Advanced Machine Learning Techniques for Data-Driven Monitoring of Coil Winding Processes in Electric Motor Manufacturing

Type: MA thesis

Status: running

Supervisors: Siming Bayer, Andreas Mayr (FAPS), Andreas Maier

Numerous industrial processes generate process data that can offer valuable insight into the manufacturing process. These datasets may include different types of information, such as temperature profiles, pressure measurements, and force-time curves, all of which are essential to understanding the dynamic nature of industrial processes. Driven by this, the evaluation of sequential data, specifically time series data, through appropriate machine learning (ML) techniques, particularly deep learning (DL), has gained significant attention. The possible applications of data-driven monitoring utilizing ML encompass detecting anomalies in manufacturing operations, predicting maintenance requirements to minimize downtime, optimizing production parameters for improved efficiency, and determining the resultant product quality.

The winding of coils is a fundamental process step in producing electric motors, generators, electromagnetic actuators, and transformers. The demand for these items, and consequently for coil winding, has increased with the transition to sustainable energy sources. DIN 8580 defines winding as the process of continuously bending the wire around a core part, such as a bobbin or stator tooth, to join them. For specific applications, especially small motors, the application of non-overlapping concentrated windings has become a common practice. The windings are wrapped around one tooth per coil, which increases mass production capabilities and reduces the size of winding heads, in line with the requirements of the automotive industry. The needle winding technique is widely adopted for producing concentrated windings in laminated cores of entire stators. The automated process performs both winding and coil joining. However, monitoring the stochastic process of needle winding presents a challenge due to numerous variables that can impact it.

In the early stages, Bosch, our industrial partner, upgraded certain winding machines to record all process data that could be important for monitoring purposes. This includes force, torque, and position curves, as well as additional tabular data such as wire length and stator tooth height. Initial attempts to predict the resulting quality of coils based on this data have shown basic potential but have been limited by small amounts of data and comparatively simple ML techniques. With the expansion of the database, our data-driven approach to process monitoring shall be further developed by incorporating more advanced ML/DL techniques. Thus far, limited research has been conducted on the implementation of ML or DL techniques for monitoring manufacturing processes using time series data, as the bulk of previous studies concentrated on time series forecasting for predictive maintenance use, rather than time series classification/regression and anomaly detection for the monitoring of manufacturing processes.

Overall, this thesis aims to develop a monitoring technique powered by data and employing advanced ML methods. The primary objective is to detect anomalies in the coil winding process, which will initiate a comprehensive process monitoring. This will enable engineers to optimize the manufacturing process. Anomaly detection serves two crucial purposes: accurately identifying established fault patterns and detecting previously unseen faults that may have evaded end-of-line testing. Detecting fault patterns reliably significantly improves overall product quality. One objective is to analyze the challenges that arise during the needle winding process and the factors that contribute to these difficulties. This involves using the winding machine’s control signals to assess the ongoing winding stages and detect sources of errors. Another objective is to determine how to handle various product types and machine configurations appropriately. A further objective is to select an appropriate ML or DL model by investigating different data preparation techniques. We plan to identify the best ML method through careful analysis and the establishment of a preliminary standard. A combination of models will also be considered. The final objective is to prototypically integrate the model into the ongoing production process and evaluate its effectiveness in a real-time operational setting. Through all this, we aim to demonstrate the potential of a ML/DL technique for data-based monitoring of coil winding procedures in the production of electric motors. This will enhance the quality of the process, provide valuable process insights, and reduce overall costs.