Application of a Group Search Optimization based Artificial Neural Network to Machine Condition Monitoring

Artificial Neural Networks (ANNs) have been applied to machine condition monitoring. This paper first addresses a ANN trained by Group Search Optimizer (GSO), which is a novel population based optimization algorithm inspired by animal social foraging behaviour. The global search performance of GSO has been proven to be competitive to other evolutionary algorithms, such as Genetic Algorithms (GAs) and Particle Swarm Optimizer (PSO). Herein, the parameters of a 3-layer feed-forward ANN, including connection weights and bias are tuned by the GSO algorithm. Secondly the GSO based ANN is applied to model and analysis ultrasound data recorded from grinding machines to distinguish different conditions. The real experimental results show that the proposed method is capable to indicate the malfunction of machine condition from the ultrasound data.