Recognizing Significant Human-Related Factors Affecting System Reliability Corey Kiassat 1 and Myrto Konstandinidou 2 1 Centre for Maintenance Optimization and Reliability Engineering, Department of Mechanical & Industrial Engineering, University of Toronto, 5 King‟s College Road, Toronto, ON, M5S 3G8, Canada. 2 Systems Reliability and Industrial Safety Laboratory, Institute of Nuclear Technology and Radiation Protection, NCSR Demokritos, Patr.Grigoriou and Neapoleos Street, 15310 Athens, Greece. Email: 1 ckiassat@mie.utoronto.ca and 2 myrto@ipta.demokritos.gr System reliability can be affected by many factors, some of which are not machine-related. An important category of these non-traditional factors are human-related (HR) factors, such as expertise, collaboration, and motivation. Operational systems consist of machinery as well as human operators and decision makers. Therefore, a risk analysis is incomplete if the probability of human error is not considered. The paper discusses a method for recognizing human-related factors affecting the system reliability and evaluating their impact. Factors are analyzed to determine their significance. The insignificant ones are then eliminated. Various Human Reliability Analysis (HRA) techniques have been developed in order to identify factors that influence human and system reliability. Among the many HRA techniques developed for various applications, this paper uses Cognitive Reliability and Error Analysis Method (CREAM) due to its cognition approach for identifying the factors affecting human reliability. Once identified, factors are utilized in the Proportional Hazards Model (PHM), a well-known failure prediction and reliability analysis model. The inclusion of HR factors will expand the traditional focus of PHM, thus making it an all-encompassing approach. Keywords: Human Reliability Analysis (HRA), Proportional Hazard Model (PHM), Maintenance, Reliability Optimization. 1. INTRODUCTION A Reliability expert who disregards the role of the human in the overall failure risk of a system exaggerates the role of machine-related failure modes in the overall unreliability of the system. This is quite common and, consequently, identical machines used across various sites may exhibit varying reliabilities. The skill level, motivation, work place politics, and many other intangible factors play a role that should be taken into account when devising reliability estimates and maintenance strategies. There is quite a strong desire to provide a coherent strategy to maximize human performance and to minimize human error in every working context. Accident analysis forecasting techniques and Human Reliability Analysis (HRA) methodologies are doing so in complex systems with hazardous operations (Cacciabue, 2000), such as nuclear power stations (Swain and Guttman, 1983) and petrochemical processes (OGP, 2002) as well as in railway transports (Cacciabue, 2005) and aviation maintenance (Chang and Wang, 2009) but can be both onerous and time demanding. The main goal of this paper is to discuss the integration of significant human-related (HR) factors into maintenance optimization models. One strategy under maintenance optimization is condition-based maintenance. In this strategy, the objective is to gain the maximum useful life of the machine and all its components. On the one hand, the components should not be replaced too soon because that would not be economical. On the other hand, the machine should not be stopped too late because unplanned downtime may be quite costly. These two conditions are contradictory and one needs to balance the two against each other in order to predict the perfect replacement time. One approach to estimate the failure time of any given equipment is the Proportional Hazards Model (PHM). The PHM is a method that relates the time of an event, such as failure or breakdown, to a number of explanatory variables known as covariates (Vlok et al, 2002). From a maintenance point of view, the idea behind the PHM is that obvious and/or hypothetical factors, including the equipment age, may act as reliability criteria that influence the hazard rate of the equipment. The hazard rate is the rate of transition out of the non-failed state to a failed