Fault-Recovery and Repair Modeling of Discrete Event Systems Using Petri Nets

Abstract

Despite advances in automated manufacturing systems (AMSs), faults occur from time to time, which cannot be avoided in a complex real system. A fault is one of the primary causes of failures making some AMS operations unable to complete, and the diagnosis is one of the most important steps in fault-recovery and repair. This work develops a methodology for investigating the behavior of faults on the resources in discrete event systems that are failure-prone. We tackle the fault-tolerant problem and propose a method to make the system able to continue performing its duties, while the failed resources are under a repair and recovery process. In this work, a failure-safe model is proposed and, at the same time, a method for fault recovery and repair of a faulty element is presented without interrupting task processing due to the occurrence of fault to some elements. We use redundant elements to replace the target elements, and these redundant elements are used to do the same work as the target elements do, when faults occur to the target elements. A target element is an unreliable element that is prone to failure. After a faulty target element is repaired and recovered, its failure model is automatically replaced by its repaired model to indicate that the corresponding element has returned to work. The proposed method is tested using an application example. The results show, compared with those obtained by the studies in the literature, that the proposed method has a great performance and outperforms the existing studies.

Modeling of Fault Recovery and Repair for Automated Manufacturing Cells with Load-Sharing Redundant Elements Using Petri Nets

“…Redundancy is a crucial concept in engineering, which involves duplicating essential components or functions of a system to increase its reliability, usually in the form of a fail-safe or backup mechanism, or to improve its performance. In [36], redundant hardware was utilized to recover from a system failure. A model-based approach was used in [37] to develop a supervisory control system that switched the faulty controller to a backup controller when a fault occurred.…”

“…In this article, we approach the issue of fault tolerance and propose a way to enable a system to persist in fulfilling its duties while taking actions to repair and recover the fault. Our previous work in [36] dealt with some of the critical problems in designing a faulttolerant controller to maintain the safety of the system. While the idea presented in [36] required the addition of redundant elements for each target element when a fault occurred in the target element, the submodel of its overflow element was utilized to substitute the model's faulty target element.…”

“…Our previous work in [36] dealt with some of the critical problems in designing a faulttolerant controller to maintain the safety of the system. While the idea presented in [36] required the addition of redundant elements for each target element when a fault occurred in the target element, the submodel of its overflow element was utilized to substitute the model's faulty target element. In this work, redundant elements become part of the components of the system and have tasks to perform, regardless of whether an unreliable element is a failure or not.…”

Deadlock Control and Fault Detection and Treatment in Reconfigurable Manufacturing Systems Using Colored Resource-Oriented Petri Nets Based on Neural Network

“…Many policies have been developed in the literature based on Petri nets, focusing on three strategies: prevention of deadlocks, avoidance of deadlocks, and detection and recovery of deadlocks [4,5]. Most of these policies assume that the resources in automated manufacturing systems are reliable [6][7][8][9][10][11][12][13], and others assume that they are unreliable [14][15][16][17][18][19][20][21][22][23][24][25]. Two analysis techniques in PNs are used to design deadlock control policies: reachability graph analysis [26][27][28] and structural analysis [3,6].…”

“…Two analysis techniques in PNs are used to design deadlock control policies: reachability graph analysis [26][27][28] and structural analysis [3,6]. In addition, three criteria are needed to design and evaluate the supervisor of AMS, which include structural complexity that leads to design a supervisor with a number of monitors [3,7,29], computational complexity that means a supervisor can be implemented on small and large sized systems [3,30], and behavioral permissiveness that leads to improve the time performance (utilization, throughput, and throughput time) of the system [4, 25,31].…”