Industry 4.0 is facing unprecedented challenges. On the one hand, small and medium-sized enterprises must continue to drive digitalisation in order to remain competitive. On the other, however, the growing complexity of industrial equipment often exceeds the knowledge of even experienced service technicians.
We are part of a consortium of 15 companies, professional associations, and universities working on the Service-Meister (Service Master) project to develop an AI-based service platform for German SMEs. The platform is intended to enable even inexperienced professionals to carry out more complex tasks with the help of AI-based service bots and data-driven services which we are applying in two so-called Schnellboote with KROHNE and the esw Group, respectively, are intended to help.
Intelligent measurement technology
In many industries, measuring points are still read manually – even if they are often difficult for technicians to access. This takes time, especially when extensive equipment networks are involved. The Industrial Internet of Things (IIoT) enables companies to gain live insights into their equipment processes and to detect impending failures or behaviour that deviates from the norm at an early stage. Centralised management of the measuring points makes the technicians’ jobs easier.
In collaboration with KROHNE, the leading manufacturer of process measurement technology, we have developed a solution that links water management to the Internet of Things (IoT). This solution is designed to enable KROHNE’s customers to keep an eye on their supply, sewage, and service networks remotely.
Intelligent service ticket system
To organize maintenance orders for industrial machines, companies have so far mostly used their own solutions, some of which have been in operation for many years and lack intelligent functionalities. The aim is to support machine operators in the creation of maintenance orders by using historical maintenance information and thus to call up the most suitable team of technicians when malfunctions occur or maintenance is necessary.
In collaboration with esw Group, we have developed an intelligent service ticket system that optimizes knowledge transfer between machine operators and maintenance technicians, using AI-powered algorithms to accurately describe problems that occur on production equipment. The machine operator receives AI-generated suggestions as to which maintenance category (mechanical, electrical, …) an occurring problem falls into in order to assign the right service technician. This way, unnecessary maintenance loops on production equipment can be reduced.
Support in the service field
However, the growing technical possibilities in measurement technology as well as in plant engineering create new challenges for service personnel who repair and maintain the machines. Even experienced technicians are not able to know and manage all systems in detail. In order to enable both experienced and less experienced workers to provide rapid, expert service, artificial intelligence (AI) is used.
For the esw Group’s service staff, we have developed an intelligent search function that gives maintenance technicians direct access to comparable problems at similar plants and the associated solution options. In addition, we have implemented AI functions in the Service Master project together with KROHNE, which can, for example, analyze sensor data with AI methods, predict malfunctions, and optimize the deployment planning of skilled workers. As an interface between the systems and service staff, chatbots ensure simple and intuitive querying of all relevant information. They support both experienced and inexperienced measurement technicians in complex tasks such as finding the cause of a fault and initiating the optimal repair steps.
Funding for the Service Meister project has been provided by the German Federal Ministry for Economic Affairs and Climate Action.