O&M impact from equipment condition monitoring software

Brad True

Equipment condition monitoring (ECM) software is helping to deliver higher return on assets (ROA) by substantially lowering O&M costs and increasing generation revenue. These systems provide early warning of deteriorating equipment conditions before they result in catastrophic failure or poor performance. Advanced ECM solutions differ from previous monitoring systems in their ability to monitor multiple operating states and across OEMs and equipment types. The return on investment for a typical ECM system in most cases is shorter than one year.

Early warning from ECM systems impact two major equipment problem categories: catastrophic failures and minor failures or gradual operational deterioration. The benefits of early warning in each category are lower costs for labor, parts, overhead and increased revenue due to increased unit availability. ECM systems also enable operators to extend maintenance intervals and the associated outages by increasing confidence in the actual health of equipment by comparing health deterioration rates to the time remaining until the next planned outage. The result is essentially shifting unplanned maintenance events to planned events. Industry research has estimated that a 600 megawatt plant using advanced ECM technology can save $1 million annually and significantly increase return-on assets.

ECM software provides early warning of equipment problems by using existing sensor data—no new sensor hardware is necessary—that resides in databases such as the plant historian or control system. ECM software generates models based on empirical data from each type or subsystem of equipment such as a boiler feed pump, fan, turbine, generator, or other piece of equipment. Additionally, practically any analog-like variable such as temperature, pressure, vibration can be used as well as calculated values like efficiency or heat rate. Typical installation time for a large plant is three to four weeks.

During operation, ECM models estimate where every variable should be and compares the estimates to the actual sensor outputs. Abnormalities or significant deviation at the sensor, equipment health and process levels are identified and presented on a web browser-based user interface for ease of access by the entire organization as required.

Brad True, SmartSignal
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Brad True is the general manager for power, process and energy industries with Chicago-based SmartSignal Corporation (btrue@smartsignal.com).

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