Predictive models for maintenance services

Predictive maintenance based on big data and geospatial data seeks to define the best time to perform work on an asset, so that maintenance frequency is as low as possible and reliability is as high as possible without unnecessary costs.

Tuesday, March 8, 2022

Maintenance teams are beginning to embrace the use of big data and predictive modeling to improve performance, which helps establish maintenance programs that reduce downtime and save maintenance costs, while extending the life of their equipment, reducing unnecessary tasks and optimizing spare parts inventory.

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What is predictive maintenance?

Predictive maintenance is a technique that uses data analysis tools and techniques to detect anomalies in your operation and possible defects in equipment and processes, so that you can fix them before they cause a failure, this allows the frequency of maintenance to be as low as possible, without incurring the costs associated with doing too much preventive maintenance.

Using geospatial data is key to implementing a successful predictive maintenance program, as is the use of sensors and predictive maintenance techniques.

These tools capture information through sensors, industrial controls and enterprise systems, accurately identifying areas that need attention. Examples of the use of predictive maintenance sensors include vibration analysis, oil analysis, thermal imaging and equipment observation. Read the full article here

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