Simultaneous signal separation and prognostics of multi-component systems: the case of identical components

When monitoring complex engineering systems, sensors often measure mixtures of signals that are unique to individual components (component signals). However, isolating component signals directly from sensor signals can be a challenge. As an example, in vibration monitoring of a rotating machine, if different components generate vibration signals at similar frequencies, they cannot be distinguished using traditional spectrum analysis (non-inseparable). However, developing degradation signals from component signals is important to monitor the deterioration of crucial components and to predict their residual lifetimes. This article proposes a simultaneous signal separation and prognostics framework for multi-component systems with non-inseparable component signals. In the signal separation stage, an Independent Component Analysis (ICA) algorithm is used to isolate component signals from mixed sensor signals, and an online amplitude recovery procedure is used to recover amplitude information that is lost after applying the ICA. In the prognostics stage, an adaptive prognostics method to model component degradation signals as continuous stochastic processes is used to predict the residual lifetimes of individual components. A case study is presented that investigates the performance of the signal separation stage and that of the final residual-life prediction under different conditions. The simulation results show a reasonable robustness of the methodology.