The huge amount of data that can be collected today, the data storage and processing capabilities provided by Big Data platforms, and the possibilities offered by advanced analytics techniques, are contributing to the fact that the future of asset maintenance will be more preventive than corrective. That is why predictive maintenance is here to stay.
What is predictive maintenance?
Predictive maintenance is the application of advanced analytical techniques to predict the future failure of a machine component, so that the component can be replaced just before it fails. This way, equipment downtime is minimized, component lifetime is maximized, and components are purchased when needed, eliminating stocks that become obsolete. Its objective: to help companies and public organizations to ensure the reliability of assets by increasing their availability, uptime and improving their safety.
It is a step beyond Descriptive Maintenance, which only shows the current state of components and machines.
How it works and one step further
Data is the engine of predictive maintenance, so in the first place you need data. Today, technologies such as IoT and M2M devices make it possible to place sensors on equipment that send an alert when parts fall outside certain pre-established ranges. For example, when detecting temperature increases above 100 degrees.
This data must be collected and structured using technology capable of collecting, analyzing and processing large amounts of data. Due to the amount of measurements that sensors make, they are usually Big Data technologies.
Once a sufficient amount of data has been collected, analytics comes into play. This type of maintenance uses predictive analytics techniques to look for patterns, trends or models in past data that can predict the probability of future events. Depending on the type of business objective, one type of technique or another will be applied. In the field of predictive maintenance, four major groups of models are identified: classification, regression, segmentation and survival analysis. The output data of these models are predictions.
Once we have the predictions, it is necessary to know how to efficiently use the resources we have in a way that minimizes the risk of unexpected repairs, maximizes the life of the machines and minimizes the impact of the machine overhaul and repair. At this point, the application of Prescriptive Analytics techniques helps to decide when to perform each intervention using the available tools and personnel without compromising the production.
Benefits of implementing predictive maintenance
As we have already mentioned, the predictive capacity applied to equipment for the analysis of information on the condition and operation of assets, allows to anticipate possible failures that help to reduce corrective interventions and their associated costs. The advantages of implementing Predictive Maintenance are:
- Reduction of failures and breakdowns
- Reduction in the number of interventions
- Increase in asset availability
- Reduction of downtime
- Optimization of maintenance personnel management
- Possibility to follow the evolution of a failure over time
- Accurate knowledge of the time limit for action
- Reduction of accidents and increase of safety
- Verification of repairs and overall reliability
Advantages that incur significant cost savings and increase profit margins. From maintenance and labor costs to spare parts, accident and industrial insurance costs.
Any more questions about how advanced analytics can help you improve your asset maintenance operations?