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Developing fault detection is very important for improving the equipment reliability and saving energy consumption. As compared with neural networks, extreme learning machine (ELM) is based on statistical learning theory, which has advantages of better classification ability and generalization performance. This paper presents a novel approach for fault detection and diagnosis based on Firefly-Chaos Algorithm and Extreme Learning Machine. The experiment result indicates this proposed method is effective for Analog Circuit fault detection and diagnosis and the model generalization ability is favorable.

Optimization techniques are becoming more popular for the improvement in control of induction motor. Many intelligent algorithms have been used to improve performance of induction motor so for including particle swarm optimization. However, the improved performance may be limited on account of inertia coefficient in particle swarm optimization, which lead to the unbalance between the searching step and searching precision. In this paper, a variable-step nonlinear dynamic inertia weight of particle swarm optimization speed controller is proposed to improve the performance of an induction motor. The experiment results show that the proposed method has excellent performance.