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

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1825
Author(s):  
Marco Civera ◽  
Cecilia Surace
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Real Life ◽  
Adaptive Methods ◽  
Decomposition Methods ◽  
Signal Decomposition ◽  
Time Frequency ◽  
Structural Health ◽  
Mode Decomposition ◽  
Depth Analysis

Signal Processing is, arguably, the fundamental enabling technology for vibration-based Structural Health Monitoring (SHM), which includes damage detection and more advanced tasks. However, the investigation of real-life vibration measurements is quite compelling. For a better understanding of its dynamic behaviour, a multi-degree-of-freedom system should be efficiently decomposed into its independent components. However, the target structure may be affected by (damage-related or not) nonlinearities, which appear as noise-like distortions in its vibrational response. This response can be nonstationary as well and thus requires a time-frequency analysis. Adaptive mode decomposition methods are the most apt strategy under these circumstances. Here, a shortlist of three well-established algorithms has been selected for an in-depth analysis. These signal decomposition approaches—namely, the Empirical Mode Decomposition (EMD), the Hilbert Vibration Decomposition (HVD), and the Variational Mode Decomposition (VMD)—are deemed to be the most representative ones because of their extensive use and favourable reception from the research community. The main aspects and properties of these data-adaptive methods, as well as their advantages, limitations, and drawbacks, are discussed and compared. Then, the potentialities of the three algorithms are assessed firstly on a numerical case study and then on a well-known experimental benchmark, including nonlinear cases and nonstationary signals.

Time–frequency and time–scale analyses for structural health monitoring

2006 ◽  
Vol 365 (1851) ◽  
pp. 449-477 ◽  
Author(s):  
Wiesław J Staszewski ◽  
Amy N Robertson
Keyword(s):  
Signal Processing ◽  
Structural Health Monitoring ◽  
Damage Detection ◽  
Health Monitoring ◽  
Time Scale ◽  
Time Frequency ◽  
Structural Health ◽  
Variant Analysis

Signal processing is one of the most important elements of structural health monitoring. This paper documents applications of time-variant analysis for damage detection. Two main approaches, the time–frequency and the time–scale analyses are discussed. The discussion is illustrated by application examples relevant to damage detection.

scholarly journals Time-Frequency Methods for Structural Health Monitoring

Sensors ◽  
2014 ◽  
Vol 14 (3) ◽  
pp. 5147-5173 ◽  
Author(s):  
Alexander Pyayt ◽  
Alexey Kozionov ◽  
Ilya Mokhov ◽  
Bernhard Lang ◽  
Robert Meijer ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Time Frequency ◽  
Structural Health

Application of Local Wave Method in the Structural Health Monitoring Signal Decomposition

2013 ◽  
Vol 457-458 ◽  
pp. 969-973
Author(s):  
Lin Yang
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Response ◽  
Signal Decomposition ◽  
Experimental Simulation ◽  
Wave Method ◽  
Structural Health ◽  
Bridge Health Monitoring ◽  
Local Wave ◽  
Nonlinear Signal

Health monitoring of the bridge structure has gradually become one of the hot topics. The signal decomposition technology is the key technique of the bridge structural health monitoring. The traditional data analysis and processing methods, which can only be applied to stationary or linear signal processing, have significant limitations. However, the structural response signals tested are mostly non-stationary and nonlinear. So methods that can effectively analyze non-stationary and nonlinear signal are urgently needed. Based on the summarization and analysis of the shortage of wavelet analysis method, the application of local wave method for data processing and analysis in structural health monitoring is put forward. The feasibility and superiority of local wave method is discussed. Experimental simulation results show that the application of local wave method in bridge health monitoring signal decomposition is feasible.

scholarly journals Online frequency and amplitude tracking in structural vibrations under environment using APES spectrum postprocessing and Kalman filtering

2021 ◽  
Author(s):  
Xuewen Yu ◽  
Danhui Dan
Keyword(s):  
Structural Health Monitoring ◽  
Kalman Filtering ◽  
Health Monitoring ◽  
Time Window ◽  
Numerical Study ◽  
Amplitude Spectrum ◽  
Real Life ◽  
Structural Vibrations ◽  
Vibration Signals ◽  
Structural Health

Identifying time-varying frequency and amplitude online in real-life structural vibrations is an essential topic of data processing in structural health monitoring. This paper proposes a novel method for this task. We assume that structural vibration signals are stationary in a short time, thus a spectral analysis method called amplitude and phase estimation (APES) is conducted to obtain the amplitude spectrum at corresponding time window, and a postprocessing technique is proposed to extract the modal frequency and amplitude from the spectrum automatically. The extracted frequency and amplitude could be regarded as the average of the instantaneous frequency and instantaneous amplitude during the window. Due to the instability of measured structural vibrations and the uncertainty of spectral shapes under ambient excitation, Kalman ?filtering is introduced by taking the signal that reconstructed from the identi?fied frequencies and amplitudes as the prediction to enhance the reliability and quality (signal-to-noise ratio) of the next measured signals. Numerical study is performed to inspect the performance of the proposed method. It is also employed to analyze the vibration signals of actual structures, i.e., a cable of a cable-stayed bridge, a hanger of an arch bridge and the main girder of a suspension bridge. The results show its potential to track frequency and amplitude in structural vibrations under environmental measurements. The method is supposed to provide fundamental support for further information obtaining and high-level decision making for structural health monitoring systems.

APPLICATION OF EMPIRICAL MODE DECOMPOSITION IN STRUCTURAL HEALTH MONITORING: SOME EXPERIENCE

2009 ◽  
Vol 01 (04) ◽  
pp. 601-621 ◽  
Author(s):  
JUN CHEN
Keyword(s):  
Structural Health Monitoring ◽  
Data Processing ◽  
Parameter Identification ◽  
Empirical Mode Decomposition ◽  
Health Monitoring ◽  
Structural Parameter ◽  
Raw Data ◽  
Structural Health ◽  
Long Span ◽  
Mode Decomposition

The installation of long-term structural health monitoring (SHM) system on super-tall buildings, long span bridges and large space structures has become a worldwide trend since last decade to monitor loading conditions, to detect damage, to assess structural safety and to guide maintenance during their service life. The core part of an SHM system is the function of data processing and structural parameter/damage identification that extracts useful information from huge amount of raw data and provides reliable knowledge for proper decision. Recently emerged data processing technique empirical mode decomposition (EMD) in conjunction with Hilbert transform (HT) provides a more better and powerful tool for SHM. This paper summarizes some research experience gained from application of EMD + HT in SHM with focuses on pre-processing raw data, structural parameter identification and damage detection. In particular, EMD is applied to determining time varying mean wind speed for wind data and to extract multipath effect from GPS data. For structural parameter identification, the EMD + HT approach is employed to identify natural frequencies and modal damping ratios of long span bridge during passage of strong typhoon and of structures with closely spaced modes of vibration. The results manifest the advantages of EMD + HT over traditional FFT-based methods in damping estimation. Furthermore, experimental investigation has been carried out to study the applicability of EMD for identifying structural damage caused by a sudden change of structural stiffness. It is concluded from all these investigations that EMD approach is a promising tool for structural health monitoring of large civil structures. Finally, some issues concerned for further practical application of EMD are highlighted and discussed based on these academic researches.

scholarly journals A Numerical Study on Computational Time Reversal for Structural Health Monitoring

Signals ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 225-244
Author(s):  
Christos G. Panagiotopoulos ◽  
Georgios E. Stavroulakis
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Time Reversal ◽  
Numerical Study ◽  
Real Life ◽  
Computational Time ◽  
External Loading ◽  
Sensors And Actuators ◽  
Time Reversibility ◽  
Structural Health

Structural health monitoring problems are studied numerically with the time reversal method (TR). The dynamic output of the structure is applied, time reversed, as an external loading and its propagation within the deformable medium is followed backwards in time. Unknown loading sources or damages can be discovered by means of this method, focused by the reversed signal. The method is theoretically justified by the time-reversibility of the wave equation. Damage identification problems relevant to structural health monitoring for truss and frame structures are studied here. Beam structures are used for the demonstration of the concept, by means of numerical experiments. The influence of the signal-to-noise ratio (SNR) on the results was investigated, since this quantity influences the applicability of the method in real-life cases. The method is promising, in view of the increasing availability of distributed intelligent sensors and actuators.

scholarly journals Ultrasonic Guided-Waves Sensors and Integrated Structural Health Monitoring Systems for Impact Detection and Localization: A Review

2021 ◽  
Author(s):  
Lorenzo Capineri ◽  
Andrea Bulletti
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Wireless Sensors ◽  
Real Life ◽  
Research Field ◽  
Time Signal ◽  
Signal Acquisition ◽  
Structural Health ◽  
Impact Detection ◽  
The Impact

In the last decade the research concerning Structural Health Monitoring (SHM) systems have continuously investigated toward autonomous systems based on sensor networks. The different functional blocks of these systems are described introducing first the basic concepts for the impact detection applications based on piezoelectric sensors for ultrasonic guided Lamb waves generated into planar structures. Then the paper will review the recent progresses of the research with focus on the integration of sensors with the electronic interface, including the embedding of sensors with the structure that is represented by the smart-skin concept. The latter benefits of the advancement in piezoelectric MEMS sensors with small footprint mounted on flexible substrates. This new layout of sensors is essential for the system design based on a network of sensors nodes with real time signal acquisition capability for impact event capture. The options of a wired or wireless sensors network are also discussed for different dimensions of the monitored structure. The multifunctional sensors capability is also a new feature discussed in the paper for sensing the environmental conditions that affect the Lamb wave signals interpretation. The power supply by environmental energy of an autonomous sensor node is another research field where large innovation is occurred and a review of energy harvesting devices is reported. The embedded signal processing capabilities in a node with IoT based wireless sensors networks, is an important fertilization between different disciplines and examples of SHM system tested in real-life application are discussed. Finally, the large capacity of data transfer of sensors networks toward large storage data archives also with low power WiFi protocols is the new frontier for exploring artificial intelligence and machine learning applied to big data and the recent research outcomes for impact detection and characterization in complex structures are reported.

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