Your machines are talking. Are you listening?
What is the minimum amount of maintenance an enterprise needs to maintain assets in optimum working condition? It’s a tough question, one that can only be answered with the support of predictive maintenance.
In Manufacturing, Predictive Maintenance is considered to be the most useful application for the Internet of Things. Detection and supervision of equipment anomalies that prevent the possibility of critical failure is an essential requirement in manufacturing today.
The global market for predictive solutions is expected to grow enormously to $21.5 billion by 2025. With enterprises looking at instant alerts about system failure and forecasting machine health, the right predictive maintenance system in any organization will maximize operational efficiency and go beyond digitization of processes.
To meet the increasing demand for Predictive Maintenance solutions, a wide range of options are available to help industrial businesses find success. The three key areas of Predictive Maintenance are:
1. Anomaly Detection
Based on anomaly detection strategy and collecting a dataset of common failure patterns, we can leverage AI to foresee problems before they occur.
2. Machine Failure
We build models that quantify the risk of failure for a machine and use this information to transform maintenance & service operations
3. Remaining Useful Life (RUL)
By taking RUL into account, we can schedule predictive maintenance and avoid unplanned downtime achieving higher operational effectiveness.
Benefits of Predictive Maintenance:
Understanding the true potential of AI initiatives and leveraging data to make insightful decisions is transforming the manufacturing industry. To determine which technique works best for a company is key to getting the most out of a predictive analytics solution. These predictive models and algorithms are being used to solve real business problems of today.
By implementing predictive maintenance, industries can:
- Monitor and analyze asset health data, both historical and real-time
- Intervene at the right time, before assets go down
- Prioritize and optimize resources
Impact of Predictive Maintenance:
When predictive maintenance is used effectively as a maintenance strategy, maintenance on machines is only done when it is needed. That is, just before the failure is likely to occur. This results in multiple cost savings:
- Minimizing equipment maintenance time
- Minimizing the production hours lost to maintenance
- Minimizing the equipment and components procurement costs
Veltris developed a real-time estimation of viable cell density in the commercial production of a pharmaceutical drug. It operates on prediction of single and multiple values of drug composition based on historical data.
According to PwC report, predictive maintenance in manufacturing could –
- Improve uptime by 9%
- Reduce costs by 12%
- Reduce safety, health, environmental & quality risks by 14%
- Extend the lifetime of aging assets by 20%
We all agree that maintenance is a tough job. You must keep an eye on product quality while ensuring system availability and minimizing resource usage for repairs. In the past, it was impossible to consider all of these variables. The advent of Industry 4.0, on the other hand, has opened up new possibilities for predictive maintenance.
Veltris bring expertise and tools to help you take full advantage of data to fuel innovation, drive new opportunities, and accelerate your smart manufacturing transformation. With extensive expertise gained from numerous customer implementation and successes, we can help unlock the true potential of data, analytics and AI.
It’s now time to rethink how we detect, anticipate, and troubleshoot machine failures.
Want to know how you can optimize asset reliability with analytics-driven maintenance? Tell us about the challenges you are facing?