scholarly journals Fault Detection for Complex System under Multi-Operation Conditions Based on Correlation Analysis and Improved Similarity

Symmetry ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1836
Author(s):  
Shi Liang ◽  
Jiewei Zeng
Keyword(s):  
Fault Detection ◽  
Correlation Analysis ◽  
Historical Data ◽  
Detection Method ◽  
Suspension System ◽  
Health Index ◽  
Maglev Train ◽  
Complex Operation ◽  
Operation Conditions ◽  
Abnormal State

During actual engineering, due to the influence of complex operation conditions, the data of complex systems are distinct, and the range of similarity differs under complex operation conditions. Simultaneously, the length of the data used to calculate the similarity will also impact the result of the fault detection. According to these, this paper proposes a fault detection method based on correlation analysis and improved similarity. In the first place, the complex operation conditions are divided into several simple operation conditions via the existing historical data. In the next place, the length of the data used to calculate the similarity is determined by correlation analysis. Then, an improved similarity calculation method is proposed to make the range of the similarity under multi-operation conditions identical. Finally, this method is applied to the suspension system of the maglev train. The experiment results indicate that the method proposed in this paper can not only detect the fault or abnormal state of the suspension system but also observe the health index (HI) changes of the system at distinct times under multi-operation conditions.

scholarly journals Fault Detection Based on Tracking Differentiator Applied on the Suspension System of Maglev Train

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Hehong Zhang ◽  
Yunde Xie ◽  
Zhiqiang Long
Keyword(s):  
Fault Detection ◽  
Detection Method ◽  
Fault Tolerant ◽  
Control Variable ◽  
Suspension System ◽  
Acceleration Sensor ◽  
Maglev Train ◽  
Tracking Differentiator ◽  
Practical Usefulness ◽  
Simulation And Experiment

A fault detection method based on the optimized tracking differentiator is introduced. It is applied on the acceleration sensor of the suspension system of maglev train. It detects the fault of the acceleration sensor by comparing the acceleration integral signal with the speed signal obtained by the optimized tracking differentiator. This paper optimizes the control variable when the states locate within or beyond the two-step reachable region to improve the performance of the approximate linear discrete tracking differentiator. Fault-tolerant control has been conducted by feedback based on the speed signal acquired from the optimized tracking differentiator when the acceleration sensor fails. The simulation and experiment results show the practical usefulness of the presented method.

scholarly journals Fault Detection Method for Suspension Systems of Maglev Train Based on Optimized Random Matrix Theory

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 187523-187530
Author(s):  
Hailin Hu ◽  
Fu Feng ◽  
Xu Zhou ◽  
Jiewei Zeng ◽  
Ping Wang ◽  
...  
Keyword(s):  
Fault Detection ◽  
Random Matrix Theory ◽  
Random Matrix ◽  
Matrix Theory ◽  
Detection Method ◽  
Maglev Train ◽  
Suspension Systems

scholarly journals Optimized KLD-Based Fault Detection Method for Complex System Under Multi-Operation Conditions

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 163917-163925
Author(s):  
Shi Liang ◽  
Zhiqiang Long ◽  
Xu Zhou ◽  
Ping Wang
Keyword(s):  
Complex System ◽  
Fault Detection ◽  
Detection Method ◽  
Operation Conditions

scholarly journals Multi-Model Switching Based Fault Detection for the Suspension System of Maglev Train

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 6831-6841 ◽  
Author(s):  
Ping Wang ◽  
Zhiqiang Long ◽  
Ning Dang
Keyword(s):  
Fault Detection ◽  
Suspension System ◽  
Maglev Train

A Fault Detection Approach Based on Sound Signal Analysis for Equipment Monitoring

2020 ◽  
pp. 129-139
Author(s):  
Weihai Sun ◽  
Lemei Han
Keyword(s):  
Fault Detection ◽  
Signal Analysis ◽  
Energy Analysis ◽  
Detection Method ◽  
Sound Signal ◽  
Practical Significance ◽  
Time Frequency ◽  
Detection Approach ◽  
Machine Fault ◽  
Learning Data

Machine fault detection has great practical significance. Compared with the detection method that requires external sensors, the detection of machine fault by sound signal does not need to destroy its structure. The current popular audio-based fault detection often needs a lot of learning data and complex learning process, and needs the support of known fault database. The fault detection method based on audio proposed in this paper only needs to ensure that the machine works normally in the first second. Through the correlation coefficient calculation, energy analysis, EMD and other methods to carry out time-frequency analysis of the subsequent collected sound signals, we can detect whether the machine has fault.

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