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Predictive maintenance: Utilising data collection to transform profits and insights in industry

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Predictive maintenance: Utilising data collection to transform profits and insights in industry

What is predictive maintenance?

Manufacturing units often have a great amount of data collected from their equipment and machinery. Like any other trending technologies, the promise of predictive maintenance is tantalising. With the help of machine learning technologies and robust data collection units, many industries could make use of their historical and failure data to make profits and insights. Predictive maintenance is a proactive maintenance strategy that can predict when machinery could fail so the maintenance work can be scheduled just before it occurs. These predictions are formed based on the data collected through the condition monitoring equipment.

Here’s a look into the other popular types of maintenance.

Preventive maintenance:

This is aimed at identifying failures. It is mostly done by carrying out regular inspections and replaced the damaged parts immediately. In the longer run, this can prevent unexpected failures.

Condition-Based Maintenance:

This is considered to be a more advanced alternative to preventive maintenance. In this approach, the maintenance engineers identify and monitor the critical parameters that could potentially affect the function of the equipment. The changes in parameters are carefully observed rather than scheduled maintenance tasks.

Corrective Maintenance:

This type of maintenance is initiated when the problem or unusual behavior is discovered when working on another. Since, corrective maintenance is found ‘just in time’, it reduces repairs and emergencies in the future. Ultimately, the aim of predictive maintenance is to:

  • Minimize the number of unexpected breakdowns and maximizing asset uptime which improves asset reliability.
  • Reduce operational costs by optimizing the time you spend on maintenance work.
  • Reduce long-term maintenance costs and maximizing production hours.

Importance of Data Collection:

To build a failure model, we require historical data that allows us to capture enough information about the events leading to failure. In addition to that, general “static” features of the system can also provide valuable information, such as mechanical properties, usage, and other operating conditions. At the end of the day, the algorithm is as good as the quality of the data collected.

 

By combining the predictive formulas and a little bit of help from the c2 platform, we are now in a position to create an accurate tool for collecting and analyzing asset data. The C2 platforms provide doubles up as equipment monitoring devices to store and collect data for the predictive algorithms to capitalize. It fills up the missing links by accelerating the development of sensor and storage solutions.