Abstract
Nowadays, maintenance activities and safety management can be supported by a mature state of the art favouring the implementation of condition-based maintenance programme, which recommends maintenance decisions based on the information collected through asset life. The main idea, which grounds in the Industry 4.0 paradigm, is to utilize the asset degradation information, extracted and identified through different techniques, to reduce and eliminate costly, unscheduled downtimes and unexpected breakdowns and to avoid risky scenarios. This paper aims at developing and testing a data-driven CBM tool to provide fault diagnostics transforming raw data from the shop-floor into information, finally enabling risk-informed decision-making. The tool relies on a process of knowledge discovery that incorporates both prior knowledge and proper interpretation of data analytics results. Prior knowledge is extracted through a process hazard analysis (PHA), while data analysis deals with statistical process control and novelty detection. The model is proposed to integrate some Cyber-Physical System element in the extant plant automation, to exploit its computational capabilities through the continuous monitoring and data analytics. This enables a "watchdog agent" of risky scenario, allowing an on-line risk-assessment of safety-critical components, finally enhancing the intelligence in the industrial process.
