From predictive maintenance to artificial intelligence - The benefits for the management of production plants

Today everyone is talking about Predictive Maintenance and Artificial Intelligence to improve efficiency in the management of production processes. Let's see in this article how the two topics are related to each other and what benefits are expected in the near future from emerging technologies.

Predictive Maintenance

Predictive maintenance has developed since the '80s thanks to the availability of computers and software at low costs, mainly in the analysis of vibrations, tribology, and thermography. Various algorithms are available in the literature for the analysis of the anomalies of the rotating parts, and to detect electric motors malfunctions.

Predictive Maintenance allows companies to obtain many benefits in addition to the prediction of failures and therefore to the prevention of process interruptions.

Today we can use algorithms to analyze the process drift and to look for anomalies in the collected data, to keep high the efficiency of the machinery and to check the quality of the produced goods.

360 ° Predictive Maintenance: From the process data, predictive maintenance algorithms allow to predict machine downtime, maintaining high plant efficiency and the quality level of the processed product.

These are no less important results than the prevention of downtime. In general, predictive maintenance is not based on Machine Learning algorithms, although recent developments in the field of machine learning have extended the potential of data analysis and allow a more global vision of the problems related to the operation of the plants.

Artificial Intelligence

The Artificial Intelligence techniques (AI) applied to the analysis of process data are often referred as Machine Learning to emphasize the ability of computers to learn, from the data of the production context, that information that leads to plant control decisions, more or less automated. That AI is not the strong AI described in science fiction films, it is a specialized AI capable of doing what a human expert of a particular task can do.

Think of the algorithm of Google maps that finds the shortest route taking into account traffic jams and deviations. Can we consider it an intelligent algorithm? Surely, it is better than me and many humans I know! Many people today agree that it is easier to create small specialized AIs to perform specific tasks than to build a large AI capable of doing many things well.

Artificial Intelligence allows us to go beyond predictive maintenance, here the main extraordinary benefits.

Small AIs must then cooperate, or better coordinate, when there are specific problems to be solved.

That is very similar to a team of technicians working together, each with their own skills.

With AI it is now possible to predict malfunctions of machine parts, to identify process inefficiencies and correlations between physical phenomena in production processes and the final quality of products.

Other very interesting services are virtual assistants to human operators, to assist them in case of failure and to better configure the working parameters of complex production lines.

What Does The Future Have To Offer?

Research in the field of AI is working in many directions, one of these is the representation of knowledge in graphs of knowledge, which resemble the mechanisms of association of ideas of the biological brain.

With knowledge graphs, it is easier to identify causal links between events and to construct algorithms able to reason about chains of events.

Imagine a system that detects a sequence of alarms and signals from a production line and is able to find the causality between a signal moving from its optimal value (for example a leakage in the air flow rate in a tube) and the chain of events that follow at different times: a cooling system that does not work, an engine that turns off for thermal protection and finally a conveyor belt that locks up.

Predictive maintenance in the future

A system capable of concatenating events could predict the stop of the production belt as soon as the air flow in the monitored pipe decreases, and signal to a technician to intervene or plan the intervention.

Another huge result that can soon be reached with solutions based on AI is the possibility of finding optimal configurations of production lines made up of heterogeneous machines: most probably the optimal overall configuration of the parameters is better than the set of optimal configurations of the single machines.

Conclusions

Today, manufacturing companies are under pressure to achieve and maintain high levels of efficiency and quality.

Global competition requires continuous investments in new technologies that support entrepreneurs and their teams in improving overall machinery productivity.

In this race for performance, every hitch, every machine stop, every production quality defect, become handicaps that can compromise the hard work of the day.

Fortunately, the emerging technologies of artificial intelligence promise substantial help to remain competitive and manage increasing complexity.

Undoubtedly the unknowns of each new technology lead us to reflect on the times and ways in which today's problems can turn into opportunities for growth tomorrow.

Entrepreneurs are once again faced with a challenge: adopting new technologies first and gain a competitive advantage over the others or waiting for competitors to take the first step. 

Do you want to avoid unexpected downtimes and have total control over your plant? Contact us now to learn more.

Artificial Intelligence Techniques to assist business decisions and increase efficiency.

If you found this article useful, follow us on LinkedIn to be updated on other articles and events