Received May 8, 2020, accepted May 27, 2020, date of publication June 1, 2020, date of current version June 12, 2020. Digital Object Identifier 10.1109/ACCESS.2020.2999054 Petri Net Model Based on Neural Network for Deadlock Control and Fault Detection and Treatment in Automated Manufacturing Systems HUSAM KAID 1 , ABDULRAHMAN AL-AHMARI 1 , (Member, IEEE), EMAD ABOUEL NASR 1,3 , ADEL AL-SHAYEA 1 , ALI K. KAMRANI 2 , MOHAMMED A. NOMAN 1 , AND HAITHAM A. MAHMOUD 1,3 1 Industrial Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia 2 Industrial Engineering Department, College of Engineering, University of Houston, Houston, TX 77204, USA 3 Mechanical Engineering Department, Faculty of Engineering, Helwan University, Cairo 11732, Egypt Corresponding authors: Husam Kaid (yemenhussam@yahoo.com) and Emad Abouel Nasr (eabdelghany@ksu.edu.sa) This Project was funded by the National Plan for Science, Technology and Innovation (MAARIFAH), King Abdulaziz City for Science and Technology, Kingdom of Saudi Arabia, Award Number (14-ELE69-02-R). 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. INDEX TERMS Automated manufacturing system, colored Petri net, deadlocks, fault detection, fault treatment, neural network. I. 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 concur- rent operations by sharing resources such as robots, automatic guided vehicles, machines, buffers, and automated tools. In an AMS, each component can be processed according to a given process sequence using a set of system resources. The associate editor coordinating the review of this manuscript and approving it for publication was Shouguang Wang . However, this sharing of resources can lead to deadlocks; hence, a few operations may remain incomplete. Therefore, deadlock control is essential for AMSs. In addition, resource faults may occur in a real-world system; this can cause new deadlocks in controlled AMSs. In general, a fault is defined as an interruption of an item’s ability to perform a particular function [1], and it is synonymous with errors, mistakes, disturbances, or failures leading to unwanted or unbearable equipment behavior [2]. Resource faults cannot be neglected in a real production system. However, a majority of previ- ous studies have only considered the process definition and VOLUME 8, 2020 This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ 103219