Name
Title | ||
|---|---|---|
A Comparison Study of Single-Scale and Multiscale Approaches for Data-Driven and Model-Based Online Denoising. |
Abstract | ||
|---|---|---|
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. Copyright (C) 2014 John Wiley & Sons, Ltd. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1002/qre.1709 | QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL |
Keywords | Field | DocType |
online denoising,data-driven filter,model-based denoising,multiscale filtering,multiscale Kalman filter | Frequency domain,Noise reduction,Time domain,Process capability,Data-driven,Computer science,Filter (signal processing),Kalman filter,Artificial intelligence,Machine learning,Wavelet | Journal |
Volume | Issue | ISSN |
30 | SP7 | 0748-8017 |
Citations | PageRank | References |
1 | 0.44 | 3 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ricardo R. Rendall | 1 | 2 | 1.47 |
| Marco S. Reis | 2 | 1 | 0.44 |