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Predictive Maintenance aspires to transform manufacturing by utilizing critical manufacturing process data to make proactive rather than reactive decisions about critical processes.
FREMONT, CA: In industries such as manufacturing, where depreciation is a high cost, and innovative equipment is expensive, establishing solid asset management becomes crucial to the sustainability and durability of the manufacturing unit. Implementing a predictive maintenance model in these configurations enables significant cost savings on numerous fronts. While there are lean management and six sigma procedures whose main objective is to increase a unit's efficiency, their relevance in the present landscape is being questioned.
In an age when technology has come to dominate practically every concrete element of our lives, it has become critical to implement efficient procedures powered by cutting-edge technology. Predictive maintenance, in essence, strives to upgrade asset management methodologies through IoT-enabled technology.
Advantages of Predictive Maintenance
Reduces the likelihood of unanticipated downtime: Unplanned downtime is a significant factor in a manufacturing plant's demise. According to a Wall Street Journal article, unexpected downtime costs industrial businesses $50 billion a year. As a result, unscheduled downtime should be kept to a minimum. Predictive maintenance is a great technique to implement proactive maintenance plans to achieve this goal. Predictive maintenance uses data from the past to identify machines that are likely to go down shortly.
Maximizes the Lifetime of Equipment: To maximize equipment lifespan, predictive maintenance efficiently eliminates the practices of planned maintenance and condition-based maintenance by constantly monitoring the output, efficiency, and quality of equipment. The model assigns different schedules to the unit's various operational components based on their life expectancy and frequency of use.
Connected Vehicles: The automotive industry's predictive maintenance application in connected cars is one of the most attractive use cases available. These connected vehicles generate and transmit a large amount of performance data from their various sensors. As a result, predictive maintenance enables the dealership and manufacturer to establish service and maintenance programs for their clients, sparing them the annoyance of unforeseen problems and boosting their user experience.
Providers of Public Utility: Utility suppliers increasingly utilize predictive maintenance and analysis to identify early warning signals of grid supply and demand issues and then repair them before they escalate into significant outages.
Research: Research behemoths like CERN rely on the massive amounts of data generated by their millions of sensors to guarantee that all of their equipment and apparatus operate at peak performance. As a result, they have the advantage of anticipating and resolving any faults.