Petasense has entered into a strategic go-to-market partnership with OSIsoft LLC to help drive adoption of IIoT technology in process and manufacturing plants. This partnership will enable industrial customers to retrofit their machinery with wireless sensors and perform predictive maintenance using both asset and process control data, while enabling plants to eliminate unplanned downtime, improve plant safety and reduce repair and maintenance costs.
“The first step in IIoT for many industrial companies and utilities is capturing data from their legacy equipment. Many of these systems—while they work fine—are years, if not decades, old and weren’t created with digital in mind,” said Pat Kennedy, CEO of OSIsoft. “Our partnership with Petasense will help lay the foundation for digital transformation.”
By analyzing vibration characteristics, Petasense software is able to predict common defects like pump-cavitation, bearing-wear and misalignment. These ML algorithms calculate a numerical health score of the machine in real-time, enabling plant managers to make informed maintenance decisions. Even a small plant can generate over 10 million vibration readings daily; Petasense has logged 25 billion wireless vibration readings across its client base.
Silicon Valley Power (SVP), the municipal electric utility owned by the City of Santa Clara has signed on to integrate OSIsoft and Petasense solutions. Today SVP monitors all its balance-of-plant (BOP) rotating machines with Petasense.
“After a successful trial, we have now rolled out the Petasense solution to cover all our mission critical rotating machinery,” said Paul Manchester, assistant plant manager at SVP. “We are now integrating Petasense and PI to capture additional value and realize greater operational efficiencies.”
“With IIoT it is possible to retrofit machinery with sensors and collect maintenance data at a size and scale that was unimaginable before,” said Arun Santhebennur, cofounder of Petasense. “Any site that uses PI will be able to easily deploy and implement predictive maintenance at an extremely affordable price point.”
