It is often required to carry out sensor-based condition monitoring for machines or operations (e.g., machining centre, foundry) during production to ensure the effectiveness. Due to the requirements of a non-invasive installation or no interruption during production, however, it may be difficult to fully instrument the machine or production equipment with monitoring sensors. As an alternative to the direct monitoring, it is possible to use energy power or temperature data, and other easy-to-install sensors measured with relatively high time resolution (~2 s) to provide enough information to effectively infer events and other properties. From this reason, the ability of inferring becomes important. To introduce how the inferencing technology can be used in the energy management, this chapter presents a pattern-based energy consumption analysis by chaining Principle Component Analysis (PCA) and logistic regression. The PCA provides an unsupervised dimension reduction to mitigate the issue of multicollinearity (high dependence) among the explanatory variables, while the logistic regression does the prediction based on the reduced dataset expressed in orthogonal axes that are uncorrelated principle components represented by Eigenvectors found in the PCA. By chaining the PCA and logistic regression, it is possible to train manually time-logged energy data and to infer the events associated with the manufacturing operations. It is expected that the proposed analysis method will enable manufacturing companies to correlate energy and operations and further use the power data to predict when operation events of interest (e.g. start up, idle, peak operation, etc.) occur, resulting in determining how current energy usage levels in manufacturing operations compares to the optimal usage patterns. This chapter also provides a short instruction to Python and IPython Notebook. It illustrates a supervised learning process by using Python to carry out pipelining PCA and logistic regression and applying a grid search to training and inference energy consumption patterns.
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