A novel fault detection method for flight data based on wavelet transform algorithm

2016 ◽  
Author(s):  
Haoqiang Liu ◽  
Hongbo Zhao ◽  
Lei Chen ◽  
Wenquan Feng
Keyword(s):  
Wavelet Transform ◽  
Fault Detection ◽  
Detection Method ◽  
Flight Data

Research on Fault Detection Method of Converters

2012 ◽  
Vol 229-231 ◽  
pp. 1150-1153
Author(s):  
Wen Zhong Ma ◽  
Ke Cheng Chen ◽  
Yang Shan ◽  
Yan Li Wang
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Fault Detection ◽  
Fault Location ◽  
Detection Method ◽  
Fault Tolerant ◽  
Experimental Results ◽  
The Neural Network ◽  
Operation Control ◽  
Fault Information

The influence of converter faults to the system is introduced and the fault detection method based on wavelet transform and neural network is proposed in this paper. The fault information can be decomposed by wavelet transform, then the fault eigenvectors can be extracted and put into neural network for training and testing. Finally the neural network outputs specific codes, and thus the fault location and fault components of converters are confirmed, which lays the foundation for the fault-tolerant operation control of converters. Simulation and experimental results show the correctness and effectiveness of the method.

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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