Neural network electrical machine faults diagnosis based on multi-population GA

Chen, Zaiping; Zhao, Yueming; Zheng, Yang; Lou, Rui

doi:10.1109/ijcnn.2008.4634343

Cited by 2 publications

(1 citation statement)

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“…Besides, GA has been used together with Levenberg-Marquardt (LM) algorithm to train an ANN in an electrical machine fault detection application. The proposed hybrid method between GA and LM overcame the weal local search ability of GA [37]. Besides, the performance of various optimization methods including ANN, fuzzy, GA and Ant Colony Optimization (ACO) and their hybrid models are compared when predicting accidents caused by repair and maintenance in oil refineries [38].…”

Section: Ann and Evolutionary Neural Network Methods For Condition Monitoring And Fault Detectionmentioning

confidence: 99%

Hybridization of harmonic search algorithm in training radial basis function with dynamic decay adjustment for condition monitoring

2021

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In recent decades, hybridization of superior attributes of few algorithms was proposed to aid in covering more areas of complex application as well as improves performance. This paper presents an intelligent system integrating a Radial Basis Function Network with Dynamic Decay Adjustment (RBFN-DDA) with a Harmony Search (HS) to perform condition monitoring in industrial processes. An effective condition monitoring can help reduce unexpected breakdown incidents and facilitate in maintenance. RBDN-DDA performs incremental learning wherein its structure expands by adding new hidden units to include new information. As such, its training can reach stability in a shorter time compared to the gradient-descent based methods. By integrating with the HS algorithm, the proposed metaheuristic neural network (RBFN-DDA-HS) can optimize the RBFN-DDA parameters and improve classification performances from the original RBFN-DDA by 2.2% up to 22.5% in two benchmarks and a real-world condition-monitoring case studies. The results also show that the proposed RBFN-DDA-HS is compatible, if not better than, the classification performances of other state-of-art machine learning methods.

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Section: Ann and Evolutionary Neural Network Methods For Condition Monitoring And Fault Detectionmentioning

confidence: 99%