Real-time fault diagnosis of HEMM using Bayesian Network: A case study on drag system of dragline

Abstract

Undetected faults in heavy earth moving machinery (HEMM) are associated with unwanted failures. Dragline is an HEMM used in coal mining for removal of overburden and dumping it into de-coaled area. Failure of dragline contributed to total downtime of 4955 h in 28-month (2014–16) period and 50% of these downtimes were contributed by its drag system alone. The symptoms those exceeded threshold limit in the drag system during this period was 438, which led to 185 faults and subsequently 16 failures. Therefore, an attempt has been made for real-time fault diagnosis of drag system to analyse the faults by establishing causal relationships between cause, symptom and fault using Bayesian Network (BN). The new set of observed evidences (e.g., symptoms) obtained through built-in sensors or visual inspection during working of dragline can be updated in the BN model. Thereafter fault inference is used to identify the fault, and categorise the degraded and catastrophic faults. Simultaneously the change in probability values of the causes are recorded for the given symptoms. The conflict between the set of observed evidences was detected, and the correctness of the model was validated through conflict analysis. Appropriate condition-based predictive maintenance action can be undertaken by the maintenance engineer for eliminating these faults. © 2020 Elsevier Ltd