Machine state forecasting technology coupled with product quality control to unleash productivity for the manufacturing sector (EnCORE)

Description du projet

Anticiper les pannes de machines

L’usine du futur sera capable de prévoir et de répondre à tout, des machines individuelles aux systèmes de chaîne de production. Passant d’une maintenance programmée et un entretien régulier des machines à une maintenance prédictive, les usines seront en mesure de prévenir les défaillances des actifs en anticipant les problèmes avant qu’ils ne surviennent. Le projet EnCORE, financé par l’UE, va développer une approche qui changera la donne en matière de maintenance prédictive. Il aura notamment recours à une technologie d’apprentissage profond pour permettre la prédiction de l’état futur d’une machine à l’aide de données correspondant aux états normaux de la machine. Le projet s’efforce de lancer cette nouvelle solution sur le marché. Son logiciel est en cours de validation pour deux applications: une machine de moulage par compression qui produit des enveloppes de bouteilles en plastique et une machine de formage qui produit des lames de rasoir.

Objectif

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.