Research on the fault analysis method of belt conveyor idlers based on sound and thermal infrared image features

The large number, scattered distribution, and complex working environment of idlers makes their faults challenging to detect. In this paper, a fault analysis method of belt conveyor idlers based on sound and thermal infrared image (TII) features is proposed. According to 18 classes of idler sound and TII data, the time-domain (TD) features of sound signals are analysed using statistical methods, the frequency-domain (FD) and time-frequency-domain (TFD) features of sound signals are analysed with the quantization and dimension reduction method based on Fisher's linear discriminant, and the TII features are analysed using statistical methods. The analysis results show that final and catastrophic faults can be detected by using FD features of idler sound and TII temperature rise of the idler outer load area and shaft end, and TFD features of idler sound signals can be used to detect typical bearing defects, which features high reliability, low cost, and easy implementation.

Automated summary

The proposed method combines sound and thermal infrared image features to detect different types of faults in idlers, showing high reliability and easy implementation.