Petri Net Model Based on Neural Network for Deadlock Control and Fault Detection and Treatment in Automated Manufacturing Systems

Husam Kaid,Mohammed A Noman,Haitham A Mahmoud,Abdulrahman Al-Ahmari,Emad Abouel Nasr,Ali K Kamrani,Adel Al-Shayea

doi:10.1109/access.2020.2999054

Husam Kaid, Mohammed A Noman + Show 5 more

Open Access

https://doi.org/10.1109/access.2020.2999054

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Abstract

Previously, different deadlock control strategies for automated manufacturing systems (AMSs) based on Petri Nets with reliable resources have been proposed. However, in real-world applications, resources may be unreliable. Therefore, deadlock control strategies presented in previous research studies are not suitable for such applications. To address this issue, this paper proposes a novel three-step deadlock control strategy for fault detection and treatment of unreliable resource systems. In the first step, a controlled system (deadlock-free) is obtained using the “Maximum Number of Forbidding First met Bad Markings Problem 1” (MFFBMP1), which does not consider resource failures. Subsequently, all obtained monitors are merged into a single monitor based on a colored Petri net. The second step addresses deadlocks caused by resource failures in the Petri net model using a common recovery subnet based on colored Petri nets. The recovery subnet is applied to the system obtained in the first step to ensure that the system is reliable. The third step proposes a hybrid approach that combines neural networks with colored Petri nets obtained from the second step, for the detection and treatment of faults. The proposed approach possesses the advantages of modular integration of Petri nets and can also learn neurons and reduce knowledge, similar to neural networks. Therefore, this approach solves the deadlock problem in AMSs and also detects and treats failures. The proposed approach was tested using an example from literature.

Highlights

An automated manufacturing system is a typical example of discrete event systems
This paper presents a three-step robust deadlock control strategy for fault detection and treatment of unreliable resource systems in an automated manufacturing systems (AMSs)
The second step addressed the problems of deadlock control caused by resource failures

Summary

INTRODUCTION

An automated manufacturing system is a typical example of discrete event systems. It allows different product types to enter at discrete points in time with asynchronous or concurrent operations by sharing resources such as robots, automatic guided vehicles, machines, buffers, and automated tools. H. Kaid et al.: Petri Net Model Based on Neural Network for Deadlock Control and Fault Detection and Treatment optimization of the process under ideal conditions. It is essential to develop a robust deadlock prevention policy that can perform fault detection and treatment of unreliable resource systems and ensure deadlock-freedom in AMSs. Petri nets are a commonly used graphical and mathematical modeling tool suitable for scheduling, deadlock analysis, and control in AMSs [3], [4]. The proposed approach possesses the advantages of modular integration of Petri nets, and has the ability to learn neurons and reduce the knowledge, as in neural networks This approach provides a combination of three features: (i) deadlock-free system without considering resource failure, (ii) detection of faults, and (iii) treatment of faults.

BASICS OF PETRI NETS

ROBUST CONTROL FOR UNRELIABLE RESOURCES

Findings

CONCLUSIONS