Case Study: Largest Power Generation Company in the United States Takes a Digital IIoT Approach for Predictive Maintenance

In 2010, Duke Energy (Duke) began a companywide program using digital technology to address reliability demands and optimize workforce activities [1]. Duke replaced route-based data collection with DAQ technology to identify problems and notify SME specialists. SMEs now remotely spend time on higher value tasks. This multi-year project included new sensors and a new information technology architecture. Duke incorporated the Industrial Internet of Things (IIoT) and big data analytics to meet its reliability goals. Building on maintenance and engineering knowledge of plant assets, Duke worked with the Electrical Power Research Institute (EPRI), sensor vendors, DAQ vendors, data historians, and anomaly detection software. Duke’s vision is for fleetwide Smart Monitoring and Diagnostics (Smart M&D).

EPRI introduced a similar framework to support power generation companies transitioning to IoT-related technologies and solutions. Duke and EPRI work together making investments in online sensors, networking infrastructure, system architecture, and diagnostics applications.

Duke identified over 10,000 assets across their facilities and added more than 30,000 sensors to its equipment including accelerometers, temperature sensors, oil analysis sensors, thermal cameras, and proximity probes. These sensors add online inspections, such as vibration, bearing temperature and oil pressure, transformer dissolved gases, and generator electromagnetic signatures.

Vibration inspections can require captures up to 100,000 samples per second for several seconds to obtain good measures of machine condition. DAQs manage the acquisition, validate data quality, time-stamp data, and extract features. To manage volumes of data, Duke uses edge computers. Every plant has its own data historian that collects, stores, and organizes feature data. SMEs utilize alarm dashboards indicating unexpected equipment behavior and investigate and screen issues to determine whether alarm anomaly needs further investigation. Anomaly data transfers to the decision support system for diagnosis by comparison against known faults in a fault signature database. The diagnosis provides a window to plan and schedule mitigating action at a time when costs are low.  Over four years, Duke avoided costs greater than 130% of its capital budget by avoiding unforeseen failures. Further, automated data collection reduces labor costs.

Shifting to a digital predictive maintenance strategy improves reliability and lowers operating costs, meeting corporate reliability objectives while optimizing workforce activities with automation and analytics.

People and process must also be considered. The historic practice of using handheld analyzers for machinery inspections is a SME obstacle. Continuous improvement of remote visualization helps SMEs make the change to Smart M&D. Managing alerts is a challenge when 10,000 assets report anomalies. Incorporating criticality, severity, and risk eases the alarm management challenge.

Duke Energy utilizes change agent leaders in maintenance and reliability areas to advocate for change. Leaders form partnerships with information technology teams and ensure the digital predictive maintenance system is reliable. Executive sponsorship facilitates change management, providing ongoing communications, fleetwide training, and continued funding.