The benefits of predictive maintenance, such as helping determine the condition of equipment and predicting when maintenance is due, are extremely strategic.
Together with the implementation of solutions based on Machine Learning, you can help even more and generate significant cost savings, greater predictability and greater system availability.
Data is collected over time to monitor equipment health. The goal is to find patterns that can help predict and ultimately prevent failures.
Why use ML in predictive maintenance?
Because ML allows you to:
Create predictive models to maximize asset life, operational efficiency, or uptime.
Take advantage of past and ongoing data.
Optimize periodic maintenance operations.
Avoid or minimize downtime. This will help avoid dissatisfied customers, save money, and perhaps save lives.
In industrial AI, the process known as “training” allows ML algorithms to detect anomalies and test correlations while looking for patterns in various data sources. Although regular maintenance is better than failure, we often end up doing maintenance before it is needed. Therefore, it is not an optimal solution from a cost perspective.
Predictive maintenance avoids maximizing the use of your resources and will detect anomalies and failure patterns and give early warnings.