A Comparative Analysis of Signal Decomposition Techniques for Structural Health Monitoring on an Experimental Benchmark

Civera, Marco; Surace, Cecilia

doi:10.3390/s21051825

Cited by 67 publications

(30 citation statements)

References 88 publications

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“…Among the variety of methods to decompose time series signals to extract and study variability are Discrete Wavelet Transform (DWT), Hilbert Vibration Decomposition (HVD) [9], Wavelet packet decomposition (WPD) [10], Empirical Mode Decomposition (EMD), and Variational Mode Decomposition (VMD). Among the mentioned methods, DWT, EMD, and VMD are widely used in non-stationary time series analysis.…”

Section: B Extracting Variability From Buildings' Load Profilesmentioning

confidence: 99%

A Case Study to Quantify Variability in Building Load Profiles

et al. 2021

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Recent technology development and penetration of advanced metering infrastructure (AMI), advanced building control systems, and the internet-of-things (IoT) in the built environment are providing detailed information on building operation, performance, and user's comfort and behavior. Building owners can obtain a wide range of energy consumption details at various levels of time granularity to augment their decisions as they manage the building operation and interact with the grid. AMI data are providing a new level of detail and visibility that may enhance building services and assets in the smart grid domain and make buildings inch closer to becoming a grid-interactive energy efficient buildings (GEB). While utility-installed AMI typically records energy consumption at a 15, 30, or 60-minute resolution, building-owner-installed metering can record energy consumption at one-minute or sub-minute time scales, providing information about how much the energy consumption varies from one sub-minute to the other (i.e. variability) at a finer time resolutions than typically available from AMI. This paper examines one-minute building load profile data sets and presents a framework to study, define, extract, quantify and analyze variability in buildings' load profiles. The discussion of variability and its analysis is based on a case study of an actual sub-minute time-resolution data set, collected in 2019, for two buildings in a Midwest state in the USA. The result shows that for the case studies, the level of variability in an end-use category is not simply proportional to its consumption. Furthermore, distinct and predictable daily variability patterns emerge in end-use load categories. This information is useful for a host of applications including prediction, forecasting, and modeling.

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Section: B Extracting Variability From Buildings' Load Profilesmentioning

confidence: 99%

A Case Study to Quantify Variability in Building Load Profiles

et al. 2021

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“…Electronics 2021, 10, x FOR PEER REVIEW 2 of 16 EMD, variational mode decomposition (VMD) and Hilbert vibration decomposition (HVD) [21] are well-known mode decomposition techniques. Considering that VMD and HVD need to add more parameters, we chose a simpler and faster EMD as the preprocessing algorithm for multi-frequency signals.…”

Section: Classical Sr and Output Saturationmentioning

confidence: 99%

Adaptive Unsaturated Bistable Stochastic Resonance Multi-Frequency Signals Detection Based on Preprocessing

et al. 2021

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Stochastic resonance (SR) has been widely used for extracting single-frequency weak periodic signals. For multi-frequency weak signals, empirical mode decomposition (EMD) can adaptively decompose the complex signal, but this method also suffers from mode mixing, which affects the accuracy of detection. SR can convert part of the noise energy into signal energy, which compensates for the defects of EMD. According to the advantages of SR and EMD, we constructed a multi-frequency signals detection method using adaptive unsaturated bistable SR based on EMD (EMD-AUBSR). In this study, we avoid the inherent saturation of SR by reconstructing the potential function and improve the multi-frequency signals detection ability by adding the preprocessing element. For strong background noise, the experimental results show that this proposed can effectively detect multi-frequency weak signals and decrease signal aliasing, whereas EMD alone cannot.

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“…The other group includes empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD) and multivariate empirical mode decomposition (MEMD), etc. [10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Modal Parameters Identification of Bridge Structures from GNSS Data Using the Improved Empirical Wavelet Transform

Fang

Meng

2021

Remote Sensing

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It is difficult to accurately identify the dynamic deformation of bridges from Global Navigation Satellite System (GNSS) due to the influence of the multipath effect and random errors, etc. To solve this problem, an improved empirical wavelet transform (EWT)-based procedure was proposed to denoise GNSS data and identify the modal parameters of bridge structures. Firstly, the Yule–Walker algorithm-based auto-power spectrum and Fourier spectrum were jointly adopted to segment the frequency bands of structural dynamic response data. Secondly, the improved EWT algorithm was used to decompose and reconstruct the dynamic response data according to a correlation coefficient-based criterion. Finally, Natural Excitation Technique (NExT) and Hilbert Transform (HT) were applied to identify the modal parameters of structures from the decomposed efficient components. Two groups of simulation data were used to validate the feasibility and reliability of the proposed method, which consisted of the vibration responses of a four-storey steel frame model, and the acceleration response data of a suspension bridge. Moreover, field experiments were carried out on the Wilford suspension bridge in Nottingham, UK, with GNSS and an accelerometer. The fundamental frequency (1.6707 Hz), the damping ratio (0.82%), as well as the maximum dynamic displacements (10.10 mm) of the Wilford suspension bridge were detected by using this proposed method from the GNSS measurements, which were consistent with the accelerometer results. In conclusion, the analysis revealed that the improved EWT-based method was capable of accurately identifying the low-order, closely spaced modal parameters of bridge structures under operational conditions.

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A Comparative Analysis of Signal Decomposition Techniques for Structural Health Monitoring on an Experimental Benchmark

Cited by 67 publications

References 88 publications

A Case Study to Quantify Variability in Building Load Profiles

A Case Study to Quantify Variability in Building Load Profiles

Adaptive Unsaturated Bistable Stochastic Resonance Multi-Frequency Signals Detection Based on Preprocessing

Modal Parameters Identification of Bridge Structures from GNSS Data Using the Improved Empirical Wavelet Transform

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