Deep Learning-Based Residual Useful Lifetime Prediction for Assets with Uncertain Failure Modes
This work addresses challenges in industrial prognostics for systems with overlapping degradation signals and unlabeled data, offering a practical solution for maintenance planning.
The research tackled the problem of predicting residual useful life for industrial assets with multiple failure modes by introducing two prognostic models that integrate mixture distributions with deep learning, achieving superior performance validated through numerical studies.
Industrial prognostics focuses on utilizing degradation signals to forecast and continually update the residual useful life of complex engineering systems. However, existing prognostic models for systems with multiple failure modes face several challenges in real-world applications, including overlapping degradation signals from multiple components, the presence of unlabeled historical data, and the similarity of signals across different failure modes. To tackle these issues, this research introduces two prognostic models that integrate the mixture (log)-location-scale distribution with deep learning. This integration facilitates the modeling of overlapping degradation signals, eliminates the need for explicit failure mode identification, and utilizes deep learning to capture complex nonlinear relationships between degradation signals and residual useful lifetimes. Numerical studies validate the superior performance of these proposed models compared to existing methods.