Machine Learning for Cyber-Physical Systems

Motor Current-Based Degradation Modeling for Tool Wear Hybrid Prognostics in Turning Process

“…However, there could be other nonlinear dependencies that impact the degradation process and are not captured by GBM alone. In this regard, by incorporating a deep machine learning method [36], we increased the capabilities of GBM. The time series transformer-based framework [50], which was built upon the transformer encoder architecture introduced for multivariate time series representation learning together with degradation modeling, created a robust hybrid prognostics platform capable of accurately predicting the growth of wear (Figure 2).…”

“…An alternative approach is the Transformer model [35], which has gained popularity for its effective utilization of self-attention in handling sequential representations. This model has been successfully employed as a time series transformer-based approach with several enhancements proposed in the literature [36][37][38].…”

Model driven root cause analysis and reliability enhancement for large distributed computing systems

“…However, due to highly limited resources of sensors (tens of KB of memory), complex models cannot be used for diagnostics. Another approach of root cause analysis system for CPS is presented in [20]. The authors propose a system that performs hierarchical fault reasoning.…”