Optimized Deep Learning-Based Fault Detection and Classification for New Energy Vehicles Using CLSTM and Deep Belief Network

New energy vehicles (NEVs) are critical for reducing carbon emissions, using renewable energy sources and supporting energy-loading applications. At the same time, efficient fault identification for their rolling bearings is required to maintain the vehicle’s safe and efficient functioning. Fault detection and classification (FDC) is critical in assuring the safety and dependability of NEVs. The NEV’s powertrain and energy storage, including the drive of an electric motor with a battery, are crucial components prone to many failures. Failure to promptly find and correct these flaws might result in NEVS breakdowns and potentially catastrophic accidents. The FDD approach consists of two steps: extraction of features and fault classification. Feature extraction entails finding relevant dimensions or traits from the NEV’s sensors and signals, allowing the AI system to recognize meaningful patterns. Subsequently, fault classification uses AI algorithms to categorize and identify individual faults based on extracted attributes, allowing for more efficient NEV diagnosis and maintenance. To detect motor faults in new energy vehicles (NEVs), optimal deep learning approaches have been presented by combining DL with optimization for fault identification and classification. They are specifically creating a CLSTM (Convolutional LSTM) framework for preprocessing NEV vibration signals and extracting fault-relevant characteristics. The parameters are optimized via frog leap optimization. The failure state is then classified using a Deep Belief Network (DBN). The results demonstrate that the suggested ODL-DBN approach can reliably and robustly recognize various defects. Furthermore, the training time drastically decreased to only 15 s, and the accuracy remained above 98%. Because of its data-driven nature, the suggested ODL-DBN could diagnose defects in new energy vehicles, benefiting less-carbon energy applications.