An overview of fault monitoring and diagnosis in mining equipment

Abstract

Proper detection and diagnosis of failing system components is crucial to efficient mining operations. However, the harsh mining environment offers special challenges to these types of actions. The atmosphere is damp, dirty, and potentially explosive, and equipment is located in confined areas far from shop facilities. These conditions, coupled with the increasing cost of downtime and complexity of mining equipment, have forced researchers and operators to investigate alternatives for improving equipment maintainability. This paper surveys monitoring and diagnosis technologies that offer opportunities for improving equipment availability in mining. Expert systems, model-based approaches, and neural nets are each discussed in the context of fault detection and diagnosis. The paper concludes with a comparative discussion summarizing the advantages and disadvantages of each.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>