The goal of this paper was to identify strategies that can be employed to improve the robustness of a health monitoring system. Strategies used included:
- Selective signal manipulation which is based on a strategy to apply unique pre-processing and post-processing manipulation techniques to improve the robustness of a health indicator (HI).
- Selective signal blocking which is taking advantage of setting enabling conditions for the algorithm so that signals and the associated noise effects are ignored when the performance of the algorithm is poor.
- Selective signal and HI amplification which is defined as when the system is reconfigured to amplify the signal factor without significantly amplifying the noise effect. An example of such strategy is by applying Time Synchronous Averaging (TSA) to attenuate high-frequency components of a signal with a suspected periodic component.
- Selective HI construction is based on the idea that different sources of signal for development of a prognostics algorithm will lead to different performances and if higher performing HIs in terms of robustness are designed and selected, then the overall performance of the algorithm will be improved.
- Selective signal shaping which is based on the strategy to modify, normalize or change the shape of the input signals to capture some of the relationships between various input signals to the algorithm and improve the robustness and reduce the noise effect.
- Reduce noise effects at the source by applying appropriate filters.
- Generate independent decisions and take an average response and mature the decision.
- Robust parameter design by optimizing the control parameters that impact the performance of the algorithm which can be tweaked and selected by the designer.
How to Cite
Noise Factor Analysis, Prognostics, Health Monitoring, Robustness Analysis
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