Application of an Improved Particle Swarm Optimization for Fault Diagnosis

In this paper, the feasibility of using probabilistic causal-effect model is studied and we apply it in particle swarm optimization algorithm (PSO) to classify the faults of mine hoist. In order to enhance the PSO performance, we propose the probability function to nonlinearly map the data into a feature space in probabilistic causal-effect model, and with it, fault diagnosis is simplified into optimization problem from the original complex feature set. The proposed approach is applied to fault diagnosis, and our implementation has the advantages of being general, robust, and scalable. The raw datasets obtained from mine hoist system are preprocessed and used to generate networks fault diagnosis for the system. We studied the performance of the improved PSO algorithm and generated a Probabilistic Causal-effect network that can detect faults in the test data successfully. It can get >90% minimal diagnosis with cardinal number of fault symptom sets greater than 25.