Embedding Intelligent Features for Vibration-Based Machine Condition Monitoring

Today's demands regarding workpiece quality in cutting machine tool processing require automated monitoring of both machine condition and the cutting process. Currently, best-performing monitoring approaches rely on high-frequency acoustic emission (AE) sensor data and definition of advanced features, which involve complex computations. This approach is challenging for machine monitoring via embedded sensor systems with constrained computational power and energy budget. To cope with constrained energy, we rely on data recording with microelectromechanical system (MEMS) vibration sensors, which rely on lower-frequency sampling. To clarify whether these lower-frequency signals bear information for typical machine monitoring prediction tasks, we evaluate data for the most generic machine monitoring task of tool condition monitoring (TCM). To cope with computational complexity of advanced features, we introduce two intelligent preprocessing algorithms. First, we split non-stationary signals of recurrent structure into similar segments. Then, we identify most discriminative spectral differences in the segmented signals that allow for best separation of classes for the given TCM task. Subsequent feature extraction only in most relevant signal segments and spectral regions enables high expressiveness even for simple features. Extensive evaluation of the outlined approach on multiple data sets of different combinations of cutting machine tools, tool types and workpieces confirms its sensibility. Intelligent preprocessing enables reliable identification of stationary segments and most discriminative frequency bands. With subsequent extraction of simple but tailor-made features in these spectral-temporal regions of interest (Rols), TCM typically framed as multi feature classification problem can be converted to a single feature threshold comparison problem with an average F1 score of 97.89%.