Abstract
This paper addresses the case of automatic controlled system which deteriorates during its operation because of components’ wear or deterioration. Depending on its specific closed-loop structure, the controlled system has the ability to compensate for disturbances affecting the actuators which can remain partially hidden. The deterioration modeling and the Remaining Useful Lifetime (RUL) estimation for such closed-loop dynamic system have not been addressed extensively. In this paper, we consider a controlled system with Proportional-Integral-Derivative controller. It is assumed that the actuator is subject to shocks that occur randomly in time. An integrated model is proposed to jointly describe the state of the controlled process and the actuator deterioration. Only the output of the controlled system is available to assess its health condition. By considering a Piecewise Deterministic Markov Process, the RUL of the system can be estimated by a two-step approach. In the first step referred as the “Diagnosis” step, the system state is estimated online from the available monitoring observations by using a particle filtering method. In the second step referred as the “Prognosis” step, the RUL is estimated as a conditional reliability by Monte Carlo simulation. To illustrate the approach, a simulated tank level control system is used.
Highlights
Due to increasing requirements on durability, reliability, and dependability of industrial systems, intensive research activity on maintenance modeling has been developed during the last decades
Based on the available information about the current system state provided by health monitoring process, different condition-based or predictive maintenance decision rules can be proposed so as to optimize the decisionmaking process, that is, to prevent or correct failures or faults [1, 2]
A predictive maintenance policy that schedules maintenance actions according to a prognosis activity without specific additional sensors seems to be an appropriate approach [8, 9]
Summary
Due to increasing requirements on durability, reliability, and dependability of industrial systems, intensive research activity on maintenance modeling has been developed during the last decades. In condition-based maintenance framework, a deterioration indicator that correctly describes the dynamic of the failure process is required This efficient indicator can be constructed from collected information on various deterioration-related monitoring parameters, such as vibration, temperature, lubricating oil, and noise levels. The need of continuous monitoring in cases of dynamic operating condition may increase the systems costs when expensive monitoring devices are required [6, 7]. In this way, a predictive maintenance policy that schedules maintenance actions according to a prognosis activity without specific additional sensors seems to be an appropriate approach [8, 9]
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
