5Dynamic Bayesian Networks: Integrating Reliability Computation in the Control System

5.1. Introduction

As discussed in the previous chapters, graphical probabilistic models are applied in risk management, maintenance and diagnosis. Nevertheless, these probabilistic models can also be used in control theory applications. The graphical probabilistic model is implemented online in the closed loop before interruption of system functioning for normal or maintenance actions. The purpose of this chapter is to integrate the probabilistic models into control theory to optimize the control strategy according to failures and their impacts on system reliability.

The control strategy has an impact on the system and its performance during the operation. For instance, modifying a control law according to the faults and failure can warrant the system functioning. Nevertheless, overcharging an actuator to compensate for decreased performance can accelerate the degradation of system components. From a long-term perspective, performance cannot be infinitely compensated for. The greater the compensation, the greater the necessary components’ overcharge and the faster the degradation rate.

The models necessary to estimate reliability under operational conditions have to be used online during the operational phase. To do this, it is necessary to use formalism well suited to the estimation and prognosis of the functioning modes induced by events occurring throughout the lifetime of the system. Moreover, ...

Get Benefits of Bayesian Network Models now with O’Reilly online learning.

O’Reilly members experience live online training, plus books, videos, and digital content from 200+ publishers.