Big-data-driven anomaly detection in industry (4.0): An approach and a case study

In this paper we present a novel approach for data-driven Quality Management in industry processes that enables a multidimensional analysis of the anomalies that can appear and their real-time detection in the running system. The approach revolutionizes the way how quality control (and esp. anomaly detection) will be realized in production processes influenced by many parameters that can be in complex nonlinear correlations. It consists of two main steps: learning the normal behavior of the system (based on past data) and detecting an anomalous behavior in the real-time (by processing real-time data). The approach is especially suitable for modern industry systems that follow Industry 4.0 principles of ubiquity sensing and proactive responding. One of the main advantages is the self-adaptive nature of the approach due to its data-driven orientation, so that the model and parameters of the approach will be continuously updated to the dynamicity of data. The approach has been applied in the process of manufacturing microwave ovens (Whirlpool) and in this paper we present results for the data-driven quality control of one of the most critical parts — microwave oven fan. Due to the high speed of the rotation, every item has to be very precisely produced (according to the CAD model), which requires very strong quality control process