Comparative analysis of discrete wavelet transform and singular spectrum analysis in signal trend identification
Abstract
Trend identification gives a general tendency of a signal and as such can represent the first step in
signal change detection. This is often a method for extracting useful features from the measured signal that can be transferred to meaningful conclusions, which can serve as a basis for process control or predicting the future behavior of the process. Fourier analysis, owing to its simplicity, is traditionally applied in time series processing; however, it has shortcomings in the analysis of signals that are not periodic. As a result, a number of techniques with better performance in non-stationary signal analysis have been proposed. In this paper, we carry out comparative analysis of two techniques for trend identification of non-stationary signals: Discrete Wavelet Transform (DWT) and Singular Spectrum Analysis (SSA). The analysis involves examining the applicability of these techniques both in offline and in real-time cases. Comparative analysis, as a result, gives support for the ...decision in selecting the technique of choice depending on the application.
Keywords:
Trend Identification / Discrete Wavelet Transform / Singular Spectrum AnalysisSource:
International Conference on Innovative Technologies (IN-TECH 2019), Proceedings, Belgrade, september 2019, 48-51., 2019, 48-51Publisher:
- World Association for Innovative Technologies
Funding / projects:
- An innovative ecologically based approach to implementation of intelligent manufacturing systems for production of sheet metal parts (RS-MESTD-Technological Development (TD or TR)-35004)
- The development of a new generation of domestic machining systems (RS-MESTD-Technological Development (TD or TR)-35022)
Collections
Institution/Community
Mašinski fakultetTY - CONF AU - Nedeljković, Dušan AU - Kokotović, Branko AU - Jakovljević, Živana PY - 2019 UR - https://machinery.mas.bg.ac.rs/handle/123456789/5266 AB - Trend identification gives a general tendency of a signal and as such can represent the first step in signal change detection. This is often a method for extracting useful features from the measured signal that can be transferred to meaningful conclusions, which can serve as a basis for process control or predicting the future behavior of the process. Fourier analysis, owing to its simplicity, is traditionally applied in time series processing; however, it has shortcomings in the analysis of signals that are not periodic. As a result, a number of techniques with better performance in non-stationary signal analysis have been proposed. In this paper, we carry out comparative analysis of two techniques for trend identification of non-stationary signals: Discrete Wavelet Transform (DWT) and Singular Spectrum Analysis (SSA). The analysis involves examining the applicability of these techniques both in offline and in real-time cases. Comparative analysis, as a result, gives support for the decision in selecting the technique of choice depending on the application. PB - World Association for Innovative Technologies C3 - International Conference on Innovative Technologies (IN-TECH 2019), Proceedings, Belgrade, september 2019, 48-51. T1 - Comparative analysis of discrete wavelet transform and singular spectrum analysis in signal trend identification EP - 51 SP - 48 UR - https://hdl.handle.net/21.15107/rcub_machinery_5266 ER -
@conference{ author = "Nedeljković, Dušan and Kokotović, Branko and Jakovljević, Živana", year = "2019", abstract = "Trend identification gives a general tendency of a signal and as such can represent the first step in signal change detection. This is often a method for extracting useful features from the measured signal that can be transferred to meaningful conclusions, which can serve as a basis for process control or predicting the future behavior of the process. Fourier analysis, owing to its simplicity, is traditionally applied in time series processing; however, it has shortcomings in the analysis of signals that are not periodic. As a result, a number of techniques with better performance in non-stationary signal analysis have been proposed. In this paper, we carry out comparative analysis of two techniques for trend identification of non-stationary signals: Discrete Wavelet Transform (DWT) and Singular Spectrum Analysis (SSA). The analysis involves examining the applicability of these techniques both in offline and in real-time cases. Comparative analysis, as a result, gives support for the decision in selecting the technique of choice depending on the application.", publisher = "World Association for Innovative Technologies", journal = "International Conference on Innovative Technologies (IN-TECH 2019), Proceedings, Belgrade, september 2019, 48-51.", title = "Comparative analysis of discrete wavelet transform and singular spectrum analysis in signal trend identification", pages = "51-48", url = "https://hdl.handle.net/21.15107/rcub_machinery_5266" }
Nedeljković, D., Kokotović, B.,& Jakovljević, Ž.. (2019). Comparative analysis of discrete wavelet transform and singular spectrum analysis in signal trend identification. in International Conference on Innovative Technologies (IN-TECH 2019), Proceedings, Belgrade, september 2019, 48-51. World Association for Innovative Technologies., 48-51. https://hdl.handle.net/21.15107/rcub_machinery_5266
Nedeljković D, Kokotović B, Jakovljević Ž. Comparative analysis of discrete wavelet transform and singular spectrum analysis in signal trend identification. in International Conference on Innovative Technologies (IN-TECH 2019), Proceedings, Belgrade, september 2019, 48-51.. 2019;:48-51. https://hdl.handle.net/21.15107/rcub_machinery_5266 .
Nedeljković, Dušan, Kokotović, Branko, Jakovljević, Živana, "Comparative analysis of discrete wavelet transform and singular spectrum analysis in signal trend identification" in International Conference on Innovative Technologies (IN-TECH 2019), Proceedings, Belgrade, september 2019, 48-51. (2019):48-51, https://hdl.handle.net/21.15107/rcub_machinery_5266 .