Providing Proactiveness: Data Analysis Techniques Portfolios

Data analysis is of paramount importance in the management of proactive maintenance. This chapter provides a deep analysis of the different techniques that can be adopted when dealing with the automation of maintenance processes. In particular, it focuses on three aspects of the maintenance that we have considered as the cornerstones of PM: Root cause analysis: aims to identify the causes of failures that have occurred in the past and avoid their appearance in the future. Basically, it provides a set of approaches intended at building a knowledgebase that relies on past experience to identify the core causes behind a problem in the system under investigation. This kind of analysis is useful when performing post-mortem analysis of the failures that have been reported and learn from them; Identification of the remaining useful life: aims to estimate the operational life of a component to support different activities including; The proper design of a system based on the operational constraints and the expectations of the users to guarantee its usability; The definition of a proper plan of maintenance activities to avoid unexpected down times. Alerting and predicting of failures: aims to identify possible failures before they actually happen and/or provide an alert about a set of still acceptable conditions that are unusual and that may result in a failure of the system if they are not managed properly in a timely fashion.