Abstract
Manufacturing industry incurs a large portion of energy consumption and carbon emission in the economy. Traditionally, smart energy management depends on aggregated measures from billing information, as well as physics-based models, empirical results derived from extensive experiments, which tend to be limited in the ability for real-time monitoring of energy efficiency. There is an urgent need to develop energy monitoring solutions for more transparency about energy use. This paper presents a new sensor-based approach for recurrence analysis of continuous power signals and multi-state modeling of energy efficiency in the machining process. First, we leverage the recurrence plot to characterize the nonlinear variations in power signals and further help delineate different states in the machining process, thereby providing statistics of energy consumption in each state. Second, we compute the composite index of energy efficiency for each workpiece and then develop multivariate statistical control charts for process monitoring of continuous production of workpieces. Third, after an anomaly is detected, we propose the orthogonal decomposition approach to diagnose the root cause of abnormal states in the energy use. The proposed methodology is evaluated and validated on real-world manufacturing of shaft-like parts in a machine shop. Experimental results show that the prediction error of energy efficiency from sensor-based models is within 5% from the ground truth, which show great potentials to implement sensor-based monitoring and analysis of real-time energy efficiency in the manufacturing process.
Highlights
Manufacturing is a wealth-generating sector in the global economy and provides critical equipment and products for national infrastructure and defense
The objective of this paper is to develop sensor-based recurrence models for energy efficiency monitoring and root cause diagnosis to analyze the critical factors of energy consumption in the machine shop
2) This study has proposed to use an orthogonal decomposition method to diagnose the root cause of a detected anomaly that delineates the variations of energy use at each state of the machining process
Summary
Manufacturing is a wealth-generating sector in the global economy and provides critical equipment and products for national infrastructure and defense. Manufacturing industry often incurs big social and environmental costs, e.g., energy consumption and carbon emission. The optimization of energy consumption practices and the improvement of energy efficiency are urgently needed for the manufacturing industry. ISO 50001 provides a framework of requirements for organizations to continuously improve energy performance and specify requirements for process and equipment design, measurement and documentation [2]. Machine tools are the substantial energy consumer in manufacturing industry. International standards for the environmentally-benign machine tool are introduced. ISO 14955 prescribes the environmental evaluation methods of machine tools, including the design methodology for energy-efficient machine tools and measurement methods for energy consumptions of specific components of machine tools [4], [5]
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