A belief rule-based system for online and centralized collaborative performance assessment of networked physical systems subject to nonideal channels

Networked physical systems (NPSs) are widely applied in modern engineering practices characterized by intensive domain knowledge and imperfect observational data. Meanwhile, collaborative performance assessment provides strong support for them to operate safely and stably over a long period of time. For a specific NPS and its corresponding online and centralized collaborative performance assessment system, the existence of interference and noise in the real-world channel that is rather nonideal inevitably obstructs the smooth progress of the assessment. To this end, in this paper, a symbolic systematic solution is proposed resorting to an improved version of the belief rule base with continuous inputs (BRB-CI). First, the extrapolation module is enhanced by integrating a matched filtering-based link. Second, the existing robustness analysis for systems based on the fundamental belief rule base is extended to systems based on the BRB-CI. Third, the optimization module is ameliorated by designing a multimetric-balanced pattern of the grey wolf optimizer with interpretability reinforcement. Ultimately, by choosing an instance of NPSs in the field of aerospace with continuous time dynamics, pertinent empirical studies are carried out to substantiate the good engineering practicability of our proposal. Note that this paper is the first piece inquiring into belief rule-based systems such a class of expert systems for online and centralized cooperative performance assessment of NPSs with continuous time dynamics such an application, with considerable attention paid to the nonideality of real-world channels.