Distributed optical fibre sensor for condition monitoring of mining conveyor using wavelet transform and artificial neural network

Abstract

Condition monitoring of mining conveyors is highly essential, not only to reduce economic loss due to sudden component breakdown but also to prevent safety risks to maintenance personnel. The vibration measurement by means of Distributed Optical Fibre Sensors (DOFS) is a potential solution to provide cost-effective real-time monitoring of mining conveyors. However, an effective application of detecting early damage and damage progression mainly depends on collection quality of optical signal and robust signal processing methods. Therefore, this study focuses on the identification of an effective signal processing method for real-time monitoring of mining conveyor systems. A prototype conveyor system with various levels of idler damages was built to run experimental tests and to collect optical signals for further analysis. A wavelet-based damage detection method is proposed, and its efficiency is evaluated. Additionally, Artificial Neural Network (ANN) is integrated to develop a smart fault detection technology for fast detection and damage classification. The proposed method has 99% accuracy in classify damage condition under varying coveyor operating speed.