Lessons Learned in Fleetwide Asset Monitoring of Gas Turbines and Supporting Equipment in Power Generation Applications
Condition monitoring remains an important technology for equipment life cycle management. Historically, online condition monitoring systems are installed only on the most critical assets within a power plant, process plant, or manufacturing facility. Less critical equipment, while vital to operation of the plant, are only monitored or tested periodically using manual route based technologies. This historical practice leaves equipment specialists with a small amount of time for analysis of collected sensory data (vibration, temperature, oil, power, etc.) as they spend the vast majority of working hours collecting equipment sensory data. Fortunately, data acquisition technology has evolved, making it possible to transform standard and advanced machinery measurements from manual collections to online collections, increasing time for specialists to analyze, and yielding opportunities for automated diagnostics and prognostics. By taking advantage of automation, the ability of equipment owners and operators to lower life cycle costs and increase reliability of plant equipment is greatly improved.
The transition from manual route based measurements to a fleetwide surveillance program touches many elements from sensors to networked data acquisition nodes to servers to historians and predictive technologies. Within power generation plants, installation costs, information technology strategies, and long term vision come together to create higher machine reliability at lower operational cost and new automation in performance monitoring, diagnostics, and advisory generation. With automation, comes increased sensory data from pumps and turbines that require new tools for data management, data mining, and data transformation into actionable information. A case study reviews the open and extensible data architecture of a fleetwide monitoring system deployed, the ongoing efforts, and current benefits delivered to the power generation industry participants.
How to Cite
signal processing, vibration monitoring, Data Acquisition, Predictive Health Monitoring, pump, Data Driven
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