Summary
In the manufacturing sectors, the traditional planned maintenance approach is no longer viable, as it cannot cope with the ever-rising complexity of production systems. This pressing problem hurts industry’s profitability, and unplanned downtime costs industrial manufacturers €43 billion per year. This pressing problem has fuelled the growth of the predictive maintenance market. Currently, predictive maintenance solutions employ typical machine learning approaches based on monolithic rule-based predictions and require from the customer labelled data that correspond to defective machine states. This impedes the penetration of predictive maintenance in the industry. EnCORE is the fruit of 5 years of R&D to develop proprietary deep neural networks fit for predictive maintenance applications. Our solution uses best-in-class deep learning technology removing the overheads related with data preparation and enable the prediction of machine’s future condition using data that correspond to normal machine states. This is a game changing approach in the predictive maintenance industry. EnCORE is at TRL-6, with our software being validated at two different applications, (1) a compression moulding machine that produces plastic bottle enclosures/caps and (2) a cold forming machine that produces razor blades. Our target market will be the Food & Beverage and Consumer Goods industries targeting both OEMs of machinery and End-Users use such machinery. To take our product to the market, we will employ an hybrid business model using both direct sales and sales through industrial IoT platforms. EnCORE’s unique offering unlocks tremendous value for our customers; this will fuel the adoption of our solution by the industry. In the commercialisation period, we forecast cumulative profits of about €15 million with a strong Return on Investment (ROI) of €13 million. This will allow us to grow our workforce by 83 new employees, to meet the expected market demand for our breakthrough product.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/889058 |
| Start date: | 01-01-2020 |
| End date: | 30-04-2020 |
| Total budget - Public funding: | 71 429,00 Euro - 50 000,00 Euro |
Cordis data
Original description
In the manufacturing sectors, the traditional planned maintenance approach is no longer viable, as it cannot cope with the ever-rising complexity of production systems. This pressing problem hurts industry’s profitability, and unplanned downtime costs industrial manufacturers €43 billion per year. This pressing problem has fuelled the growth of the predictive maintenance market. Currently, predictive maintenance solutions employ typical machine learning approaches based on monolithic rule-based predictions and require from the customer labelled data that correspond to defective machine states. This impedes the penetration of predictive maintenance in the industry. EnCORE is the fruit of 5 years of R&D to develop proprietary deep neural networks fit for predictive maintenance applications. Our solution uses best-in-class deep learning technology removing the overheads related with data preparation and enable the prediction of machine’s future condition using data that correspond to normal machine states. This is a game changing approach in the predictive maintenance industry. EnCORE is at TRL-6, with our software being validated at two different applications, (1) a compression moulding machine that produces plastic bottle enclosures/caps and (2) a cold forming machine that produces razor blades. Our target market will be the Food & Beverage and Consumer Goods industries targeting both OEMs of machinery and End-Users use such machinery. To take our product to the market, we will employ an hybrid business model using both direct sales and sales through industrial IoT platforms. EnCORE’s unique offering unlocks tremendous value for our customers; this will fuel the adoption of our solution by the industry. In the commercialisation period, we forecast cumulative profits of about €15 million with a strong Return on Investment (ROI) of €13 million. This will allow us to grow our workforce by 83 new employees, to meet the expected market demand for our breakthrough product.Status
CLOSEDCall topic
EIC-SMEInst-2018-2020Update Date
27-10-2022
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