44: Industry in the Future: UTC an academic partner for enterprise

The ongoing digital transformation of industry is a major societal challenge. For UTC, accompanying a growing number of companies during the changes, the phenomenon represents an increasingly strategic field for studies. This Dossier zooms in on the university’s main activities and on the specific nature of its approach to the industries of the future.

44: Industry in the Future: UTC an academic partner for enterprise

Heading for more, better, predictive maintenance

The future of industry also lies in development of predictive maintenance protocols on the production lines. UITC’s Roberval Laboratory is working on an innovative methodology to better detect the fore-signals of a risk of machine failures.

Currently, in order to avoid as far as possible break-downs and failures of industrial tools and costly non-programmed down-time repair sessions, companies today tend to practice preventive maintenance, which can be systematic, or at predefined points in time or decided conditionally. In the latter case, for example, maintenance actions are triggered generally by indicators such as excessive wear of a tool. But, to better anticipate risks of break-downs and keep the number of maintenance operations down to the strict amount necessary, the ideal situation would consist of continuous data collection about the state of the production system, thereby ensuring a reliable projection of its evolution in time. This is the principle that underpins preventive maintenance, i.e., prediction as to what moment(s) will see a possible breakdown occurring.


The connections between product, process and maintenance

A proactive approach, such as just described, is more complex to implement and, consequently, remains rare in industry today. But the upsurge of quantities of data collected on production lines and the possibility to use automated data analysis, will no doubt accelerate the movement. With this in mind, the UTC-Roberval Laboratory has begun research on an original methodology that opens the path towards more reliable, more accurate predictions. “Today, in our factories, maintenance policies for the machines tend to be disconnected from process monitoring and product quality considerations”, explains Zohra Cherfi, a research scientist working at UTC-Roberval. “And yet, when you think about it, line maintenance determines the process quality and thus, in part, the product’s quality. These are early days for our research but we have the objective to identify those signals in process behaviour and/or in product quality observations, that can alert the operatives as to a risk of machine breakdown and/or failure, and with these we hope to build an aid-to-decision tool to optimize maintenance policies and their implementation”. Amélie Durupt, likewise a research scientist at UTC-Roberval stresses that “This is a novel approach. To be fair, there is abundant literature about the links between process, product and maintenance, but the papers mostly relate to systematic maintenance scheduling and not in regard to establishing rules for decision that take these three parameters into account, leading to making the right decisions at the right moment, in a relatively automated fashion”.

UTC-Roberval will be engaging its scientists on two research projects with industrialists concerned by this novel topic.