- Networks & Connectivity - WiFi
- Sensors - Vibration Sensors
- Electrical Grids
- Asset Health Management (AHM)
- Predictive Maintenance
A large power utility in Hawaii was grappling with the challenge of frequent equipment failures that occurred between their scheduled quarterly walkaround condition monitoring routes. The company, which operates several combined cycle natural gas fired power plants, was seeking a more efficient and reliable solution for condition monitoring on their Balance of Plant (BOP) generation assets. The assets included a critical Boiler Feed Pump (BFP), motors, several condensate pumps, heat drip pumps, auxiliary cooling water pumps, and half-a-dozen forced draft fans. The main issue was that significant equipment failures were happening in the intervals between their scheduled condition monitoring, leading to unplanned downtime and operational inefficiencies.
About The Customer
The customer in this case study is a large power utility based in Hawaii. The company operates several combined cycle natural gas fired power plants and has a range of Balance of Plant (BOP) generation assets. These assets include a critical Boiler Feed Pump (BFP), motors, several condensate pumps, heat drip pumps, auxiliary cooling water pumps, and half-a-dozen forced draft fans. The company was facing significant challenges with equipment failures occurring between their scheduled quarterly walkaround condition monitoring routes, leading to unplanned downtime and operational inefficiencies.
The power utility decided to trial Petasense technology on 8-10 of their assets. They installed twenty triaxial battery powered vibration sensors (Motes) on these machines, along with WiFi connectivity. The installation was completed in a day, and they began tracking vibration using the web-based ARO Cloud software. The technicians and reliability engineers also downloaded an iOS app to access the asset data anywhere, anytime. This allowed them to monitor the health of their assets continuously and detect any abnormalities in real-time. When they detected abnormal peaks and gradually increasing vibration levels, they were able to quickly identify a defect and take corrective action. They also modified the monitoring frequency to once per hour to ensure that any changes in vibration would be detected promptly.
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