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.

scholarly journals A Hybrid Model Based on Wavelet Decomposition-Reconstruction in Track Irregularity State Forecasting

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Chaolong Jia ◽  
Lili Wei ◽  
Hanning Wang ◽  
Jiulin Yang
Keyword(s):  
Time Series ◽  
Signal Analysis ◽  
Wavelet Decomposition ◽  
Forecast Model ◽  
Practical Significance ◽  
Frequency Signal ◽  
Time Frequency ◽  
Simple Basis ◽  
Best Fitting ◽  
Track Irregularity

Wavelet is able to adapt to the requirements of time-frequency signal analysis automatically and can focus on any details of the signal and then decompose the function into the representation of a series of simple basis functions. It is of theoretical and practical significance. Therefore, this paper does subdivision on track irregularity time series based on the idea of wavelet decomposition-reconstruction and tries to find the best fitting forecast model of detail signal and approximate signal obtained through track irregularity time series wavelet decomposition, respectively. On this ideology, piecewise gray-ARMA recursive based on wavelet decomposition and reconstruction (PG-ARMARWDR) and piecewise ANN-ARMA recursive based on wavelet decomposition and reconstruction (PANN-ARMARWDR) models are proposed. Comparison and analysis of two models have shown that both these models can achieve higher accuracy.

A Gear Chain Fault Detection Method Using an Adaptive Interference Canceling

2007 ◽  
Vol 345-346 ◽  
pp. 1303-1306
Author(s):  
Bum Won Bae ◽  
In Pil Kang ◽  
Yeon Sun Choi
Keyword(s):  
Fault Detection ◽  
Signal Analysis ◽  
Detection Method ◽  
Gear Tooth ◽  
Vibration Signal ◽  
Gear Fault ◽  
Diagnosis Method ◽  
Vibration Signal Analysis ◽  
Processing Techniques ◽  
Signal Processing Techniques

A fault diagnosis method based on wavelet and adaptive interference canceling is presented for the identification of a damaged gear tooth. A damaged tooth of a certain gear chain generates impulsive signals that could be informative to fault detections. Many publications are available not only for the impulsive vibration signal analysis but the application of signal processing techniques to the impulsive signal detections. However, most of the studies about the gear fault detection using the impulsive vibration signals of a driving gear chain are limited to the verification of damage existence on a gear pair. Requirements for more advanced method locating damaged tooth in a driving gear chain should be a motivation of further studies. In this work an adaptive interference canceling combined with wavelet method is used for a successful identification of the damaged tooth location. An application of the wavelet technique provides a superior resolution for the damage detection to the traditional frequency spectrum based methods. An analysis and experiment with three pair gear chain show the feasibility of this study yielding a precise location of the damaged gear tooth.

scholarly journals A Method of DC Arc Detection in All-Electric Aircraft

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4190
Author(s):  
Teng Li ◽  
Zhijie Jiao ◽  
Lina Wang ◽  
Yong Mu
Keyword(s):  
Fault Detection ◽  
Real Time ◽  
Power Distribution ◽  
Distribution System ◽  
Detection Method ◽  
Discrete Wavelet ◽  
Power Distribution System ◽  
Time Frequency ◽  
Intelligent Recognition ◽  
Dc Arc

Arc faults in an aircraft’s power distribution system (PDS) often leads to cable and equipment damage, which seriously threatens the personal safety of the passengers and pilots. An accurate and real-time arc fault detection method is needed for the Solid-State Power Controller (SSPC), which is a key protection equipment in a PDS. In this paper, a new arc detection method is proposed based on the improved LeNet5 Convolutional Neural Network (CNN) model after a Time–Frequency Analysis (TFA) of the DC currents was obtained, which makes the arc detection more real-time. The CNN is proposed to detect the DC arc fault for its advantage in recognizing more time–frequency joint details in the signals; the new structure also combines the adaptive and multidimensional advantages of the TFA and image intelligent recognition. It is confirmed by experimental data that the combined TFA–CNN can distinguish arc faults accurately when the whole training database has been repeatedly trained 3 to 5 times. For the TFA, two kinds of methods were compared, the Short-Time Fourier Transform (STFT) and Discrete Wavelet Transform (DWT). The results show that DWT is more suitable for DC arc fault detection. The experimental results demonstrated the effectiveness of the proposed method.

Research on Arc Fault Characteristics and Detection Method

2013 ◽  
Vol 331 ◽  
pp. 133-136
Author(s):  
Shao Hua Ma ◽  
Jie Qiu Bao ◽  
Zhi Yuan Cai ◽  
Chui Yi Meng
Keyword(s):  
Fault Detection ◽  
Detection Method ◽  
Electrical Equipment ◽  
Testing System ◽  
Fault Current ◽  
Current Waveform ◽  
Fault Testing ◽  
Time Frequency ◽  
Set Up ◽  
Fault Mechanism

According to the arc fault mechanism of production and refer to UL1699 standard,arc fault testing system is set up,and the current waveform of arc fault and electrical equipment are collected. By analyzing time-frequency characteristics of arc fault current,paper indicates the boundedness of traditional arc fault detection method and present a brand new arc fault detection method based on fractal theory.The effectiveness and accuracy of this method is tested and verified with simulation.

Vibrations Signal Analysis for Generator Machine Fault Detection

2020 ◽  
Author(s):  
Febus Reidj G. Cruz ◽  
Alejandro H. Ballado ◽  
Daniel Phillip Valmocena ◽  
Dwight G. Teves ◽  
Jose Miguel Domingo
Keyword(s):  
Fault Detection ◽  
Signal Analysis ◽  
Machine Fault
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