White Paper
Predictive maintenance is being readily adopted by forward thinking manufacturers who understand that predicting equipment and process malfunctions can save considerable time and costs. Alchemite™, trained on noisy, historical manufacturing data can help guide engineers with suggested process calibrations. The unique Alchemite™ system handles the sparse and noisy data typically found in these datasets, delivering models and tools to maximise production performance, reduce failures, and cut costs.
Executive summary
In manufacturing, ongoing maintenance of equipment and production line machinery represents a major cost, where unplanned downtime costs an estimated $50 billion a year (according to a 2013 Solomon RAM study) and asset failure is the cause of 42% of this downtime. Predictive maintenance is being readily adopted by forward thinking manufacturers who understand that predicting equipment and process malfunctions can save considerable time and costs. Adopting such methods to existing manufacturing processes is not straightforward. In order to make accurate predictions, there has to be existing data in place to guide the model – however that is rarely the case. Intellegens has developed a machine learning tool, Alchemite™, that trains models on all available data, no matter how sparse or noisy. We bring all the available data together and use underlying correlations to accurately predict missing values and generate the most complete models possible. Applying this novel method to the available historical and simulated data enables organisations to identify opportunities for reducing costs and downtime, time savings, and overall performance improvements, through predictive maintenance and process optimization.