Title :
Active Diagnosability of Discrete Event Systems and its Application to Battery Fault Diagnosis
Author :
Ziqiang Chen ; Feng Lin ; Caisheng Wang ; Yi Le Wang ; Min Xu
Author_Institution :
Sch. of Mech. & Power Eng., Shanghai Jiao Tong Univ., Shanghai, China
Abstract :
A battery system may consist of many batteries; each battery can have a normal operating mode and several faulty modes. This makes the fault status of a battery system very complex. To diagnose such a complex system, passive diagnosis is often insufficient. We may need to actively control the system to complete the diagnosis task. In this brief, we investigate the active diagnosis in the framework of discrete event systems. We model the system to be diagnosed by an automaton (finite state machine) with state outputs in which some events are controllable in the sense that they can be enforced, and some events are not. We say that the system is actively diagnosable if we can find a control under which the faults can be diagnosed. We derive a necessary and sufficient condition for a system to be actively diagnosable. Algorithms are devised for checking active diagnosability and finding controls that achieve it. The theoretical results are then applied to fault diagnosis of battery systems. We illustrate the approach using a simplified battery system consisting of four batteries. We find a control that diagnoses the faults based on the measurements of two temperature sensors.
Keywords :
discrete event systems; fault diagnosis; fault tolerant control; primary cells; sensors; active diagnosability; battery fault diagnosis; discrete event systems; necessary condition; passive diagnosis; sufficient condition; temperature sensors; Aging; Automata; Batteries; Battery charge measurement; Control systems; Fault diagnosis; Resistance; Battery management system; detectability; diagnosability; discrete event systems (DESs); fault diagnosis; fault diagnosis.;
Journal_Title :
Control Systems Technology, IEEE Transactions on
DOI :
10.1109/TCST.2013.2291069