Intelligent Diagnostic System for Home Dishwashers based on LSTM Method

The efficiency improvement and intelligent diagnosis of ozone-driven thermal process systems have become a key technical challenge in the industry. This study establishes a deep learning-based efficiency diagnosis model, which extracts features from system operation data through wavelet transform and principal component analysis, and constructs a three-layer LSTM network structure for fault prediction. The experimental results show that under the conditions of 60。C temperature and 5mg/L ozone concentration, the system cleaning efficiency reaches 96.8%, which is 18.5% higher than the traditional method, and the energy consumption is reduced by 22.3%. The developed fault diagnosis model has a prediction accuracy of 94.2%, and the average response time is 0.3 seconds, achieving a significant improvement in system performance.