Belt Conveyors Rollers Diagnostics Based on Acoustic Signal Collected Using Autonomous Legged Inspection Robot

Abstract

Growing demand for raw materials forces mining companies to reach deeper deposits. Difficult environmental conditions, especially high temperature and the presence of toxic/explosives gases, as well as high seismic activity in deeply located areas, pose serious threats to humans. In such conditions, running an exploration strategy of machinery parks becomes a difficult challenge, especially from the point of view of technical facilities inspections performed by mining staff. Therefore, there is a growing need for new, reliable, and autonomous inspection solutions for mining infrastructure, which will limit the role of people in these areas. In this article, a method for detection of conveyor rollers failure based on an acoustic signal is described. The data were collected using an ANYmal autonomous legged robot inspecting conveyors operating at the Polish Ore Enrichment Plant of KGHM Polska Miedź S.A., a global producer of copper and silver. As a part of an experiment, about 100 m of operating belt conveyor were inspected. The sound-based fault detection in the plant conditions is not a trivial task, given a considerable level of sonic disturbance produced by a plurality of sources. Additionally, some disturbances partially coincide with the studied phenomenon. Therefore, a suitable filtering method was proposed. Developed diagnostic algorithms, as well as ANYmal robot inspection functionalities and resistance to underground conditions, are developed as a part of the “THING–subTerranean Haptic INvestiGator” project.

Acoustic Anomaly Detection of Mechanical Failures in Noisy Real-Life Factory Environments

“…When applied in an unsupervised way, the approach can be used for predicting the remaining useful life in the absence of available run-to-failure data, as was done in [22] using the autoencoder based methodology to analyze the vibrations of a robotic arm. Skoczylas et al [23] used a diagnostic feature extracted from the spectral coefficients of the acoustic signal to identify the faulty operation of the rotating elements of the belt conveyor using the autocorrelation characteristics. Ho et al [24] suggested using Blind Source Separation as a signal decomposition approach to analyze vibration data of rotating bearings for the detection of fault patterns and signatures.…”

Procedures of Detecting Damage to a Conveyor Belt with Use of an Inspection Legged Robot for Deep Mine Infrastructure

“…This work is a part of the THING (subTerranean Haptic INvestiGator) project, which involves using a modified commercial robot to take over the necessary monitoring processes currently performed by humans [34][35][36]. The inspection procedure, with the use of this apparatus together with the description of other algorithms (damage detection based on acoustics and thermovision), has been presented in the previous works [37,38].…”

“…which involves using a modified commercial robot to take over the necessary monitoring processes currently performed by humans [34][35][36]. The inspection procedure, with the use of this apparatus together with the description of other algorithms (damage detection based on acoustics and thermovision), has been presented in the previous works [37,38]. For the purposes of the inspection, the robot walked around the conveyor route and recorded a video.…”

Inspection Robotic UGV Platform and the Procedure for an Acoustic Signal-Based Fault Detection in Belt Conveyor Idler

“…There are plenty of articles focused on the diagnostics of drive units (gearboxes, pulleys) using vibration analysis or infrared thermography [2][3][4][5][6] or temperature [7]. The conveyor belt has been defined as one of the most expensive component in conveyor, thus various NDT techniques (image analysis, laser scanning, magnetic field measurement) have been applied [8][9][10][11][12][13].…”