Equipment Reliability through Advanced Analytics

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In today’s competitive global environment, businesses need to be agile, flexible, resilient,
and possess dynamic capabilities. The advent of advanced digital technologies makes it possible for firms to completely innovate the concept of quality management. A living ecosystem equipped with advanced digital technologies (e.g., smart sensors, machine learning, big data analytics, and artificial intelligence) can be developed to manage quality.

Predictive maintenance management requires sharing information on production and inventory levels among networked partner firms, as well as the changing consumer demands. This system of collaboration is expected to aid in satisfying customer expectations through accurate demand prediction, improved service levels, and reliability. Expansion of smart devices with self-diagnosing and predictive failure capabilities will help reduce failures and operating costs, optimize inventories, improve the access to maintenance, reduce the need to maintain spare inventory for safety purposes, and enhance the replacement timing. Industry 4.0 needs to respond aggressively with several solutions that encompass safety, quality, value, and cost to meet end-user needs for proactive predictive maintenance strategies.

Therefore, to minimize possible losses and ensure flexibility by avoiding sudden downtimes, predictive maintenance is an essential strategic operating method for businesses that are building smart plants for the future. In addition, efforts to diagnose failure of facilities, equipment, and/or systems at an early stage are benefiting from technological advancements in both manufacturing and service industries. There are several real-world examples of software development that support such
Possibilities. In Tata Steel we have taken few initiatives in this regard stating by developing simple models using Linear Regressions to using advanced methodologies like SVM and K-means cluster algorithms.