Miao Li, Fanyong Cheng*, Jiong Yang, Maxwell Mensah Duodu, Hao Tu
Energy Engineering, Vol.121, No.9, pp. 2543-2568, 2024, DOI:10.32604/ee.2024.051231
Abstract Accurate and reliable fault detection is essential for the safe operation of electric vehicles. Support vector data description (SVDD) has been widely used in the field of fault detection. However, constructing the hypersphere boundary only describes the distribution of unlabeled samples, while the distribution of faulty samples cannot be effectively described and easily misses detecting faulty data due to the imbalance of sample distribution. Meanwhile, selecting parameters is critical to the detection performance, and empirical parameterization is generally time-consuming and laborious and may not result in finding the optimal parameters. Therefore, this paper proposes a… More >
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