One of the main applications of artificial intelligence in industrial environments is predictive maintenance. If before there was a human maintenance team, with protocols and routines for 'review and monitoring' (and repair if necessary) of each segment or area of the production chain, AI can do it much more accurately and with fewer resources.
Artificial Intelligence. It seems incongruous, but we have already become so accustomed to the concept that it has become part of our daily lives, and even in our homes we have devices that use this technology, starting with our cell phones. In companies the use is much wider and deeper, and AI is present in the innovation and transformation of most business and industrial processes, covering all automation and any robotic process.
But, let's go first to the concept, what is AI? The term was coined by the American scientist and mathematician John McCarty as early as 1955. It is a branch of computer science that tries to mimic the functioning of human neurons in machines. From then until today, thanks to the great speed of calculation that today's computing allows, some of these machines already come very close (some even believe that they surpass) this concept of 'thinking'.
The development of this technology has advanced by leaps and bounds in this era. Partly due to its very nature, which allows it to 'learn' and be multidisciplinary, like human intelligence. But, above all, it reacts to changes in its environment (for which, previously, it has to analyze it and possess all the data about it). It is this ability to solve problems and adapt that makes AI so valuable.
Because we are talking about an environment, the virtual one, which is becoming increasingly vast and unmanageable: petabytes of data generated day by day, minute by minute, second by second. Well, an AI can handle and accurately calculate the different possibilities resulting from a situation and choose the optimal one. It can also identify patterns to predict and prevent, and it can issue failure alerts long before they occur. And it can even 'steer' humans towards solving a problem.
AI becomes a tool and guides through a warehouse to find exactly where a product is located. It analyzes the fuel consumption of an entire fleet of vehicles and identifies areas for improvement. It accurately calculates the parameters and structures in a building and even finds security threats and patches them before we even know they exist! In industries, in particular, where hundreds of thousands of data from customer inputs, sensor analysis, environmental parameters, product parameters, etc. must be taken into account, it can take care of (almost) everything, even before it is detected that action is needed.
One of the main applications of artificial intelligence in industrial environments is predictive maintenance. If before there was a human maintenance team, with protocols and routines for 'review and monitoring' (and repair if necessary) of each segment or area of the production chain, AI can do it much more accurately and with fewer resources. Because it is able to identify and calculate the in-service time, estimated life cycle and stress on individual parts and components in the supply chain, it can also predict quite accurately their potential failure points. It can also tell us when maintenance checks should be carried out or, in the case of software, send the necessary upgrade and update packages itself.
In this way, thanks to Artificial Intelligence, it is possible to avoid downtime or outages in the service and, therefore, in production, with the consequent cost savings that this entails. But, in addition, the life cycle of the parts is increased and therefore sustainability, by discarding and throwing away fewer products (they can be taken out of service when they are not yet broken, which facilitates their reuse or recycling). Finally, maintenance costs will be much lower, since the system will be monitored at all times by the AI.
In predictive maintenance, a combination of IoT sensors that monitor each of the parameters established in the production systems (noise, vibration, temperature, etc.) and the analysis algorithms that receive and calculate all this data to truly predict what can happen is essential. These data sources are increasingly larger and produce more and more information, so only systems equipped with artificial intelligence, edge computing and machine learning will be able to automate these prediction models and generate the corresponding warnings.
This model is especially interesting in industrial environments, both those purely oriented to manufacturing (automotive, consumer goods, etc.) and the so-called 'critical sectors' (energy, transportation, etc.), with high and intense activity and a great impact on service and billing in case of interruption or downtime.
Imagine a power plant serving an entire city. Of course, the system has redundant security measures and continuous monitoring by both automated systems and human teams, as well as 'rescue teams' ready to act urgently in case of problems. A system failure or outage will not only leave thousands of citizens without electricity, but will also cause traffic and transportation chaos, millions of dollars in losses for companies (production cuts, refrigerated or frozen goods, physical risk for workers), etc.
In these cases, as in all those involving intensive use of assets (energy, transport, water, utilities, healthcare, etc.), companies must be committed to maximizing the performance of these assets, while keeping them up to date and renewed - pipes erode, cables break, roads wear out.
It is clear that the advantages of applying AI to predictive maintenance are enormous, and not only practical, but also economical. Although these systems are 'accused' of being costly and unprofitable, the reality is that the ROI, according to surveys of companies that use them, ends up being positive in most cases. The Edge Industrial Barbara Platform is aimed at facilitating the implementation of these predictive maintenance models. It not only allows the capture of data from sensors, actuators and other industrial elements of any type, but also facilitates the execution of edge computing algorithms, allowing the implementation of much more complex predictive models and automated decision making. Among other functionalities it achieves:
The truth is that all this data and analytics provided by artificial intelligence are the boost that industry 5.0 needs, and already has, and what will make the factories of the future more efficient, reduce their energy consumption, increase their safety and continue to adapt, however fast the pace of innovation, to each new circumstance. It is even expected that, with the increase in computing speed and networks (the 5G or even 6G, which will undoubtedly come), this intelligent technology will even be able to 'read between the lines' and incorporate into its algorithm not only the data that is there, but also the data that is missing. This, while already done on a small scale at more local levels (Edge computing, where data is available faster and at a higher resolution), will soon be available for more massive applications as well.
For more information contact Barbara's team here.