###### 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 Miedz 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 INvestGator\" project. inspection robotics; diagnostics; conveyor; deep mine; mining transport +
Footnote †: journal: Medical Imaging  
**Citation:**  
S. S. A., S.  
which commonly used monitoring systems cannot detect or are too expensive for a wide range of applications. Therefore, it should be highlighted why the role of inspection in the maintenance of mining infrastructure is so important. In practice, there are often thousands of kilometers of conveyors routes that need to be checked from both sides before and after startups. Due to the growing demand for raw materials and exhausting deposits, world mining is forced to descend to even greater depths. Unfortunately, this poses a serious challenge related to the deteriorating geological and mining conditions, together with environmental conditions (temperature, toxic and explosive gases) and the intensification of seismic activity in the rock mass. These will lead to an increased amount of belt conveyor failures and an even larger need for diagnostic systems. In addition, human perception is limited and often results in many injury events and threat or leads to incorrect indications. In practice, many portable monitoring systems for inspection (thermovision, vibrational, magnetic, acoustic) are available on the market but are problematic due to transport and the need to stop the conveyor for the complicated assembly process. Therefore, in the area underground mining, there is a growing technological expectation in terms of new solutions eliminating the role of humans in the mining area. Due to the size and dispersion of the conveyor infrastructure, monitoring systems must be inexpensive, reliable, easy to use, and able to integrate data from various sources. In [1], an Internet of Things and Big Data solution dedicated to conveying a transportation system was described. The authors presented the concept of a smart roller for a conveyor, which measures temperature and has the ability to communicate via RFID (Radio-frequency Identification). The general architecture of such a solution has also been presented but has not been implemented on a larger scale. Currently, such solutions are rare in the mining industry mainly due to the hard, expensive, and long-term implementation. In article [2], the authors present a robot for belt conveyor suspension inspection. The described robot moves along a cable mounted above the conveyor, and the inspection is carried out by a thermographic camera and CO and CH4 sensors.