword(s):  
Signal Processing ◽  
Fault Diagnosis ◽  
Blind Source Separation ◽  
Source Separation ◽  
Rolling Bearing ◽  
New Method ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method

This document presents fault diagnosis method of rolling bearing based on blind source separation. The algorithm based on fast ICA is improved to separate fault signals according to the rolling bearing’s fault characteristics. Through the experiment it is shown that the algorithm can separate the signals collected from rolling bearing and gearbox effectively, which can provide a new method for fault diagnosis and signal processing of machinery equipment.

scholarly journals Bearing Fault Diagnosis Based on a Hybrid Classifier Ensemble Approach and the Improved Dempster-Shafer Theory

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2097 ◽  
Author(s):  
Yanxue Wang ◽  
Fang Liu ◽  
Aihua Zhu
Keyword(s):  
Fault Diagnosis ◽  
Ensemble Classifier ◽  
Classifier Ensemble ◽  
Support Vector ◽  
Hybrid Classifier ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Ensemble Approach ◽  
Dempster Shafer Theory ◽  
Shafer Theory

Bearing fault diagnosis of a rotating machine plays an important role in reliable operation. A novel intelligent fault diagnosis method for roller bearings has been developed based on a proposed hybrid classifier ensemble approach and the improved Dempster-Shafer theory. The improved Dempster-Shafer theory well considered the combination of unreliable evidence sources, the uncertainty information of basic probability assignment, and the relative credibility of the evidence on the weights in the process of decision making under the framework of fuzzy preference relations, which can effectively deal with conflicts of the evidences and then well improve the diagnostic accuracy for the hybrid classifier ensemble. The effectiveness of the improved Dempster-Shafer theory has been verified via a numerical example. In addition, deep neural networks, a support vector machine, and extreme learning machine techniques have been utilized in the single-stage classification based on singular spectrum entropy, power spectrum entropy, time-frequency entropy, and wavelet packet energy spectrum entropy in this work. Performances of the proposed hybrid ensemble classifier has been demonstrated on a bearing test-rig, compared with the original Dempster-Shafer theory. It can be found that the overall error rate can be greatly reduced with the hybrid ensemble classifier and the improved Dempster-Shafer theory.

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