Managing Predictive Maintenance Programmes Supported by Industrial Internet of Things

Course Overview

Price volatility does not excuse weak discipline in IoT-supported predictive maintenance — it punishes it. Regulators and insurers increasingly want documented evidence of how this strand of energy and hydrocarbon operations is controlled. Participants develop a defensible line of reasoning for the choices they make about the wider energy and hydrocarbon operations agenda. Exercises escalate in difficulty, ending with the ambiguous situations that make IoT-supported predictive maintenance hard in practice. The level assumes working familiarity with the operating asset but no prior formal training in this strand of energy and hydrocarbon operations. Progress on this aspect of energy and hydrocarbon operations is usually lost in the gap between approval and execution. The programme is built to be used, and every section of IoT-supported predictive maintenance it covers ends in something applicable. Applied research in energy and hydrocarbon operations consistently shows that early structure around the energy and hydrocarbon operations discipline reduces downstream rework. It ends with a prioritised list of changes to this area of energy and hydrocarbon operations that the participant is prepared to defend internally.