Improved S Transform Based Fault Detection Method in VSC Interfaced DC System

2020 ◽  
pp. 1-1
Author(s):  
Dongyu Li ◽  
Abhisek Ukil ◽  
Kuntal Satpathi ◽  
Yew Ming Yeap
Keyword(s):  
Fault Detection ◽  
Detection Method ◽  
Dc System ◽  
S Transform

Gearbox Fault Detection Using Hilbert and TT-Transform

2005 ◽  
Vol 293-294 ◽  
pp. 79-86 ◽  
Author(s):  
Xianfeng Fan ◽  
Ming J. Zuo
Keyword(s):  
Fault Detection ◽  
Hilbert Transform ◽  
Detection Method ◽  
Vibration Signal ◽  
Fault Detection And Diagnosis ◽  
Time Representation ◽  
Machine Vibration ◽  
S Transform ◽  
Gearbox Vibration ◽  
Detection And Diagnosis

Machine vibration signal has been used in fault detection and diagnosis. Modulation and non-stationarity existing in the signal generated by a faulty gearbox present challenges to effective fault detection. Hilbert transform has the ability to address the modulation issue. This paper outlines a novel fault detection method called Hilbert & TT-transform (HTT-transform) which combines Hilbert transform and TT-transform obtained from the inverse Fourier transform of the S-transform. The principle of the proposed method is to analyze the modulating signal created by a faulty gear using a time-time representation. The method has the advantage of providing a new way of localizing the time features of the modulating signal around a particular point on the time axis through scaled windows. It is verified with simulated signals and real gearbox vibration signals. The results obtained by CWT, S-transform, TT- transform, and HTT-transform are compared. They show that utilizing the proposed method can improve the effectiveness of gearbox fault detection.

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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