Data Analysis Means Greater Efficiency

The data volumes generated in industrial production are huge. For the quality assurance of electric drives just off the line, the test benches alone record data for thousands of parameters. However, these data, together with information from production itself, are opening up opportunities to create a more efficient overall process and maximum reliability for it. Ultimately, at end-of-the-line testing, the test benches deliver not only the final “OK” or “Not OK” test result, but also all kinds of raw data. This gives operators detailed insight into the product and the production workflows.

The pre-requisite for this, above all, is extremely high computing power. That's what makes “Advanced Data Analytics” possible, the magic word that enables ZF test benches to tap into the potential of production data. “In production, for example of transmissions, electric drives, steering or braking systems, we generate vast amounts of information“, explains Gert Jeckel, data analytics expert in test bench development at ZF. “Simply put, we collect all these data at a central location, like in our ZF IoT Cloud. There, smart, constantly learning algorithms analyze these data for repeating patterns that indicate possible problems.” If a product does not pass the final quality check, the data analytics applications not only look at the obviously exceeded limit value, but the overall picture of all measured parameters. This is how they immediately identify the cause of the malfunction. This knowledge enables accelerated repairs without having to spend valuable time looking for the cause after disassembly.

Identifying malfunctions that have occurred and quickly fixing them, however, is only one of the goals of data analytics. In order to avoid returns right from the start, the production information is also entered in the ZF data pool. Together with the test results, a digital twin that uniquely identifies the individual product is generated in this way. By comparing the incorrect test values with the production data, weaknesses in the production flow (for example, a damaged batch of components) can be recognized early on and eliminated. Jeckel adds: “We can address specific factors that generate increased material or labor expenses and subsequently make the processes more efficient and reliable.”

The ZF data analysis approach can be expanded to go above and beyond already existing systems. It is conceivable that end customers will one day be able to connect to the ZF IoT Cloud. Based on the actual use of a ZF system, algorithms calculate the next predictive maintenance appointment. They can also issue a warning early on regarding minor disruptions before they turn into major damages. Another advantage of this approach: “If we combine the knowledge gained from data analysis with our comprehensive product and process knowledge, we can offer our customers an optimal product tailored to their needs", adds Markus Kramer, Sales Manager ZF Test Systems. In the end, engineers can continuously enhance products based on the operational data and adapt them to customer needs.