Enhanced feature extraction method for motor fault diagnosis using low-quality vibration data from wireless sensor networks

2020 ◽  
Vol 31 (4) ◽  
pp. 045016 ◽  
Author(s):  
Qing Shu ◽  
Siliang Lu ◽  
Min Xia ◽  
Jianming Ding ◽  
Jiahao Niu ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Feature Extraction ◽  
Sensor Networks ◽  
Fault Diagnosis ◽  
Extraction Method ◽  
Wireless Sensor ◽  
Feature Extraction Method ◽  
Vibration Data

Research on a feature extraction method for local faults in planetary gearboxes based on improved dynamic time warping

2021 ◽  
Vol 63 (8) ◽  
pp. 465-471
Author(s):  
Shang Zhiwu ◽  
Yu Yan ◽  
Geng Rui ◽  
Gao Maosheng ◽  
Li Wanxiang
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Frequency Domain ◽  
Extraction Method ◽  
Dynamic Time Warping ◽  
Spectral Feature ◽  
Time Warping ◽  
Feature Extraction Method ◽  
Time Frequency ◽  
Dynamic Time

Aiming at the local fault diagnosis of planetary gearbox gears, a feature extraction method based on improved dynamic time warping (IDTW) is proposed. As a calibration matching algorithm, the dynamic time warping method can detect the differences between a set of time-domain signals. This paper applies the method to fault diagnosis. The method is simpler and more intuitive than feature extraction methods in the frequency domain and the time-frequency domain, avoiding their limitations and disadvantages. Due to the shortcomings of complex calculation, singularity and poor robustness, the paper proposes an improved method. Finally, the method is verified by envelope spectral feature analysis and the local fault diagnosis of gears is realised.

scholarly journals Fault diagnosis of wind turbine bearing using wireless sensor networks

2017 ◽  
Vol 21 (suppl. 2) ◽  
pp. 523-531 ◽  
Author(s):  
Indhu Ramalingam ◽  
Sankaran Annamalai ◽  
Sugumaran Vaithiyanathan
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Wireless Sensor ◽  
Turbine Bearing
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