Tag Archives: Senseye

Advent of Machine Learning Into Predictive Maintenance

Over the years, numerous analytical and empirical methods have been developed to prevent machine failure, reduce costs, and increase production capacity. In the present age, companies like M2M Data Corporation and Senseye started out by developing physical models of machines based on data collected from their customers’ facilities. The data consists of parameters such as pressure, speed of motors, particle concentration in lubricants, acoustic data, temperature in the friction generating part of the equipment, and machine unique data sheets. For example, each pump has its unique “pump curves” that correlate pump rotation speed with discharge pressure and flow. A deviation of these above parameters is used to flag the plant personnel of possible machine issues, and could also suggest a specific malfunction. Continue reading

Case Study: Senseye Improves Maintenance Outcomes at Auto Manufacturing Plant

Overview

Senseye (client registration required) deployed its equipment prognostics and maintenance management software platform at a major auto manufacturing plant in the U.K. in January 2016. After completing a pilot period, the customer has moved into a first paid commercial phase. Senseye is ramping up to analyze machine health for 12,000 machines across three production lines, including over a thousand robots. The customer contracted major industrial suppliers to instrument machines with sensors and to pull the data into an industrial cloud. Senseye then handles the analytics portion using its IoT-based solution. Senseye claims that the customer previously reviewed analytics offerings from major analytics players, including Microsoft and SAP, but that none offered the level of automation, return-on-investment (ROI), or prognostics that it required. Continue reading