Upcoming Role of Condition Monitoring in Risk-Based Asset Management for the Power Sector

The electrical power sector is stimulated to evolve under the pressures of the energy transition, the deregulation of electricity markets and the introduction of intelligent grids. In general, engineers believe that technologies such as monitoring, control and diagnostic devices, can realize this evolvement smoothly. Unfortunately, the contributions of these emerging technologies to business strategies remain difficult to quantify in straightforward metrics. Consequently, decisions to invest on these technologies are still taken in an ad-hoc manner. This is far from the risk-based approach commonly recommended for asset management (AM). The paper introduces risk-based management as a guiding principle for maintenance management. Then, the triple-level AM model (strategic, tactical and operational) as the foundation to define risk-based AM is described. Afterwards, two categories of risks, one triggered by technical stimuli and the other by non-technical stimuli are introduced. It is shown that the main challenge of managing risks with technical stimuli is to have the ability to understand the technical cause of failures, which is located at the operational level within the triplelevel AM model. One method to quantitatively understand the technical cause of failures is by means of condition diagnostic and monitoring technologies. Therefore, the aim of this paper is to clarify the potential contribution of condition diagnostic and monitoring technologies to risk-based decision making for the power sector. This paper shows that, in practice, the implementation of condition diagnostic and monitoring technologies is mainly driven by purely technical asset based considerations without evaluating the contribution to, for instance, risks. This paper provides a list of aspects in which condition diagnostic and monitoring may contribute to risk evaluation with technical stimuli. The listed aspects (which are: (1) asset specific condition data, (2) timely condition data and (3) predictive condition data) can be regarded as input for the probability of failure and as influencing input for the consequence of failure, hence benefiting quantitative risk studies and AM activities (such as condition assessment/ maintenance or replacement). Finally, these benefits can be evaluated afterwards in a risk-based AM planning stage, so that asset managers can justify investments on necessary technical improvements of condition monitoring systems.