Efficient Detection on Stochastic Faults in PLC Based Automated Assembly Systems With Novel Sensor Deployment and Diagnoser Design

In this dissertation, we proposed solutions on novel sensor deployment and diagnoser design to efficiently detect stochastic faults in PLC based automated systems First, a fuzzy quantitative graph based sensor deployment was called upon to model cause-effect relationship between faults and sensors. Analytic hierarchy process (AHP) was used to aggregate the heterogeneous properties between sensors and faults into single edge values in fuzzy graph, thus quantitatively determining the fault detectability. An appropriate multiple objective model was set up to minimize fault unobservability and cost while achieving required detectability performance. Lexicographical mixed integer linear programming and greedy search were respectively used to optimize the model, thus assigning the sensors to faults. Second, a diagnoser based on real time fuzzy Petri net (RTFPN) was proposed to detect faults in discrete manufacturing systems. It used the real time PN to model the manufacturing plant while using fuzzy PN to isolate the faults. It has the capability of handling uncertainties and including industry knowledge to diagnose faults. The proposed approach was implemented using Visual Basic, and tested as well as validated on a dual robot arm. Finally, the proposed sensor deployment approach and diagnoser were comprehensively evaluated based on design of experiment techniques. Two-stage statistical analysis including analysis of variance (ANOVA) and least significance difference (LSD) were conducted to evaluate the diagnosis performance including positive detection rate, false alarm, accuracy and detect delay. It illustrated the proposed approaches have better performance on those evaluation metrics. The major contributions of this research include the following aspects: (1) a novel fuzzy quantitative graph based sensor deployment approach handling sensor heterogeneity, and optimizing multiple objectives based on lexicographical integer linear programming and greedy algorithm, respectively. A case study on a five tank system showed that system detectability was improved from the approach of signed directed graph's 0.62 to the proposed approach's 0.70. The other case study on a dual robot arm also show improvement on system's detectability improved from the approach of signed directed graph's 0.61 to the proposed approach's 0.65. (2) A novel real time fuzzy Petri net diagnoser was used to remedy nonsynchronization and integrate useful but incomplete knowledge for diagnosis purpose. The third case study on a dual robot arm shows that the diagnoser can achieve a high detection accuracy of 93% and maximum detection delay of eight steps. (3) The comprehensive evaluation approach can be referenced by other diagnosis systems' design, optimization and evaluation

Analysis of Remote Diagnosis Architecture for a PLCBbased Automated Assembly System

To troubleshoot equipment installed in geographically distant locations, equipment manufacturers and system integrators are increasingly resorting to remote diagnosis in order to reduce the down time of the equipment, thereby achieving savings in cost and time on both the customer and manufacturer side. Remote diagnosis involves the use of communication technologies to perform fault diagnosis of a system located at a site distant to a troubleshooter. In order to achieve remote diagnosis, several frameworks have been proposed incorporating advancements such as automated fault diagnosis, collaborative diagnosis and mobile communication techniques. Standards exist for the capabilities representative of different levels of remote equipment diagnosis. Several studies have been performed to analyze the ability of human machine interface to assist troubleshooters in local fault diagnosis. However, the ability of a remote diagnosis system architecture to assist the troubleshooter in performing diagnosis and the effects of the failure types and other factors in a remote diagnosis environment on remote troubleshooting performance are not frequently addressed. In this thesis, an attempt is made to understand the factors that affect remote troubleshooting performance: remote diagnosis architecture, nature of failure, skill level of the local operator and level of expertise of the remote troubleshooter. For this purpose, three hierarchical levels of remote diagnosis architectures to diagnose failures in a PLC based automated assembly system were built based on existing standards. Common failures in automated assembly systems were identified and duplicated. Experiments were performed in which expert and novice troubleshooters used these remote diagnosis architectures to diagnose different types of failures while working with novice and engineer operators. The results suggest that in the diagnosis of failures related to measured or monitored system variables by remote expert troubleshooters, remote troubleshooting performance improved with the increase in the levels of the remote diagnosis architectures. In contrast, in the diagnosis of these failures by novice troubleshooters, no significant difference was observed among the three architectures in terms of remote troubleshooting performance and the novice troubleshooters experienced problems with managing the increased information available. Failures unrelated to monitored system parameters resulted in significantly reduced remote troubleshooting performance with all the three architectures in comparison to the failures related to monitored system parameters for both expert and novice troubleshooters. The experts exhibited better information gathering capabilities by spending more time per information source and making fewer transitions between information sources while diagnosing failures. The increase in capabilities of the architectures resulted in reduced operator interaction to a to a greater extent with experts. The difference in terms of overall remote troubleshooting performance between engineer and novice operators was not found to be significant