Degradation Assessment and Maintenance Decision-making for Mechanical and Electrical Assets Driven by Condition Data

Abstract

Condition-based maintenance (CBM) is a proactive maintenance strategy that utilizes condition data to inform maintenance decisions, aiming to prevent critical asset failures, reduce maintenance costs, and enhance system safety. Despite significant advancements in prognostic techniques and the widespread adoption of sensors for continuous data collection, integrating these heterogeneous data into effective maintenance decision-making remains challenging. In terms of methodology, developing degradation models that accurately reflect system health is both critical and complex. This complexity arises from the diverse data types and intricate failure mechanisms associated with various assets, such as electrical distribution systems (EDS), wind turbines incorporating load and supervisory control and data acquisition (SCADA) data, and rotating machinery with multiple failure modes. From the application aspect, practical implementation faces obstacles including stakeholders’ specific asset requirements, cost-effective monitoring decisions, and the necessity for targeted maintenance policies rather than generic strategies. For example, stakeholders may have differing benefits and priorities, as seen between maintenance contractors and wind farm owners. Making cost-effective monitoring decisions involves determining which assets require sensor deployment. Furthermore, the implementation of targeted maintenance policies is crucial, such as the adoption of contractor-oriented maintenance strategies for wind farms and opportunistic maintenance (OM) strategies for large-scale electrical distribution systems. Addressing these challenges is essential for optimizing maintenance practices in mechanical and electrical assets. To address these gaps, the overarching objective of this thesis is structured around four key topics, focusing on integrating precise degradation modeling with practical maintenance decision-making frameworks for targeted mechanical and electrical assets. In the first topic, an opportunistic CBM is proposed for EDS to address the complexity of large-scale asset management. Maintenance actions are triggered based on the health status observed during inspections. In the second topic, a contractor-oriented maintenance strategy is developed for both onshore and offshore wind farms, aiming to maximize the maintenance contractor profits. This strategy utilizes prognostic information from both monitorable and non-monitorable components, constructing a degradation-related efficiency model that quantifies wind turbine efficiency losses due to component degradation and integrates this into the maintenance decision-making process. Moreover, the third topic introduces an asset-criticality-guided maintenance strategy, which incorporates machine criticality and sensor deployment into the decision-making framework. This approach provides practical insights for identifying asset-specific criteria and aims to maximize the expected revenue of the mechanical systems. Finally, the fourth topic explores the feasibility of implementing additional load monitoring for wind turbine degradation assessment. A cost-effective load sensor system is designed to collect the load data, and a novel degradation assessment method is proposed to incorporate the load data with a nonlinear dynamic state-space neural network model to extract the degradation information of a wind turbine more efficiently. This thesis advances the field of CBM by offering innovative, data-driven, and actionable strategies tailored to the specific needs of different stakeholders in mechanical and electrical asset management. The developed methods will contribute to significantly reducing operation and maintenance expenses while enhancing net revenue for mechanical and electrical assets across diverse engineering applications