An Arc Fault Detection Method Based on Current Amplitude Spectrum and Sparse Representation

2019 ◽  
Vol 68 (10) ◽  
pp. 3785-3792 ◽  
Author(s):  
Na Qu ◽  
Jianhui Wang ◽  
Jinhai Liu
Keyword(s):  
Fault Detection ◽  
Sparse Representation ◽  
Detection Method ◽  
Amplitude Spectrum ◽  
Current Amplitude

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.

An enhanced fault-detection method based on adaptive spectral decomposition and super-resolution deep learning

2019 ◽  
Vol 7 (3) ◽  
pp. T713-T725
Author(s):  
Zhenyu Yuan ◽  
Handong Huang ◽  
Yuxin Jiang ◽  
Jinbiao Tang ◽  
Jingjing Li
Keyword(s):  
Deep Learning ◽  
Fault Detection ◽  
Spectral Decomposition ◽  
Detection Method ◽  
Eastern China ◽  
Amplitude Spectrum ◽  
Super Resolution ◽  
Dominant Frequency ◽  
Seismic Survey ◽  
Amplitude Data

Coherence is widely used for detecting faults in reservoir characterization. However, faults detected from coherence may be contaminated by some other discontinuities (e.g., noise and stratigraphic features) that are unrelated to faults. To further improve the accuracy and efficiency of coherence, preprocessing or postprocessing techniques are required. We developed an enhanced fault-detection method with adaptive scale highlighting and high resolution, by combining adaptive spectral decomposition and super-resolution (SR) deep learning into coherence calculation. As a preprocessing technique, adaptive spectral decomposition is first proposed and applied on seismic data to get a dominant-frequency-optimized amplitude spectrum, which has features of scale focus and multiple resolution. Eigenstructure-based coherence with dip correction is then calculated to delineate fault discontinuities. Following the remarkable success of SR deep learning in image reconstruction, a convolutional neural network (CNN) model is built and it then takes fault-detection images as the input to achieve enhanced results. The effectiveness of our proposed method is validated on a seismic survey acquired from Eastern China. Examples demonstrate that coherence from adaptive amplitude spectrum without dip correction is comparable to the dip-corrected one from seismic amplitude data at a certain degree, and they even highlight the specific scale of fault targets. Comparing fault detections from adaptive spectrum and some specific-frequency components, it can be concluded that adaptive spectral-based coherence highlights the primary scale of faults at various depths with only one single volume of data, thus improving the interpretation efficiency and reducing storage cost. Furthermore, with the trained CNN model, the resolution and signal-to-noise ratio of coherence images are effectively improved and the continuity of detected fault is promisingly enhanced.

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.

A fault detection method for new PV module strufure

2010 ◽  
Vol 24 (2) ◽  
pp. 131-136 ◽  
Author(s):  
Ze Cheng ◽  
Bingfeng Li ◽  
Li Liu ◽  
Yanli Liu
Keyword(s):  
Fault Detection ◽  
Detection Method ◽  
Pv Module
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