Operational predictive maintenance is a strategy that employs data analysis tools and procedures to discover operational irregularities as well as potential problems in equipment and processes. Predictive maintenance systems are intended to help a manufacturing facility identify the overall state of out-of-service equipment in order to determine when maintenance should be performed. Because chores are completed only when necessary, this strategy claims significant cost savings over time or even regular or time-consuming preventative maintenance.
Operational predictive maintenance is usually associated with monitoring and controlling the conditions of machines, as well as other equipment that may be essential for work. This method can be used to assess the load capacity of equipment, and to establish or identify the maximum load that can be supported by the equipment. Operational predictive maintenance methods are used to determine and forecast the conditions of machines as they are placed under different operating conditions. Predictive maintenance helps reduce cost, unexpected breakdowns, and offers the manufacturer the opportunity to plan maintenance schedule.
Operational predictive maintenance programs are used in industrial maintenance environments, where the purpose of asset management is the protection of assets. This allows the company to focus on the more productive assets and to eliminate the unprofitable or expensive assets, as well as reducing the loss of potential revenue and preventing costly repairs. These methods are also used to protect the assets and replaced them with less expensive new ones. Operational predictive maintenance is a type of maintenance that directly tracks an asset's health, status, and performance in real time. Thus, there is an increasing demand worldwide.
For example, in October 2021, Armaments Research Company planned to develop a predictive maintenance platform for crew-served weapons for the U.S. Marine Corps. This project is part of ARC’s recently awarded 5-year Small Business Innovative Research Phase III contract with the U.S. Department of Defense and General Services Administration.
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