- Analytics & Modeling - Machine Learning
- Predictive Maintenance
- Data Science Services
Most organisations apply a “Reactive Maintenance” approach to their processes, in which repairs and replacements are made to the equipment after a failure occurs. It costs around 10x more to repair a machine after it fails, not to mention the direct impact on revenue and customer satisfaction. Through “Preventative Maintenance” equipment is repaired or replaced at pre-set time intervals in order to avoid failure. Whilst this approach reduces unplanned downtime it is expensive as these scheduled repairs take place when there can be nothing wrong with the equipment. However, the benefits of predictive maintenance are significant, so it is becoming the preferred method for manufacturers, enabling organisations to foresee and schedule repairs and replacements when needed, achieving 100% operational uptime of the equipment. One challenge for traditional machine learning in manufacturing is that techniques require clean and complete data. However, manufacturing and process data can be sparse and noisy.
Currently, it is difficult for engineers to access and interpret production process data, they rely on personal experiences and opinions to modify process parameters. This leads to inconsistent and potentially suboptimal decision making, and moreover increases the risk of process failure, increasing associated time and costs. The production line is especially difficult to model using standard techniques due to the inherent time lag and inertia between changing operating parameters and their effect. Costs associated with waste materials and failed production could also be significantly reduced with the application of relevant and innovative deep learning technology to design production processes more efficiently.
Intellegens has developed a machine learning tool, Alchemite, that trains models on all available data, no matter how sparse or noisy. It brings all the available data together and use underlying correlations to accurately predict missing values and generate the most complete models possible.
The technology leverages existing process data to reduce the costs of asset maintenance programs and improves the overall understanding of the complete system, optimising production lines and processes involved in manufacturing.
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