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

Human factors of future rail intelligent infrastructure

The introduction of highly reliable sensors and remote condition monitoring equipment will change the form and functionality of maintenance and engineering systems within many infrastructure sectors. Process, transport and infrastructure companies are increasingly looking to intelligent infrastructure to increase reliability and decrease costs in the future, but such systems will present many new (and some old) human factor challenges. As the first substantial piece of human factors work examining future railway intelligent infrastructure, this thesis has an overall goal to establish a human factors knowledge base regarding intelligent infrastructure systems, as used in tomorrow’s railway but also in many other sectors and industries. An in-depth interview study with senior railway specialists involved with intelligent infrastructure allowed the development and verification of a framework which explains the functions, activities and data processing stages involved. The framework includes a consideration of future roles and activities involved with intelligent infrastructure, their sequence and the most relevant human factor issues associated with them, especially the provision of the right information in the right quantity and form to the right people. In a substantial fieldwork study, a combination of qualitative and quantitative methods was employed to facilitate an understanding of alarm handling and fault finding in railway electrical control and maintenance control domains. These functions had been previously determined to be of immediate relevance to work systems in the future intelligent infrastructure. Participants in these studies were real railway operators as it was important to capture users’ cognition in their work settings. Methods used included direct observation, debriefs and retrospective protocols and knowledge elicitation. Analyses of alarm handling and fault finding within real-life work settings facilitated a comprehensive understanding of the use of artefacts, alarm and fault initiated activities, along with sources of difficulty and coping strategies in these complex work settings. The main source of difficulty was found to be information deficiency (excessive or insufficient information). Each role requires different levels and amounts of information, a key to good design of future intelligent infrastructure. The findings from the field studies led to hypotheses about the impact of presenting various levels of information on the performance of operators for different stages of alarm handling. A laboratory study subsequently confirmed these hypotheses. The research findings have led to the development of guidance for developers and the rail industry to create a more effective railway intelligent infrastructure system and have also enhanced human factors understanding of alarm handling activities in electrical control

Human factors of future rail intelligent infrastructure

The introduction of highly reliable sensors and remote condition monitoring equipment will change the form and functionality of maintenance and engineering systems within many infrastructure sectors. Process, transport and infrastructure companies are increasingly looking to intelligent infrastructure to increase reliability and decrease costs in the future, but such systems will present many new (and some old) human factor challenges. As the first substantial piece of human factors work examining future railway intelligent infrastructure, this thesis has an overall goal to establish a human factors knowledge base regarding intelligent infrastructure systems, as used in tomorrow’s railway but also in many other sectors and industries. An in-depth interview study with senior railway specialists involved with intelligent infrastructure allowed the development and verification of a framework which explains the functions, activities and data processing stages involved. The framework includes a consideration of future roles and activities involved with intelligent infrastructure, their sequence and the most relevant human factor issues associated with them, especially the provision of the right information in the right quantity and form to the right people. In a substantial fieldwork study, a combination of qualitative and quantitative methods was employed to facilitate an understanding of alarm handling and fault finding in railway electrical control and maintenance control domains. These functions had been previously determined to be of immediate relevance to work systems in the future intelligent infrastructure. Participants in these studies were real railway operators as it was important to capture users’ cognition in their work settings. Methods used included direct observation, debriefs and retrospective protocols and knowledge elicitation. Analyses of alarm handling and fault finding within real-life work settings facilitated a comprehensive understanding of the use of artefacts, alarm and fault initiated activities, along with sources of difficulty and coping strategies in these complex work settings. The main source of difficulty was found to be information deficiency (excessive or insufficient information). Each role requires different levels and amounts of information, a key to good design of future intelligent infrastructure. The findings from the field studies led to hypotheses about the impact of presenting various levels of information on the performance of operators for different stages of alarm handling. A laboratory study subsequently confirmed these hypotheses. The research findings have led to the development of guidance for developers and the rail industry to create a more effective railway intelligent infrastructure system and have also enhanced human factors understanding of alarm handling activities in electrical control