In the long term, machine learning will replace conventional, manual predictive maintenance processes. AI can independently recognize patterns in machine data in order to predict faults and their causes even better. This also harbors risks for machine manufacturers.
Developments in the field of artificial intelligence are progressing at an astonishing rate. These impressive IT innovations are also revolutionizing customer service. AI-supported processes will become established in predictive maintenance in the not too distant future. But that is probably not even the end of this development: the trend is ultimately moving towards Smart predictive maintenance!
A machine learns the relevant characteristics of an object using sample data
Machine learning is a sub-area of artificial intelligence. In conventional computer programs, the solution to a problem is explicitly pre-programmed by an expert.
Machine learning, on the other hand, is a process in which a computer independently calculates meaningful models from existing sample data in order to solve problems automatically. In this process, algorithms learn to recognize correlations in the previously imported data and apply them independently to newly imported data sets.
You can imagine how machine learning works like this, for example: A computer should learn to recognize defective components. To do this, it is shown thousands of photographs of either defective or functioning components. For each photograph, the computer is given the information as to whether the component is faulty or intact. The computer then uses the image data to find out independently what the essential features of an image of defective parts are and in this way becomes better and better at recognizing them.
The implementation of machine learning in predictive maintenance solutions
For use in predictive maintenance, the computer must first learn to recognize when the system has a fault. First of all, you provide it with all the data you can collect from the system. As in the case of component recognition, the computer needs to know whether there is a fault.
The AI can then examine which patterns emerge in the data set directly before different faults. This makes it possible to predict failures ever more precisely and accurately or to prevent them by intervening in good time.
Machine learning vs. standard methods
Conventional predictive maintenance solutions rely on statistical procedures defined by a service expert. Here, people first make assumptions about correlations between different machine data in advance. These are formalized in mathematical equations and described as a relationship between two or more variables.
A service expert considers which data on the machine indicates a possible malfunction in the future. This task requires some specialist knowledge, but can also be automated with the help of machine learning processes.
In the article on Implementation of predictive maintenance describes exactly how you can create your first pilot project in just 8 weeks using conventional methods.
Advantages of machine learning for predictive maintenance
When implementing predictive maintenance solutions, the development of the data analyzer is the most time-consuming part. If you want to rely solely on the experience of your service employees, you usually have to limit yourself to just a few use cases at the beginning. Your experts will have to come up with every data constellation that could indicate future failures themselves. This is of course very time-consuming.
The algorithm makes this faster and better in the long term. It also recognizes risk factors for failures that your team would never have thought of. On the other hand, the implementation of artificially intelligent systems is of course initially more time-consuming and cost-intensive.
Why is machine learning not yet used across the board?
The implementation of machine learning to improve predictive maintenance strategies initially requires a very high initial investment. Profit, on the other hand, can only be made in the long term. New forms of service agreements are needed to monetize such innovations.
Many companies still lack the imagination for this, as they do not yet see service as an independent business area and business model innovations in this area are often beyond their horizon. Due to the direct impact on cash flow, machine manufacturers prefer to invest in improving their primary products rather than in service-relevant IT.
"The hardware focus of most machine and plant manufacturers is another obstacle to the development of such innovations. I often come across absolute professionals in the development of sophisticated machine concepts who, however, consider the creation of control software to be the Olympus of IT development. I think that's a shame, because a lot of potential is wasted here. New, innovative business models that offer real added value for customers can be developed from the higher-level analysis of operating data. In my opinion, it is questionable whether the lengthy development of a machine that works 3% more efficiently will achieve this in the future." - Dr. Simon Tonat
Artificial intelligence could become a problem for OEMs
The advantages of artificial intelligence over conventional predictive maintenance methods are clear. The current methods will probably be largely replaced in the future. Although there is still a long way to go until then, there are already dangers on the horizon for the German mechanical engineering industry.
This is because the development of machine learning-supported predictive maintenance solutions requires no or far less specific plant knowledge. After all, AI uses algorithms to identify a machine's problem without the need for a maintenance expert. This offers IT companies the opportunity to enter the market.
This development is also reflected in our contribution to the Future of the service business described in detail. There you will learn how the After-Sales-business will change.
The path towards smart predictive maintenance?
However, digitalization offers much more far-reaching possibilities than simply monitoring individual systems with artificially intelligent systems. In Industry 4.0 scenarios, it will be possible to predictively maintain an entire network and ultimately the entire production of a company. Machine learning will also be able to read error patterns from the data of different systems in the production processes as a whole.
Such smart predictive maintenance systems can then also be trained to solve problems independently. For example, when the system triggers maintenance orders, schedules a ticket in the system and assigns an available technician to the task without a human being requesting it. It would also be possible for an intelligent system to check the spare parts stock and order any parts required independently.
Service will change!
Of course, you are often overwhelmed by these rapid developments and cannot jump on every bandwagon immediately. Nevertheless, you should be aware of the importance of AI for development in the service business.
Despite all the doubts and concerns, a little fascination for the possibilities that arise from such innovations is certainly appropriate. Even if it is still somewhat in the realm of science fiction at the moment: The service business will be transformed by machine learning and AI, and not just in the area of predictive maintenance.
