With the help of industrial analytics, companies want to gain added value from production data. Intelligent data analysis software alone is not enough; it must also incorporate (human) knowledge about the specific application and the process.
Thanks to the digital transformation, ever larger amounts of data are produced in the production context (Industrial Big Data). Values such as temperature and pressure measurements or engine performance data have enormous potential if they are sufficiently analyzed and evaluated with the help of data analytics.
Definition of Industrial Analytics: Generic term for the evaluation of all data arising in industry, both machine-generated and from the interaction between man and machine.
The term Predictive Maintenance goes hand in hand with the term Industrial Analytics. Measurement and production data of machines and systems are to be used to derive maintenance information from them and, at best, to predict downtimes before malfunctions even occur. An example: Together with the Smart Data Solution Center Baden-Württemberg, the mechanical engineering company Hermle developed a method for evaluating machine conditions. The SDSC-BW team recorded maintenance data of a machine type for a period of twelve months. With the help of monitored learning methods, the experts derived an approach from this that enables automated evaluation of the machine state.
Smart data analysis does not necessarily require huge data volumes, Andreas Wierse stated, Managing Director of Sicos BW, who founded the Smart Data Solution Center Baden-Württemberg together with the Karlsruhe Institute of Technology (KIT): “Even a lot of small amounts of data can be profitable in combination with other external information. The decisive factor is that data analysis must be able to detect patterns or connections that provide valuable indications for possible process improvements.”
In addition to preventive maintenance, quality assurance is an important application field for industrial data analytics. For example, the mechanical engineering company Grenzebach uses an industrial analytics solution from automation specialist Weidmüller for real-time quality assurance for its friction stir welding systems. “The analytics software compares the forces detected on the sensors during the welding process with an ideal reference data set. As soon as there is a deviation that is outside the defined parameters, the machine operator receives a message and immediately knows that something is not right. This eliminates the need for manual inspection of each weld seam,” Weidmüller’s data scientist Dr. Daniel Kress explained.
To determine the reference model, Weidmüller evaluated the data sets of more than 100 weld seams together with Grenzebach and evaluated them using intelligent data analysis methods. The know-how of a mechanical engineer is therefore an essential part of the analyses. Analytics software can predict an error with a certain probability, but the prerequisite for this is always that the error has been classified beforehand.
Interplay of Analytics and Human Expertise
Deniz Ercan, Head of Nexed Data Analytics at Bosch Connected Industry, also confirmed the importance of application and process know-how of experts on site: “A data analysis specialist cannot simply march into a production process, pull data from different sources and let his software work for itself. Instead, the key lies in the interaction of data and human knowledge.”
In addition, the data must first be prepared. “Depending on the type and age of the machine, the data are often available in a wide variety of structures and formats. Accordingly, collection and standardization are often the most difficult and tedious step,” Mr. Ercan stated Only then can the actual analysis begin. Studies show that up to 80 percent of the effort involved in a data analytics project is invested in data collection, cleansing and preparing.
Reduced Scrap and Rework Rates thanks to Data Analytics
Bosch expert Deniz Ercan related a data analytics success story: “The quality of a sensor layer fluctuated greatly at a particle sensor manufacturer. Despite intensive research into the cause, however, it was not possible to get to the bottom of the matter.” The data analysis specialists compared all available data – including those that were only indirectly related to the actual problem – and found that a completely different sensor layer was responsible for the quality differences.
“That was surprising enough for our customer. But we were also able to identify a hitherto unknown problem with pseudo-scrap,” Mr. Ercan continued. Due to an error in the machine control, qualitatively flawless parts were classified as rejects. “Once known, employees were able to fix the problem immediately. That alone saved the customer approx. 1000 euros per day, and the cost of data analysis was amortized within a single week.”
DataProphet, an award-winning AI-as-a-Service company, has seen significant success in the foundry space, achieving an average of 40% reduction in scrap for one of the leading foundries in the Southern Hemisphere – resulting in the client realizing an ROI of 19x their initial investment over a 2-year period.
As stated by the foundry’s CEO: “We might have been able to achieve similar results in the past, but we had absolutely no clue what we did to achieve the good result. With artificial intelligence, we have a really good idea of what we need to do to improve production. DataProphet’s prescriptions have resulted in a significant reduction in scrap and rework, making a positive impact on our bottom line.”
The reduction in scrap not only resulted in the bottom-line ROI but significantly accelerated the client’s process of achieving their vision—becoming one of the best foundries in the world; the foundry customer generated record production outputs in 2018 & 2019. Simultaneously, this automotive OEM reduced its carbon footprint, saving 135kg of carbon dioxide emissions for each defective block not shipped.
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