What if you could anticipate where the next failure in your network of assets is likely to occur? Or optimise your maintenance schedule for minimum downtime? You would save in cost, effort, and energy and gain customer satisfaction and performance. Machine learning has the potential to provide such valuable guidance, based on past and present data from your in-service assets. But this data is bound to be imperfect, and to have gaps. It is sparse and noisy – the type of data on which conventional machine learning approaches do not perform well.
Alchemite™ deep learning software is designed for sparse, noisy data. Use it to extract vital insights from your existing data and dramatically increase the efficiency of your maintenance programs or services.
White paper - predictive maintenance and process optimisation in manufacturing
This Intellegens white paper discusses the application of deep learning in manufacturing, including how predictive maintenance is being readily adopted by forward-thinking manufacturers who understand that predicting equipment and process malfunctions can save considerable time and costs.
In this video clip from the Intellegens webinar ‘Impossible journeys in 15-dimensional space’, Dr Tom Whitehead discusses the application of the Alchemite™ software to predictive maintenance problems.
Alchemite™ for predictive maintenance
- Auto-generate and refine models to understand the performance and likely failure of assets
- Quantify the uncertainty of predictions to enable rational risk assessment
- Understand where more data is needed on your network of assets
- Model asset behaviour to prioritise maintenance work for maximum efficiency and minimum downtime