Petasense > Case Studies > Large-scale Implementation of Wireless Predictive Maintenance

Large-scale Implementation of Wireless Predictive Maintenance

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 Large-scale Implementation of Wireless Predictive Maintenance - IoT ONE Case Study
Technology Category
  • Analytics & Modeling - Predictive Analytics
  • Functional Applications - Enterprise Asset Management Systems (EAM)
  • Sensors - Pressure Sensors
  • Sensors - Temperature Sensors
  • Sensors - Voltage Sensors
Applicable Industries
  • Utilities
Applicable Functions
  • Maintenance
Use Cases
  • Predictive Maintenance
The Customer
Arizona Public Service
About The Customer
Arizona Public Service is the largest electric utility in Arizona, with 6,300 MW of generating capacity and serving 1M customers in over 11 counties. 
The Challenge

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.”

The Solution

APS selected Petasense, a leading IIoT startup, because it met these four criteria and more. They liked that the machine learning algorithms automatically analyzed data coming from the sensors, saving time that was previously spent inspecting healthy machines. Given the remote nature of Sundance, the ability to access the reliability data from anywhere and allowing remote personnel to interact with the raw data and document maintenance tasks, was very important. APS liked both the Vibration Motes and Transmitters, which could bring data from multiple sensor types (pressure, temperature, current), providing for expansion to different types of assets.

The central APS team wanted to work collaboratively with the plant to help shift away from a reactive maintenance culture. They selected more than 100 rotating assets to monitor, including critical pumps, motors, and fans. In most cases, they were able to install the Motes in approximately 30 minutes, without shutting down the equipment.

Operational Impact
  • [Process Optimization - Predictive Maintenance]

    Data can be collected autonomously, and frequently enough to enable early detection of problems.

  • [Data Management - Data Availability]

    Sensors data helps to analyze the root cause of defects.

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