Optimization of condition-based maintenance using soft computing

Due to high costs associated with conventional maintenance strategies, application of soft computing in monitoring the condition of equipment to predict the health of various components of machine tools in manufacturing processes has attracted the attention of researchers. Soft computing is a better alternate to predict and optimize the manufacturing processes related to physics-based models as these processes are complex and precarious. The theories of artificial neural systems, fuzzy logic, genetic algorithms, ant colony optimization, simulated annealing and particle swarm optimization are utilized by soft computing techniques to handle real-world issues that cannot be palatably handled utilizing conventional computing methods. This paper presents a state-of-the-art review on the recent developments in the use of soft computing in condition-based maintenance in manufacturing.

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