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摘要
Signal denoising is a pervasive operation in most online applications, such as engineering process control and online optimization, strongly affecting the outcome of these higher-level tasks and impacting the overall variability exhibited by processes and products. Therefore, it plays a fundamental role in improving process capability, which is, however, often overlooked. In this work, we compare the performance of different types of currently available online denoising filters using a variety of test signals that represent the diversity of situations likely to be found in practice, properly corrupted with additive noise of varying magnitudes. Both single-scale/multiscale, data-driven/model-based and time domain/frequency domain, online filtering approaches were contemplated, in what is, to the best of the authors knowledge, the more extensive comparison study conducted on online denoising (or filtering) methodologies. A new class of multiscale denoising algorithms is also considered in this study, based on the online wavelet multiresolution decomposition. In this context, we propose and test a new formulation, called the online multiscale hybrid Kalman filter. After proper tuning, the methods are tested and their performances compared. As a result of the comparison study, clear guidelines are provided for practitioners on the use of online denoising methodologies, which allow for a better management of the impact of the propagation of unstructured components of variability in the final outcome of the processes.
- 出版日期2014-11
