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Predictive Maintenance

Predictive maintenance (PdM) is a maintenance strategy that aims to increase cost savings and decrease equipment downtime by predicting when a failure is likely to occur and then performing reactive maintenance to prevent the failure. Traditional maintenance would often rely on maintenance being performed at a certain time. This type of schedule maintenance is useful in preventing equipment failures, but has a high cost associated with it, as it requires maintenance team manpower and costly supplies to be used at every scheduled time. PdM, on the other hand, relies on real time data to identify potential problems before they occur so a work order can be placed and maintenance can be performed on an as-needed basis.

Predictive maintenance programs can be run in a variety of ways. Acoustical analysis can be done at the sonic or ultrasonic level. Acoustical analysis listens to the sounds a machine makes as it runs and then compares the current sound to the optimal sound to make a prediction around failures. An oil analysis can be done to either analyze the condition of the lubricant itself, or to check the wear particles to look at the machine components that use the oil. Vibration analysis can be utilized on any piece of equipment that runs at high speeds. Similar to acoustical analysis, vibration analysis looks at the movements the machine makes, and then compares it to the optimal movement in order to assess its status. There are still other ways of analyzing data for predictive maintenance, but every type of analysis can benefit from the use of machine learning.

Machine learning maintenance management helps optimize predictive models by letting machines do most of the analyses. Machine learning often utilizes Internet of Things (IoT) devices and sensors to gather data from equipment and feed it into the machine learning algorithm. These algorithms then compare the data to what the machine has already learned about the equipment in order to predict any failures. The more data that can be processed, the more accurate the machine learning algorithm is likely to be. Machine learning is often a cost effective solution, as after it is programmed it can be continually run as an operational expense that can be budgeted for, and it reduces the costs associate with routine maintenance. Additionally, as more data is ingested, businesses can gain better insight into the lifecycle of their machines and appropriately plan for repairs and replacements.

Predictive maintenance can be useful in a variety of fields, such as:

  • Oil and Gas: Oil and gas industries can benefit from machine learning and IoT devices to gain insight into equipment that is otherwise difficult to access, such as underwater or offshore locations. This allows repairs to be done as needed, so that companies can only send out expensive management teams when necessary.

  • Manufacturing: The manufacturing industry can benefit from predictive maintenance through the use of early fault detection. By recognizing when a fault is about to occur, businesses can ensure that they are able to react appropriately and prevent their equipment from going offline. This prevents costly downtime and ensures that the manufacturing process runs smoothly from start to finish.
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