An online data driven fault detection method in dynamic process based on sparse representation

2017 ◽  
Author(s):  
Rui Yang ◽  
Mengjie Huang
Keyword(s):  
Fault Detection ◽  
Sparse Representation ◽  
Dynamic Process ◽  
Detection Method ◽  
Data Driven ◽  
Online Data

scholarly journals A Data-driven Fault Detection Method Based on Dissipative Trajectories

2016 ◽  
Vol 49 (7) ◽  
pp. 717-722
Author(s):  
Qingyang Lei ◽  
Muhammad Tajammal Munir ◽  
Jie Bao ◽  
Brent Young
Keyword(s):  
Fault Detection ◽  
Detection Method ◽  
Data Driven

scholarly journals An Arc Fault Detection Method Based on Multidictionary Learning

2018 ◽  
Vol 2018 ◽  
pp. 1-8
Author(s):  
Na Qu ◽  
Jianhui Wang ◽  
Jinhai Liu ◽  
Zhi Wang
Keyword(s):  
Fault Detection ◽  
Sparse Representation ◽  
Dictionary Learning ◽  
Detection Method ◽  
Amplitude Spectrum ◽  
Learning Method ◽  
Test Results ◽  
Training Samples ◽  
Simulation Results ◽  
Normal Work

This paper uses the dictionary learning of sparse representation algorithm to detect the arc fault. Six kinds of characteristics, that is, the normalized amplitudes of 0Hz, 50Hz, 100Hz, 150Hz, 200Hz, and 250Hz in the current amplitude spectrum, are used as inputs. The output is normal work or arc fault. Increasing the number of training samples can improve the accuracy of the tests. But if the training samples are too many, it is difficult to be expressed by single dictionary. This paper designs a multidictionary learning method to solve the problem. Firstly, n training samples are selected to form s overcomplete dictionaries. Then a dictionary library consisting of s dictionaries is constructed. Secondly, t (t≤s) dictionaries are randomly selected from the dictionary library to judge the test results, respectively. Finally, the final detest result is obtained through the maximum number of votes, that is, the modality with the most votes is the detest result. Simulation results show that the accuracy of detection can be improved.

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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