Three-phase induction motors (TPIMs) are prone to numerous faults due to their complicated stator and rotor conditions and require a fast response, accurate, and intelligent diagnostic system. Recently developed fault diagnostic systems for induction motors are based on machine learning approaches, but their complex structure typically results in long training time. Moreover, they need to be retrained from scratch if the system is not accurate. We apply incremental broad learning (IBL) method to the diagnosis of TPIM faults. The IBL can train and retrain the network efficiently due to its flexible structure. The new diagnostic framework also consists of feature extraction techniques (empirical mode decomposition and sample entropy) and a non-negative matrix factorization (NMF) IBL approach. The experimental results demonstrate that the IBL system is superior to some algorithms, such as deep belief networks, convolutional neural networks, and extreme learning machine. Moreover, the IBL simplified by NMF is more accurate than the IBL without NMF. INDEX TERMS Fault diagnosis, feature extraction, incremental board learning, non-negative matrix factorization, three-phase induction motor.
The occurrence of fault in induction motors is dangerous in our daily life. It is significant to diagnose motor component faults accurately and quickly. In this paper, we propose an efficient and responsive motor fault diagnostic method based on Feature Incremental Broad Learning (FIBL) and Singular Value Decomposition (SVD). Firstly, we extract fault features from raw signals with Particle Swarm Optimization-Variation Model Decomposition, Sample Entropy and Time Domain Statistical Features. Secondly, these features are input into a broad learning system to train a network. Then we use FIBL to retrain the network if the diagnosis accuracy is unsatisfactory. Finally, SVD is used to further simplify the system structure to reduce diagnostic errors. In order to evaluate the performance of the diagnostic system, experiments are conducted. Experimental results show that with the proposed diagnostic method, motor component faults detection is quicker and more accurate. INDEX TERMS Fault diagnosis, feature extraction, incremental broad learning, singular value decomposition, induction motor.
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