{"id":7024,"date":"2026-05-21T21:43:31","date_gmt":"2026-05-21T13:43:31","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=7024"},"modified":"2026-05-21T22:57:15","modified_gmt":"2026-05-21T14:57:15","slug":"jase-202609-32-051","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202609-32-051","title":{"rendered":"Multi-Algorithm Collaborative Temperature Sensing in Wireless Power Systems: A Data-Driven Early Fault Detection Method"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 32<\/a><\/p>\n<\/div>\n\n\n\n

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Xueli Guo<\/i><\/a> , Jie Wang, Haijun Wang, Wanke Ma, Feng Zheng, and Kai Li<\/p>\n\n\n\n

State Grid Nanyang Power Supply Company Economic and Technical Research Institute, Nanyang, 473000, China.<\/p>\n<\/div>\n\n\n\n

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Received: March 2, 2026
Accepted: April 8, 2026
Publication Date: May 21, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

Comparison of Accuracy and Precision<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:\u00a0 BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202609_32.051<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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The operation of wireless power systems (WPS) requires temperature monitoring because accurate temperature measurements help to detect overheating which serves as a fault warning. This research introduces an early fault detection system that uses multiple temperature sensing algorithms to analyze data for equipment failures. The Internet of Things (IoT) network gathers thermal measurements from various sensor units which undergo pre-processing through Savitzky-Golay filtering for noise reduction and principal component analysis for feature extraction. The Efficient Decision-tuned Least Squares Support Vector Machine (EDLSSVM) conducts fault detection while the Decision Tree (DT) model produces understandable rules to analyze threshold-crossing patterns. The two models combine their outputs through a consensus process which decreases false positive results and enhances overall system trustworthiness. The hybrid system combines nonlinear modeling with understandable rules to boost classification results which creates a dependable system that detects system faults and performs predictive maintenance in current WPS systems. The proposed consensus mechanism uses adaptive weights based on model confidence and decision consistency, which distinguishes it from traditional ensemble methods. The system automatically resolves conflicts by treating all models as equal. The research used both simulated data and real-time data to conduct their tests under different environmental conditions which included both STC and CEC testing scenarios. The multi-algorithm approach outperforms single-model methods, achieving 0.982 accuracy, 0.96 precision, 0.98 recall, and 0.97 F1-score. The results show that IoT monitoring systems combined with collaborative machine learning enable efficient real-time fault detection and predictive maintenance capabilities for modern WPS systems.<\/p>\n\n\n\n

Keywords: Wireless power systems (WPS), temperature sensing, early fault detection, Internet of Things (IoT), Efficient Decision-tuned Least Squares Support Vector Machines (EDLSSVM).<\/em><\/p>\n\n\n\n

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  1. [1] A. Harish, A. Prince, and M. V. Jayan, (2022) \u201cFault detection and classification for wide area backup protection of power transmission lines using weighted extreme learning machine\u201d IEEE Access 10: 82407\u201382417. DOI: 10.1109\/ACCESS.2022.3196769.<\/li>\n
  2. [2] M. Irfan et al., (2025) \u201cDesign of a novel noise-resilient algorithm for fault detection in wind turbines on a supervisory control and data acquisition system\u201d Scientific Reports 15: 13308. DOI: 10.1038\/s41598-025-97663-3.<\/li>\n
  3. [3] D. Thomas, (2024) \u201cUnveiling wind turbine failures: causes, detection, and prevention for enhanced reliability\u201d Journal of Failure Analysis and Prevention 24: 2051\u20132053. DOI: 10.1007\/s11668-024-02026-1.<\/li>\n
  4. [4] L. Cao, Z. Wang, and Y. Yue, (2022) \u201cAnalysis and prospect of the application of wireless sensor networks in ubiquitous power Internet of Things\u201d Computational Intelligence and Neuroscience 20: 9004942. DOI: 10.1155\/2022\/9004942.<\/li>\n
  5. [5] J. Liu, Z. Zhao, J. Ji, and M. Hu, (2020) \u201cResearch and application of wireless sensor network technology in the power transmission and distribution system\u201d Intelligent and Converged Networks 1: 199\u2013220. DOI: 10.23919\/ICN.2020.0016.<\/li>\n
  6. [6] G. W. Sun et al., (2022) \u201cA wireless sensor network node fault diagnosis model based on a belief rule base with power set\u201d Heliyon 8: e10879. DOI: 10.1016\/j.heliyon.2022.e10879.<\/li>\n
  7. [7] X. Zou et al., (2023) \u201cCurrent status and prospects of research on sensor fault diagnosis of agricultural Internet of Things\u201d Sensors 23(5): 2528. DOI: 10.3390\/s23052528.<\/li>\n
  8. [8] T. Mahmood et al., (2022) \u201cAn intelligent fault detection approach based on a reinforcement learning system in a wireless sensor network\u201d The Journal of Supercomputing 78: 3646\u20133675. DOI: 10.1007\/s11227-021-04001-1.<\/li>\n
  9. [9] N. Nathiya, C. Rajan, and K. Geetha, (2025) \u201cA hybrid optimization and ML-based energy-efficient clustering algorithm with self-diagnosis data fault detection and prediction for WSN-IoT application\u201d Peer-to-Peer Network-ing and Applications 18: 13. DOI: 10.1007\/s12083-024-01892-8.<\/li>\n
  10. [10] M. S. Salhi et al., (2024) \u201cOn the use of wireless sensor nodes for agricultural smart fault detection\u201d Wireless Personal Communications 134: 95\u2013117. DOI: 10.1007\/s11277-024-10889-8.<\/li>\n
  11. [11] S. Lavanya et al., (2021) \u201cA tuned classification approach for efficient heterogeneous fault diagnosis in IoT-enabled WSN applications\u201d Measurement 183: 109771. DOI: 10.1016\/j.measurement.2021.109771.<\/li>\n
  12. [12] R. Prasad and K. B. Baghel, (2021) \u201cA novel fault diagnosis technique for wireless sensor networks using a feedforward neural network\u201d IEEE Sensors Letters 6: 1\u20134. DOI: 10.1109\/LSENS.2021.3136590.<\/li>\n
  13. [13] F. Fan et al., (2023) \u201cAn optimized ML technology scheme and its application in fault detection in wireless sensor networks\u201d Journal of Applied Statistics 50: 592\u2013609. DOI: 10.1080\/02664763.2021.1929089.<\/li>\n
  14. [14] D. Dong and H. Feng, (2024) \u201cDesign and use of a wireless temperature measurement network system integrating artificial intelligence and blockchain in electrical power engineering\u201d PLoS One 19: e0296398. DOI: 10.1371\/journal.pone.0296398.<\/li>\n
  15. [15] M. Li et al., (2020) \u201cA data-driven method for fault detection and isolation of the integrated energy-based district heating system\u201d IEEE Access 8: 23787\u201323801. DOI: 10.1109\/ACCESS.2020.2970273.<\/li>\n
  16. [16] G. Wang et al., (2021) \u201cPower transformer fault diagnosis system based on Internet of Things\u201d EURASIP Journal on Wireless Communications and Networking 2021(21): 1\u201321. DOI: 10.1186\/s13638-020-01871-6.<\/li>\n
  17. [17] A. Quispe-Astorga et al., (2025) \u201cData-driven fault detection and diagnosis in cooling units using sensor-based ML classification\u201d Sensors 25: 3647. DOI: 10.13390\/s25123647.<\/li>\n
  18. [18] H. Ruan et al., (2022) \u201cDeep learning-based fault prediction in wireless sensor network embedded cyber-physical systems for industrial processes\u201d IEEE Access 10: 10867\u201310879. DOI: 10.1109\/ACCESS.2022.3144333.<\/li>\n
  19. [19] S. Yao et al., (2021) \u201cIntelligent and data-driven fault detection of photovoltaic plants\u201d Processes 9: 1711. DOI: 10.3390\/pr9101711.<\/li>\n
  20. [20] B. Feng et al., (2024) \u201cDistributed chaotic bat algorithm for sensor fault diagnosis in AHUs based on a decentralized structure\u201d Journal of Building Engineering 95: 110031. DOI: 10.1016\/j.jobe.2024.110031.<\/li>\n
  21. [21] U. Shah, (2024) \u201cFault detection and diagnosis in electric vehicle systems using IoT and ML: A support vector machine approach\u201d Journal of Electrical Systems 20: 990\u2013999. DOI: 10.52783\/jes.1414.<\/li>\n
  22. [22] C.-Y. Lu et al., (2025) \u201cDevelopment and validation of an explainable hybrid deep learning model for multiple-fault diagnosis in intelligent automotive electronic systems\u201d Electronics 14(22): 4488. DOI: 10.3390\/electronics14224488.<\/li>\n<\/ol>\n<\/div>\n\n\n\n

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