Application of Probabilistic Causal-effect Model based Artificial Fish-Swarm Algorithm for Fault Diagnosis in Mine Hoist
This paper presents an intelligent methodology for diagnosing incipient faults in mine hoist. As Probabilistic Causal-effect Model-Based diagnosis is an active branch of Artificial Intelligent, in this paper, the feasibility of using probabilistic causal-effect model is studied and it is applied in Artificial Fish-Swarm Algorithm (AFSA) to classify the faults of mine hoist. In probabilistic causal-effect model, the authors employed probability function to nonlinearly map the data into a feature space, and with it, fault diagnosis is simplified into optimization problem from the original complex feature set. And an improved distance evaluation technique is proposed to identify different abnormal cases.