Singular Spectrum Analysis of Biomedical Signals

Sanei, Saeid; Hassani, Hossein

doi:10.1201/b19140

Cited by 96 publications

(128 citation statements)

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“…The mathematical procedures which we specifically seek to link with nature consist of Singular Value Decomposition (SVD) based methods and signal subspace (SS) methods which form the basis of a general class of subspace-based noise reduction algorithms. The superior performance of this class of algorithms in noise reduction and forecasting has been proved by several studies [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

From nature to maths: Improving forecasting performance in subspace-based methods using genetics Colonial Theory

Hassani

Ghodsi

Silva

et al. 2016

Digital Signal Processing

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Many scientific fields consider accurate and reliable forecasting methods as important decision-making tools in the modern age amidst increasing volatility and uncertainty. As such there exists an opportune demand for theoretical developments which can result in more accurate forecasts. Inspired by Colonial Theory, this paper seeks to bring about considerable improvements to the field of time series analysis and forecasting by identifying certain core characteristics of Colonial Theory which are subsequently exploited in introducing a novel approach for the grouping step of subspace based methods. The proposed algorithm shows promising results in terms of improved performances in noise filtering and forecasting of time series. The reliability and validity of the proposed algorithm is evaluated and compared with popular forecasting models with the results being thoroughly evaluated for statistical significance and thereby adding more confidence and value to the findings of this research.

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Section: Introductionmentioning

confidence: 99%

From nature to maths: Improving forecasting performance in subspace-based methods using genetics Colonial Theory

Hassani

Ghodsi

Silva

et al. 2016

Digital Signal Processing

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“…SSA is a time series analysis method that popularly used in biomedical and meteorological sciences [12][13][14]. Recently, it was used for the purposes of engineering application such as fault diagnosis of rolling element bearings [15][16][17][18][19], tool wear health monitoring [20,21] and delamination in composite materials [22].…”

Section: Methodsmentioning

confidence: 99%

Vibration-based damage detection of structural joints in presence of uncertainty

2018

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Abstract. Early damage detection of structure's joints is essential in order to ensure the integrity of structures. Vibration-based methods are the most popular way of diagnosing damage in machinery joints. Any technique that is used for such a purpose requires dealing with the variability inherent to the system due to manufacturing tolerances, environmental conditions or aging. The level of variability in vibrational response can be very high for mass-produced complex structures that possess a large number of components. In this study, a simple and efficient time frequency method is proposed for detection of damage in connecting joints. The method suggests using singular spectrum analysis for building a reference space from the signals measured on a healthy structure and then compares all other signals to that reference space in order to detect the presence of faults. A model of two plates connected by a series of mounts is used to examine the effectiveness of the method where the uncertainty in the mount properties is taken into account to model the variability in the built-up structure. The motivation behind the simplified model is to identify the faulty mounts in trim-structure joints of an automotive vehicle where a large number of simple plastic clips are used to connect the trims to the vehicle structure.

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“…In general, SSA is a signal analysis method used for climatic and forecasting data analysis [15,16] and biomedical signal analysis [17,18] . It is also used as an anomaly detection method in tool wear health monitoring [19,20] and for damage assessment in wind turbine blades [21] , but it is still unpopular for fault detection in rolling element bearings.…”

Section: Singular Spectrum Analysis | Featurementioning

confidence: 99%

A new methodology for fault detection in rolling element bearings using singular spectrum analysis

Al-Bugharbee¹,

Trendafilova²

2017

int j cond monitor

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Singular Spectrum Analysis of Biomedical Signals

Cited by 96 publications

References 0 publications

From nature to maths: Improving forecasting performance in subspace-based methods using genetics Colonial Theory

From nature to maths: Improving forecasting performance in subspace-based methods using genetics Colonial Theory

Vibration-based damage detection of structural joints in presence of uncertainty

A new methodology for fault detection in rolling element bearings using singular spectrum analysis

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