Title :
Fault monitoring and diagnosis in mining equipment: current and future developments
Author :
Sottile, Joseph, Jr. ; Holloway, Lawrence E.
Author_Institution :
Kentucky Univ., Lexington, KY, USA
Abstract :
The authors survey monitoring and diagnosis technologies which offer opportunities for improving equipment availability in mining. They briefly present a framework for comparing and contrasting different techniques, and examine the application of expert systems and knowledge-based methods to mining applications. Model-based methods are discussed from the viewpoint of both analytical models and qualitative models. Neural nets and other pattern recognition techniques are described. The special problems of monitoring and diagnosis that mining poses are discussed, and the relative benefits of the various methods are summarized.<>
Keywords :
computerised monitoring; engineering computing; expert systems; failure analysis; fault location; knowledge based systems; mining; pattern recognition; diagnosis technologies; expert systems; fault monitoring; knowledge-based methods; mining equipment; model-based methods; neural nets; pattern recognition; Condition monitoring; Diagnostic expert systems; Electrical fault detection; Fault detection; Fault diagnosis; Manufacturing systems; Mining equipment; Neural networks; Production; Transducers;
Conference_Titel :
Industry Applications Society Annual Meeting, 1992., Conference Record of the 1992 IEEE
Conference_Location :
Houston, TX, USA
Print_ISBN :
0-7803-0635-X
DOI :
10.1109/IAS.1992.244201
