Petasense

C&W Services was using a manual condition monitoring program at one of its leading life sciences’ client up until last year. At best, data was collected manually every 30 days, even on the most critical machines, using a handheld data logger. After the data collection, all of the data analysis had to be outsourced to a third party for analysis. This approach has several limitations:1. Unplanned Downtime2. Shortage of Manpower3. Safety and Access to Machines4. Inconsistent Readings Collected by Manual Processes

In 2016, Arizona Public Service (APS) decided to enter the California ISO (CAISO) market, which allows them to sell power into the California market. One of their key assets was Sundance, a 420 MW unmanned peaker plant located 50 miles outside Phoenix. The entry into the CA energy market meant that starts tripled and run hours doubled almost immediately at the plant. They started looking for wireless Predictive Maintenance (PdM) system because the running hours were typically when no one was on site, which meant that traditional forms of PdM were not possible. Typically, a specialist would collect vibration and other condition data on equipment, but it had to be taken during operation, and it was difficult to get personnel out to the site.“Reliability was foremost on our minds,” commented Don Lamontagne, Supervisor of Equipment Reliability Engineering. “We faced huge loss of potential revenue, as well as fines if we weren’t able to generate power when it’s needed.”

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.

A large power utility in Hawaii was looking for more frequent condition monitoring on their Balance of Plant (BOP) generation assets. They had experienced significant equipment failures that occurred between their scheduled quarterly walkaround condition monitoring routes.

C&W Services was using a manual condition monitoring program at one of its leading life sciences’ client up until last year. At best, data was collected manually every 30 days, even on the most critical machines, using a handheld data logger. After the data collection, all of the data analysis had to be outsourced to a third party for analysis. This approach has several limitations:1. Unplanned Downtime2. Shortage of Manpower3. Safety and Access to Machines4. Inconsistent Readings Collected by Manual Processes

In 2016, Arizona Public Service (APS) decided to enter the California ISO (CAISO) market, which allows them to sell power into the California market. One of their key assets was Sundance, a 420 MW unmanned peaker plant located 50 miles outside Phoenix. The entry into the CA energy market meant that starts tripled and run hours doubled almost immediately at the plant. They started looking for wireless Predictive Maintenance (PdM) system because the running hours were typically when no one was on site, which meant that traditional forms of PdM were not possible. Typically, a specialist would collect vibration and other condition data on equipment, but it had to be taken during operation, and it was difficult to get personnel out to the site.“Reliability was foremost on our minds,” commented Don Lamontagne, Supervisor of Equipment Reliability Engineering. “We faced huge loss of potential revenue, as well as fines if we weren’t able to generate power when it’s needed.”

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.