CONSENSORS: A Neural Network Framework for Sensor Data Analysis

Abstract

Machine breakdowns in industrial plants cause production delays and financial damage. In the era of cyber-physical systems, machines are equipped with a variety of sensors to monitor their status. For example, changes to sensor values might indicate an abnormal behavior and, in some cases, detected anomalies can be even used to predict machine breakdowns. This procedure is called predictive maintenance, which pursues the goal to increase machine productivity by reducing down times. Thereby, anomalies can be either detected by training data models based on historic data or by implementing a self-learning approach. In this work, the use of neural networks for detecting anomalies is evaluated. In the considered scenarios, anomaly detection is based on temperature data from a press of a machine manufacturer. Based on this, a framework was developed for different types of neural networks as well as a high-order linear regression approach. We use the proposed neural networks for restoring missing sensor values and to improve overall anomaly detection. An evaluation of the used techniques revealed that the high-order linear regression and an autoencoder constitute best practices for data recovery. Moreover, deep neural networks, especially convolutional neural networks, provide the best results with respect to overall anomaly detection.