Predictive maintenance through the use of artificial intelligence is one of the most interesting aspects of the fourth industrial revolution. More and more companies are adopting AI as part of the industry 4.0 plan, thanks also to state incentives. Let’s see how you can use this technology also in your company, how it works and what advantages it offers, especially for the prevention of downtime.
Talking about predictive maintenance no longer carried out with human personnel, but thanks to artificial intelligence, means bringing real change to the company and relying on increasingly advanced machines and robots. In fact, new technologies not only communicate with each other, but they also know how to learn from the situations that arise during the production process.
what is predictive maintenance?
Predictive maintenance techniques are designed to help determine the condition of in-service equipment in order to estimate when maintenance should be performed. This approach promises cost savings over routine or time-based preventive maintenance, because tasks are performed only when warranted. Thus, it is regarded as condition-based maintenance carried out as suggested by estimations of the degradation state of an item.
How does the way we work change?
This is a particularly important technological evolution especially for predictive maintenance, the aim of which is to guarantee real-time intervention on the components of the machinery, repairing and replacing the parts only when a fault occurs.
You will immediately understand how the advantages for your company are, therefore, remarkable, especially in terms of efficiency and savings, since you can avoid machine stoppages and production stoppages. In addition, employees must also quickly learn a new way of working, based on robotic machinery and managed precisely by AI.
IoT sensors and AI in predictive maintenance
Thanks to AI and IoT sensors it is now possible to plan an intervention on the machinery not only considering the historical and statistical data, but also verifying in real time the state of health and the possible occurrence of faults in the system.
Modern AI solutions applied to industry 4.0 allow your company to implement predictive maintenance solutions based on the data collected and processed by smart objects, i.e. each machine with IoT sensor that connects to the network. Without forgetting that communication can take place not only between machines within the company, but also outside or in the cloud . Let’s briefly see the steps necessary to do predictive maintenance with artificial intelligence in your company.
To bring predictive maintenance to your company, the first step is to choose which KPIs you want to monitor, clean the necessary data and enter them in the system. Usually this is information related to the machine, such as dimensions, manufacturer and model; telemetry data collected by sensors such as temperature, pressure or speed; history of faults and past interventions.
The next step is the identification of statistical and predictive models to identify a priori any malfunctions to be subjected to verification and self-correction by AI through machine learning processes.
Once the best algorithm has been identified, we move on to the distribution of the model which, with the new IoT scenarios, is based on Edge Computing. What is it about? You must know that the artificial intelligence models for predictive maintenance are distributed directly in the perimeter devices, to allow processing in the vicinity of the data both in terms of time and in terms of space. A truly important technological evolution that brings your company advantages in terms of efficiency, productivity and savings thanks to the elimination of the risk of downtime.
No more downtime thanks to AI
As we have seen, artificial intelligence and IoT sensors collaborate today with human personnel to avoid downtime and increase the efficiency of your company. It is therefore clear that predictive maintenance is not only a technological response, but also a business response.
The advantages of an AI-based data analysis system allow, for example, to improve the control of the state of health of machinery, but also to improve energy and production efficiency. Compared to an assessment based only on the physical parameters of the individual machinery, AI is able to inform the person of the overall state of the system by offering a more general view.
We can therefore say, and you too will know, that the maintenance of an entire plant is complex since it is not the simple sum of the machinery that makes the factory.
Let’s take a practical example. In the case of an in-line system, if a machine fails, the entire line stops, while in a system with multiple lines, the machine downtime of one of them would not affect the functionality of the others. Finally, we must not forget the human component, fundamental for the management and operation of the factory.
Thanks to artificial intelligence you can count on a valid ally in the management of the Smart Factory and you will no longer risk blocking the production process, with all the negative consequences of the case, for a machine stop.
The challenges to be faced in the field of predictive maintenance
Among those working in the sector it is said that “the global optimization of industrial parameters is a complex problem”. This also occurs in the methods of analysis of the production activity at the basis of predictive maintenance, which start from the individual components to describe the general system.
The biggest challenge facing predictive maintenance operators is precisely the need to obtain information and data on the system, machinery and types of faults. The awareness of everything that happens in the system will then allow you to implement the new AI solutions and install modern IoT sensors in Industry 4.0 , creating a model capable of improving business efficiency.
The new technologies bring many advantages to the company, but also greater complexity and therefore require staff capable of developing complex machine learning algorithms and AI, which can give concrete support to human staff in predictive maintenance and capacity planning.
