The increasing complexity of home appliances necessitates robust fault detection systems to ensure operational safety and reduce maintenance costs.Therefore, we developed an acousticvibration fusion framework based on sparse representation (AVF-SR) to enable intelligent fault monitoring in home appliances.By integrating contact-based vibration sensors (accelerometers) and noncontact acoustic sensors (micro-electro-mechanical system microphones), AVF-SR overcomes the limitations of single-modality systems in noisy household environments.In AVF-SR, we used multiscale wavelet decomposition and an adaptive feature fusion mechanism based on the Fisher Discrimination Criterion to adjust sensor weights according to appliance state.The experimental results on a drum-type washing machine, a split-type air conditioner, and a refrigerator demonstrated that adaptive fusion improves identification accuracy by 15 to 25% compared with those of static weighting methods.SR enhanced recognition accuracy from 65 to over 85% even under low signal-to-noise ratio (-5 dB) conditions.Diagnostic confidence scores ranged from 0.72 to 0.85.The results of this study provide a basis for the development of advanced, lightweight sensors for deployment on low-power microcontrollers in consumer electronics.The limitations of this study related to fault types and laboratory-controlled simulations necessitate further research to enhance model generalizability across diverse appliances and investigate self-supervised learning, which addresses the scarcity of labeled fault data in home appliance deployments.
Zhao et al. (Mon,) studied this question.