Predictive Maintenance – 100 % Resource Utilization and 0% Downtime

Industrial Automation is concerned with improving safety and productivity of workers enabling them to participate more effectively in supervisory roles. With technology advancing by leaps and bounds, there has been a major thrust on implementing a method of proactive maintenance based on perceived future needs, not a specific time period or mileage.

Predictive maintenance (PdM) has lately emerged as an advanced strategy to maximize the workers productivity and the efficiency and uptime of machines. It is being widely used in industrial automation especially for IoT to determine the state of machinery and factory tools to predict their maintenance when required thus, saving on re-work and raw material costs.

“Moving to the world where machines ask for their own maintenance before they fail!”

Industrial automation systems with high computing power can capture, process and analyze humongous amounts of BI data generated by sensors in the factory floor. This can provide real-time telemetry on detailed aspects of production processes. Machine models trained on such data allow firms to predict when different machines will fail.

The idea of predictive maintenance revolves around installing sensors on processing machines that will measure vibrations, torques, temperatures, and other things like oil quality and level. The market size of predictive maintenance is estimated to grow from USD 582.3 Million in 2015 to USD 1,884.3 Million by 2020, at a Compound Annual Growth Rate (CAGR) of 26.5%.

eInfochips provides lifecycle management services for global companies in their legacy and future products and solutions which comprises of obsolescence, sustenance and globalization/ localization L2 to L4 support.