Aug 2026· Engineering Research Express· Vol 8, pp. 155357· 0 citations· 24 references
Physics
TL;DR
The diagnosis framework constructed in this study effectively reduces the dependence on manual experience and provides technical support for improving the safety and operation level of building electrical systems.
Abstract
In view of the fact that the traditional diagnosis technology cannot meet the increasingly complex reliability requirements of modern building electrical systems, a fault diagnosis method of building electrical systems based on hyperparameter back propagation neural network (BPNN) is proposed in this study. By constructing a three-layer BPNN model and comparing various parameter configurations in the experiment, the optimal number of hidden layer nodes, learning rate and momentum factor are determined. The results show that the diagnostic accuracy of the optimized model is 90% and 85% on the training set and the test set respectively, and its performance is significantly better than that of the traditional rule diagnosis and expert system in short circuit, open circuit, overload and grounding fault identification. The diagnosis framework constructed in this study effectively reduces the dependence on manual experience and provides technical support for improving the safety and operation level of building electrical systems. The follow-up work will focus on introducing advanced feature extraction technology and online learning mechanism to further improve the comprehensiveness of system diagnosis.
The results indicate that phase-angle information provides supplementary and class dependent discriminative value, but does not consistently improve all fault classes, whereas conventional voltage and current measurements alone represent a simpler and more stable alternative, whereas phase-angle measurements may be incorporated when synchronized phasor information is already available.
Zeynep Bala Duranay, İsmail Anıl Avcı, Mohammed Bushra Mohammed et al.· Symmetry· 0 citations
Electromagnetic valves are widely utilized in electrohydraulic control systems because of simple structure, low cost, and fast response. Accurate diagnosis of electromagnetic valve faults is crucial to ensure high effective operation of the control systems. This paper proposes a faults diagnosis model of electromagnetic valve based on Bayesian optimization (BO) and a two‐layer long short‐term memory (LSTM) neural network. First, to fully extract information features from the fault samples, a two‐layer LSTM network structure is used as the core of the electromagnetic valve fault diagnosis framework. Second, the BO algorithm is adopted to automatically adjust the hyperparameters of the two‐layer LSTM network model. The BO algorithm restricts the search space of hyperparameters in each iteration, which enhances the algorithm's efficiency. Moreover, owing to the limited availability of fault samples, cross‐recurrence quantification analysis is utilized for data preprocessing to strengthen and expand the fault characteristic data of the electromagnetic valve. This method effectively improves the diagnostic efficiency of the model. Finally, a real case study on electromagnetic valves is conducted to validate the performance and feasibility of our proposed method. The fault diagnosis accuracy of our proposed method reaches 94.29%, which is significantly higher than other fault diagnosis methods.
J. Pang, Yuanzhong Chen, Jinkun Dai et al.· Quality and Reliability Engi...· 0 citations
This study aimed to develop and implement a cloud-enabled intelligent fault inference system for a photovoltaic installation, using locally acquired electrical and environmental data, preprocessed prior to cloud-based inference.
Claudemiro de Lima Júnior, Mariana da S. M. Sobral, Paulo F. C. Barbosa· Revista Brasileira de Engenh...· 0 citations
An enhanced diagnostic approach integrating the Sparrow Search Algorithm with PNN is developed, which confirms that SSA can substantially strengthen the anti-disturbance capability of PNN, thereby providing reliable technical support for power transformer fault diagnosis and offering certain reference value for enhancing the operational reliability of power grid equipment.
Bo Liu, Jianghong Dong, Shuyu Ren et al.· Journal of Physics, Conferen...· 0 citations
The findings show that the suggested hybrid model works better than conventional techniques, with a fault classification accuracy of 98.66% as opposed to decision trees’ 97.42% and SE-CDAE’s 97.98% accuracy.
Qinghua Chen, Tao Xu, Cheng Zhou et al.· Distributed Generation &...· 0 citations
The proposed framework employs ANN as a nonlinear feature embedding and a Radial Basis Function SVM subsequently classifies using an Error-Correcting Output Codes (ECOC) strategy, validating the effectiveness of the proposed hybridisation strategy for intelligent transmission system protection.
Kudu Abubakar Mohammed, M. Balogun, Adesina M. Lambe et al.· Communication in Physical Sc...· 0 citations
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