In practice, maintaining a diverse plant fleet is a highly complex task: planned and unplanned work must be balanced with the generally limited capacities. Production priorities have to be taken into account just as much as the skills and experience profiles of employees when it comes to assigning the right person to the specific task - always a challenge, especially in times of a shortage of skilled workers. The fact that maintenance always has to deal with an inhomogeneous plant fleet increases the complexity - because even plants from the same manufacturer have their own specifics, depending on the year of construction and operation, which must be taken into account.

As maintenance activities are often not yet fully digitalized, fault reports or fault logging, communication with specialists or between individual teams and the prioritization of pending tasks are often bumpy. Often, neither real-time access to information nor the simple evaluation of historical data is available - both of which are key productivity drivers for maintenance staff. The planning and execution of maintenance and repair work will continue to be 'people business' in the future - in other words, it will be highly dependent on the experience and specialist knowledge of the experts involved. Even in the medium term, complex technical problems will demand a great deal from specialists with their specific skills.

The maintenance of the future

But what levers are there to make the work of the people involved easier? Efficient maintenance ultimately not only has a positive effect on costs, quick problem solving also improves overall equipment effectiveness (OEE) - maintenance is a key value driver in production.
It is essential for efficient work to avoid system breaks and establish information symmetry. This means that all players in the complex factory ecosystem should work in a networked and intuitive real-time data environment. This also involves the creation of efficient human-machine communication: with the advancement of plant networking via IIoT or as part of multi-agent systems, production plants are generating an ever-increasing amount of information. This offers great potential for automating previously manual processes and relieving employees of repetitive tasks.

Human-machine communication

These considerations formed the basis for a collaboration between 5thIndustry, a provider of store floor applications, and Wandelbots, a provider of no-code robot solutions. The aim of the collaboration was to integrate faults recorded by an industrial robot directly into the cloud-based ticket system of the maintenance staff. To this end, Wandelbots' 'Fleetmanager', an application for fleet monitoring of 6-axis robots, was linked to the smart maintenance software '5i.Maintenance'. The specific data is sent to the cloud via an edge device. This makes it possible to automatically determine whether an emergency stop has been initiated, a process interruption triggered or a force limit exceeded.

Figure 1 illustrates the procedure: The robot goes into fault (1). This information is processed by the 'fleet manager' (2) and forwarded as an event (3) to a message queue (4) to the maintenance app hosted in the cloud. Forwarding the message via a message queue guarantees resilient, asynchronous processing of the message, which ensures that the information is not lost due to network disruptions or short-term system failures. In addition, this publish/subscribe concept allows the message to be processed by different subscribers (5). In the assumed case, a microservice of the maintenance app processes the information, after which the relevant data is translated into a maintenance ticket. For their part, the responsible maintenance staff receive a push or email notification; in this ticket, they see all the necessary information about the fault message directly on their tablet or cell phone (6). The incident can now be processed via the maintenance app (7).