Predictive maintenance, or predictive maintenance of industrial assets, is one of the most obvious expressions of the data-driven approach of artificial intelligence applied to the manufacturing ecosystem. As a direct consequence of the ever increasing diffusion of IoT and AI sensors in the context of production lines, predictive maintenance is essential to maximizing productivity, product quality and production agility.
Its relevance in the field of smart manufacturing is immense since a correct implementation depends on the ability to reduce downtime as much as possible, with all the consequences of organizational and image costs that derive from it. As the expression itself suggests, in the industrial environment the purpose of predictive maintenance is to predict and anticipate possible machine failures, so as to start the maintenance process before these occur. In this way, industries can obtain two great benefits: first of all, they move away from the reactive logic which inevitably involves prolonged periods of machine downtime; above all, they can go beyond the concept of programmed maintenance, which, albeit very useful, can always intervene where intervention is not (yet) needed.
On the other hand, predictive maintenance allows companies to intervene on their productive assets only when there is a real need and before faults occur: maintenance costs decrease, machinery lasts longer and, consequently, investments also decrease, which is why, within typical smart manufacturing applications, predictive maintenance always has a place under the sun. In addition to logic, its success is demonstrated by the numbers: according to Market Research Future, the market for predictive maintenance solutions could exceed $23 billion in 2025, with a CAGR growth rate of 25.5% from 2017 to 2025.
Predictive Maintenance, pillar of Industry 4.0
Predictive maintenance is not a technology in itself, but is one of many applications of the data-driven model within the industrial context. It is indeed from the union of sensors, machinery, networks, algorithms and processing platforms that the amount of data generated daily by industrial machinery can be transformed into information, then into analysis and, finally, into concrete actions.
From a technical point of view, enabling predictive maintenance is not a small challenge, since it involves identifying, within extremely fast-moving and bulky data sets, small but significant deviations from the standard, whose progressive evolution could lead to critical issues for machinery and therefore for production. The task is far from easy, since – while trying to simplify – the questions to ask are endless: what are the relevant data? When is some data to be considered anomalous and, above all, worthy of attention? How can a combination of data identify the possibility of a future machine failure? How to manage rare events, i.e. those events that do not have enough history to build a predictive approach? Many are the issues which a good technical preparation and specific sectoral competence – elements that, together, make a good Data Scientist – must be able to address, always taking into consideration both the experience of the people who work with the machines every day and the fundamental contribution of Machine Learning and its intrinsic capacity for self-learning.
In this way, predictive maintenance stands as a real pillar of smart manufacturing, but in truth it is a concept that goes far beyond the factory: for example, airlines can take advantage of its benefits to avoid inconvenience to passengers; in the energy sector it can be used to avoid failure on the lines and the same is true for a thousand other areas of use, including the possibility of directly intervening on consumer products in the post-sale phase. All these usage scenarios are viable, provided you adopt a data driven approach and an efficient system for data acquisition, management and processing: at that point, the results will be certain.