Production Tool Component RUL Prediction With DCI Pre-Alarm

The patent transforms the raw sensor data into multiple feature parameters through signal processing techniques (FFT, wavelet transform, statistical features). It then selects optimal features using correlation analysis and information gain, and finally inputs these processed parameters into the prediction model. This multi-stage parameter transformation and selection process significantly improves prediction accuracy while maintaining reasonable computational complexity.

The patent replaces the simple exponential model with a more sophisticated prediction framework that uses machine learning algorithms (random forest, support vector regression, neural networks). This substitution of the prediction mechanism with advanced computational methods enables the system to capture complex non-linear degradation patterns that the exponential model cannot represent, thereby improving accuracy.

The system performs preliminary analysis of component degradation trends using RUL prediction before actual failures occur. By forecasting the remaining useful life with high accuracy, the system enables maintenance to be scheduled just in time when the component approaches failure, rather than following fixed predetermined intervals. This preliminary prediction action allows optimization of maintenance timing to minimize production disruption.

The patent transitions from static scheduled maintenance with fixed intervals to dynamic condition-based maintenance. The maintenance schedule is continuously adjusted based on real-time monitoring of component health indicators and updated RUL predictions. This dynamic approach allows the system to extend maintenance intervals when components are healthy and accelerate maintenance when degradation is detected, optimizing production capacity while ensuring reliability.

The patent segments the prediction process into distinct stages: data acquisition, preprocessing, feature extraction, feature selection, and prediction. Each stage processes data independently and can be optimized separately. The feature extraction stage uses efficient algorithms (FFT, wavelet packets) that can process sensor data in real-time, while the prediction stage uses pre-trained models that provide fast inference. This segmentation enables real-time prediction capability while managing computational load effectively.

The system performs preliminary training of prediction models using historical data offline, storing the trained model parameters for later use. During real-time operation, only inference is performed using the pre-trained models, which significantly reduces computational load compared to training models in real-time. The feature extraction and selection are also pre-computed based on offline analysis, enabling fast real-time prediction with minimal computational resources.