25 Jun The Powerful Advantages of Predictive Maintenance
Improving production throughput is a key objective for manufacturing entities that essentially impacts the bottom line. Breakdown of key equipment in the manufacturing line can be a bottleneck in achieving optimal production efficiency. The traditional solution to circumvent production downtime is to perform manual and scheduled maintenance—known as preventive maintenance.
While preventive maintenance can be effective at times, sudden failure of mechanical equipment that occur in between scheduled maintenance intervals can lead to expensive repairs, decreased revenue, and potential market-share loss due to unfulfilled orders. With the emergence of the fourth industrial revolution, predictive maintenance is replacing preventive maintenance as the most efficient approach to managing manufacturing equipment.
Predictive Maintenance – the Essentials
Predictive maintenance requires monitoring the performance and condition of equipment during normal operation to identify patterns and predict probable issues that can lead to equipment failure. To evaluate equipment condition, predictive maintenance uses non-destructive techniques such as infrared, vibration, 3D movement, sound level measurements, acoustic, airborne ultrasonic, oil analysis, and other sensor measurements. An industrial IoT platform tool is utilized to model, test and apply the predictive maintenance solution to the collected dataset. The tool includes algorithms and data analytics to detect patterns in asset data.
Predictive Maintenance – the Benefits
- Reduced maintenance time and cost – Maintenance schedules based on actual equipment conditions reduces maintenance times by 20-50%. Maintenance costs can also be reduced by as much as 10%. These savings can have positive impacts to the organization’s bottom-line results.
- Improve throughput and efficiency – Advanced analytics and prediction minimizes the need for redundant asset management while reducing downtime, thereby improving overall equipment effectiveness (OEE).
- Competitive advantage – Predictive maintenance can establish improved company branding to customers while boosting customer satisfaction due to greater sense of predictability and confidence.
AI-Enabled Predictive Maintenance
Advanced AI algorithms can learn normal machine behavior from its data and use this as a baseline to predict anomaly and generate real-time alerts while identifying deviations. These algorithms require historical data or a set of training and desired output data. There are two primary approaches – supervised and unsupervised algorithms. Supervised algorithms require historical input and output datasets to develop the model. In the case of unsupervised algorithms only input data is required to model the distribution of the data and automatically provide insight into correlations.
Since predictive maintenance may be required to forecast new problems without previous output datasets, MicroAI has developed MicroAI™ – an unsupervised learning engine that evolves by fine tuning itself in order to deliver an optimized maintenance schedule for a machine.
MicroAI™ enables machine operators and device managers to gather specific data about their assets in real-time without dependency on the cloud. In addition, MicroAI™’s unique ability to train the AI model locally on an Edge device, as opposed to within a cloud environment, enables the delivery of real-time critical alerts to those who need it most, making asset maintenance more efficient and more productive.