Intelligent energy audit and machine management for energy-efficient manufacturing

To reduce energy consumption for sustainable and energy-efficient manufacturing, a good understanding of the dynamic energy consumption patterns on the manufacturing shop floor is essential. In this paper, we introduce a novel approach to address the challenge of missing operation context information during in-situ energy data measurement. Finite-State Machines (FSMs) are used to model the engineering processes, and a two-stage framework for online classification of real time energy measurement data in terms of machine operation states is proposed for energy audit and machine management. The first stage uses advanced signal processing techniques to reduce noise while preserving important features, and the second stage uses intelligent pattern recognition algorithms to cluster energy consumption patterns. Our proposed two-stage framework is evaluated on an industrial injection moulding system using a Savizky-Golay (SG) filter and a Neural Network (NN), and our experimental results show a 95.85% accuracy in identification of machine operation states.