February 1, 2011

New Tools for Diagnosing and Troubleshooting Power Plant Equipment Faults

By Aaron Hussey and Stephen Hesler, Electric Power Research Institute, and Randall Bickford, Expert Microsystems Inc.

The Electric Power Research Institute has developed a pair of diagnostic tools that combine and integrate features from multiple sources of plant information. The Diagnostic Advisor and the Asset Fault Signature Database will improve diagnostics for and troubleshooting of equipment faults by providing a holistic view of the condition of plant equipment.

In competitive environments, electric power plants must operate under reduced operation and maintenance (O&M) budgets while maintaining high reliability and availability. Early detection of equipment faults and subsequent planning of maintenance actions can help cut costs while maximizing availability. However, the detection of equipment faults and subsequent troubleshooting often require information beyond what traditional process instrumentation provides. Improving the use of power plant information sources for equipment fault prediction and diagnosis can help electric utilities meet plant availability goals and reduce O&M costs.

In a new plant design, it is desirable to install sensors based not only on process control design but also on equipment fault detection needs, as identified through a structured failure modes effects analysis (FMEA) and/or fault tree analysis. At existing plants, however, information obtained manually through predictive maintenance or operator rounds can be used alongside process data for detecting and diagnosing equipment faults.

The Electric Power Research Institute (EPRI) has designed a new diagnostic analysis software application and database that assists electric power generation plant staff in the early identification of equipment faults. That early detection enables rapid incident response and prevents failures of critical power generation equipment. This article describes EPRI’s work in designing a pair of tools that combine features from multiple sources of plant information to assist with troubleshooting and diagnostics of plant equipment.