Using FliPSi to Generate Data for Machine Learning Algorithms
Abstract
Cyber-Physical Production Systems (CPPS) are becoming increasingly important in modern manufacturing, which leads to a growing need for automated anomaly detection, maintenance decision-making, and fault diagnosis. Artificial Intelligence (AI) and Machine Learning (ML) algorithms are often used to perform these tasks. However, there is a shortage of real data sets with fault modes, making it difficult to train ML algorithms. To overcome this problem, we propose the use of the Flexible Production Simulation (FliPSi) to generate simulated data for the development, training and evaluation of ML algorithms. FliPSi is a simulation tool developed in Unity, a game engine, which is used to simulate modular CPPS. It allows the construction of different CPPS, data collection, and generating data which could realistically originate from CPPS. The focus of FliPSi is to generate data for the development, training and testing of anomaly detection and diagnosis algorithms. In this paper, we show that data generated with FliPSi can be used to train ML algorithms, specifically recurrent neural networks (RNNs) and variations such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), which are specifically designed to process sequential data and that the algorithms trained with these data sets can be employed to detect simulated anomalies. With this, we show that FliPSi can be employed to overcome a blocking issue in using ML algorithms for anomaly detection.
Citation: A. Liebert, C. Wittke, J. Ehrhardt, R. Jaufmann, N. Widulle, S. Eilermann, M. Krantz, O. Niggemann, “Using FliPSi to Generate Data for Machine Learning Algorithms,” ETFA - IEEE Conference on Emerging Technologies and Factory Automation, 2023. doi:10.1109/ETFA54631.2023.10275500.