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Petasense

United States
2014
Private
< $10m
11 - 50
Open website
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.
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Petasense is a provider of Industrial IoT infrastructure as a service (iaas), platform as a service (paas), application infrastructure and middleware, analytics and modeling, functional applications, networks and connectivity, and sensors technologies, and also active in the buildings, food and beverage, healthcare and hospitals, life sciences, oil and gas, renewable energy, and utilities industries.
Technologies
Sensors
Pressure Sensors
Temperature Sensors
Vibration Sensors
Voltage Sensors
Networks & Connectivity
Software-Defined Networking
WiFi
Infrastructure as a Service (IaaS)
Cloud Databases
Functional Applications
Enterprise Asset Management Systems (EAM)
Application Infrastructure & Middleware
Data Visualization
Analytics & Modeling
Predictive Analytics
Platform as a Service (PaaS)
Data Management Platforms
Use Cases
Predictive Maintenance
Process Control & Optimization
Functions
Business Operation
Maintenance
Industries
Buildings
Food & Beverage
Healthcare & Hospitals
Life Sciences
Oil & Gas
Renewable Energy
Utilities
Services
Hardware Design & Engineering Services
Software Design & Engineering Services
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, functional applications, networks and connectivity, and sensors IoT technology stack.
  • Application Layer
  • Functional Applications
  • Cloud Layer
  • Platform as a Service
    Infrastructure as a Service
  • Edge Layer
  • Automation & Control
    Processors & Edge Intelligence
    Actuators
    Sensors
  • Devices Layer
  • Robots
    Drones
    Wearables
  • Supporting Technologies
  • Analytics & Modeling
    Application Infrastructure & Middleware
    Cybersecurity & Privacy
    Networks & Connectivity
Technological Capability
None
Minor
Moderate
Strong
Number of Case Studies4
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.
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
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.”
Number of Similar Suppliers5
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