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The aim of predictive maintenance (PdM) is first to predict when equipment failure might occur, and secondly, to prevent the occurrence of the failure by performing maintenance. Monitoring for future failure allows maintenance to be planned before the failure occurs. Ideally, predictive maintenance allows the maintenance frequency to be as low as possible to prevent unplanned reactive maintenance, without incurring costs associated with doing too much preventive maintenance.
Predictive maintenance uses condition-monitoring equipment to evaluate an asset's performance in real-time. A key element in this process is the Internet of Things (IoT). IoT allows for different assets and systems to connect, work together, and share, analyze and action data.
For predictive maintenance to be effective, it requires both hardware to monitor the equipment and software to generate the corrective work order when a potential problem is detected. Specific types of predictive maintenance include:
Additionally, tools such as CMMS, condition monitoring, connected tools and sensors, and data integration can help companies act on the analytics collected by these devices and sensors.
Studies have shown that organizations spend approximately 80% of their time reacting to issues rather than proactively preventing them. Predictive maintenance puts predictive maintenance ahead of the game. It helps predict failures and actively monitor performance. As a result, it saves time and money. Organizations that commit to a predictive maintenance program can expect to see significant improvements in asset reliability and a boost in cost efficiency, such as:
The best predictive maintenance programs take time to develop, implement and perfect. The timeline to achieve gains such as these varies, but some clients see positive returns in as little as a year.