by Senseye

Senseye PdM™ is the leading software product for Predictive Maintenance 4.0, trusted by Fortune 500 companies to reduce unplanned downtime and increase maintenance efficiency.

Solution Overview

Trusted by Fortune 500 industrial companies, Senseye PdM™ is the leading cloud-based product for Predictive Maintenance 4.0. It is used by maintenance teams to halve unplanned downtime and increase maintenance efficiency by using proprietary machine-learning algorithms to automatically forecast machine failure and remaining useful life. With Senseye PdM you can typically achieve ROI in less than 3 months.

Senseye improves operational efficiency by:

  • reducing downtime - increasing throughput.
  • reducing bottlenecks - decreasing operational expenses.
  • helping to maintain a steady and reliable flow - forecasting failures, making planned maintenance possible before unplanned downtime occurs.

Senseye already saves industrial companies millions of dollars a month.

Solution Approach

Senseye utilises high quality machine condition indicators and asset information stored in your PI System to enable predictive maintenance at scale. PI System integration is achieved using PI Web API; no additional hardware or software is required. Senseye performs advanced detection and diagnostics on time-series data from any machine type with zero configuration. Combined with access to equipment maintenance information, Senseye is able to match real-time data against previous failures and generate prognostic output including Remaining Useful Life (RUL). Results from Senseye can be fed back into your PI System for use in real-time dashboards and reporting.



PI System Requirements

PI System version 3.4.390 or later, PI System Access (PSA) License

Solution Type

Advanced Analytics, Condition Based Maintenance


  • Mining, Metallurgy & Material
  • Oil & Gas
  • Pulp and Paper
  • Food & Beverages

Business Impacts

Optimize Processes, Increase Asset Health & Uptime, Improve Energy Efficiencies, Manage Risk & Regulatory


Applications for the PI System