Petasense
Overview
HQ Location
United States
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Year Founded
2014
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Company Type
Private
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Revenue
< $10m
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Employees
11 - 50
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Website
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Twitter Handle
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Company Description
Petasense is an Industrial Internet of Things startup based in Silicon Valley. They make learning wireless sensors that connect to the cloud to democratize Predictive Maintenance for industrial customers. The vision of the company is to connect, collect and predict for the industrial world to improve operational efficiency and reduce costs.
IoT Snapshot
Petasense is a provider of Industrial IoT infrastructure as a service (iaas), platform as a service (paas), application infrastructure and middleware, analytics and modeling, networks and connectivity, sensors, and functional applications technologies, and also active in the buildings, food and beverage, healthcare and hospitals, life sciences, oil and gas, renewable energy, and utilities industries.
Technologies
Use Cases
Functional Areas
Industries
Services
Technology Stack
Petasense’s Technology Stack maps Petasense’s participation in the infrastructure as a service (iaas), platform as a service (paas), application infrastructure and middleware, analytics and modeling, networks and connectivity, sensors, and functional applications IoT Technology stack.
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Devices Layer
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Edge Layer
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Cloud Layer
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Application Layer
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Supporting Technologies
Technological Capability:
None
Minor
Moderate
Strong
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Case Studies.
Case Study
Continuous condition monitoring pays off at a large power utility
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.
Case Study
Wireless Predictive Maintenance to Fix a Dated Walk-Around Program
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
Case Study
Large-scale Implementation of Wireless Predictive Maintenance
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.”
Case Study
Leveraging IoT for Condition Monitoring in a Large Power Utility
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.
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