Condition-based maintenance (CBM) offers the possibility of replacing the predominant maintenance-as-scheduled paradigm with a maintenance-on-demand paradigm. In all CBM algorithms, faults must first be recognized, then characterized and finally reconciled. Multiple CBM methods have been proposed, including model-free, model-based and metamodel-based methods. However, the signals from real systems are obscured by sources of error. This research examines the impact of error upon a metamodel-based CBM approach using a simulated system to reveal the significance of error in the all-important step of fault recognition. The use of a simulated system allows control of the type and magnitude of both the error and of the fault signals allowing their significance to be evaluated. As a result of this research, a stronger theoretical foundation metamodel-based CBM techniques is established and several promising behaviors are identified.
The Verification Analysis of the Software Dynamic Evolution Topology Structure Model Based on Demand and Runtime Variability Parallel Driver under the Background of Large Data
