The importance of human factor and maintenance activities in risk assessment for railway applications

This thesis focuses on the importance of human factor and maintenance activities in risk assessment for railway applications. Risk based maintenance is a key factor of RAMS (Reliability, Availability, Maintainability and Safety) for railway. One of the widest used techniques to evaluate the optimal maintenance policy of complex systems is the RCM (Reliability Centred Maintenance). This procedure starts from a failure analysis before individuating the optimal maintenance operation focusing on a decision diagram which is very vague and subjective. Trying to solve this problem, the first part of this work introduces an innovative approach that proposes a new decision-making diagram. The new diagram is based on a fuzzy-FMECA (Failure Modes, Effects and Criticality Analysis) assessment combined with some Boolean variables in order to provide a unique maintenance task for every identified scenario depending on the O (Occurrence), S (Severity) and D (Detection) assessment. The proposed procedure provides a diagnostic-oriented decision diagram able to solve the problems of the standardized RCM procedure and, at the same time, to optimize the Operation&Maintenance cost and the system availability favoring CBM (Condition-Based Maintenance) tasks such as Condition Monitoring and Failure Finding procedures. The proposed enhanced RCM is based on a FMECA, which is a central technique used to perform risk assessment in every industrial and technological field. Despite this, several papers in literature agree that classical FMECA suffer many drawbacks. The developed fuzzy FMECA technique aims to solve all these problems with a simple and effective tool that could be applied in railway applications. Moreover, an innovative risk threshold estimation method has been developed to divide critical and negligible modes after the FMECA assessment in order to prioritize countermeasures. The second topic covered by this research is the analysis of human reliability in railway engineering. Human factors remarkably contribute to railway accidents and, as a matter of fact, it is one of the main causes of accident on the last years. This is the reason why it is mandatory to study and evaluate human reliability in maintenance operation of railway systems. Literature is plenty of techniques developed to study the human reliability, however the only validated method for railway field is RARA (Railway Action Reliability Assessment). RARA has been developed in 2012 and is characterized by a highly subjective and complex assessment. Trying to solve these needs, this work proposes an improvement of RARA method able to solve its main shortcomings thanks to fuzzy logic. Using the proposed fuzzy-RARA the analyst is facilitated in the assessment of the numerical parameters and the subjectivity is remarkably mitigated. Finally, the last part of the work presents an innovative technique specifically developed for railway. This method integrates the Weibull distribution and aims to provide a time-dependent model for the Human Error Probability. Furthermore, the proposed method gave the possibility to select one or more variable breaks within the work shift, which is an aspect generally neglected by the state-of-the art. Both the proposed methods for Human Reliability Analysis have been tested on the maintenance activities performed by qualified operators nearby the railroad. The results highlight the significant contributions of the human error within the contexts of the complete risk assessment of the railway system.