Model-Based and Interactive Planning for Predictive Plant Maintenance Management

by Yan Jin, Stanford Univ, Stanford, United States,
Raymond E. Levitt, Stanford Univ, Stanford, United States,

Document Type: Proceeding Paper

Part of: Computing in Civil and Building Engineering


Maintenance of process in power plants represents about 30% of the operating cost of these plants, including the cost of repair labor, materials and out side services but not the cost of plant downtime. Though industry has recognized for some time that predictive maintenance can reduce unplanned maintenance and thereby result in higher plant availability and improved quality of output without increasing direct maintenance costs, little research has been done due to the complexity of the problem. In this paper, we represent a model-based and interactive approach to predictive maintenance planning, and demonstrate some of the simulation results of applying this approach to powerplant maintenance management.

Subject Headings: Maintenance | Materials processing | Power plants | Computer models | Human factors | Labor | Computer software | Knowledge-based systems

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