Structural damage identification under variable environmental/operational conditions based on singular spectrum analysis and statistical control chart

While many studies have been used onboard monitoring methods to detect train wheel defects, this paper aims to extract damage features from vibrational signals obtained by the wayside monitoring system. To provide an appropriate tool for the decomposition of experimental nonstationary vibration signals containing impacts, an analytical amplitude-based signal decomposition method is provided. A two-axle motor car with a flat wheel defect is studied at different operating conditions. Vibrational signals arising from wheel-rail interactions are gathered by the Wayside Data Collection Setup (WDCS) with five piezoelectric accelerometers. The tests are performed at the constant speeds of 10, 20, 30, and 40 km/h. The acquired signals are decomposed by the Multi-channel Singular Spectrum Analysis (MSSA) into the contributing components. Based on the results, increases in the amplitudes of the second reconstructed components (RCs) of the vibrational signals sensed by all the accelerometers can be observed at a specific time instance as a repeating pattern. Similar patterns are obtained at different motor car positions relative to the measurement area, as well as different motor car speeds. To verify the consistency of the second RCs, the crest factors are obtained for all the angular positions. The results show sudden increases in the crest factor at a fixed angular position that indicate the existence of a defect at that specific angular position of the wheel.

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“…37,38 Also, one may refer to recent studies using the SSA for the structural fault detection. 31,[39][40][41][42][43] The framework of this study…”

Section: Singular Spectrum Analysis and Multichannel Singular Spectru...mentioning

confidence: 99%

Experimental detection of train wheel defects using wayside vibration signal processing

Salehi

Bagherzadeh

Fakhari

2023

Structural Health Monitoring

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“…Time series decomposition method intends to decompose the frequency sequence into a set of independent components, that is, the slow‐varying trend and periodic and noise components, among which trend‐related components were interpreted as potential environmental impacts and should be removed; the reconstructed sequence only related to the structural dynamics was composed of the remaining components. Relevant methods mainly refer to methods based on spectral decomposition like simplistic geometric spectrum analysis (SGSA) and singular spectrum analysis (SSA) with satisfactory abilities in the separation of environmental effects and damage identification 111 …”

Section: Elimination Of Modal Variability Based On Output‐only Model ...mentioning

confidence: 99%

“…Relevant methods mainly refer to methods based on spectral decomposition like simplistic geometric spectrum analysis (SGSA) and singular spectrum analysis (SSA) with satisfactory abilities in the separation of environmental effects and damage identification. 111…”

Section: Frequency Sequence Decompositionmentioning

confidence: 99%

Eliminating environmental and operational effects on structural modal frequency: A comprehensive review

Wang

Yang

et al. 2022

Structural Contr & Hlth

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Modal frequencies are widely used for vibration-based structural health monitoring (SHM) and for capturing the dynamics of a monitored structure to reveal possible failures. However, changing environmental and operational conditions (i.e., temperature, humidity, wind load, and traffic load) may submerge the modal variability induced by structural damage, thereby falsely identifying damage of interest. This paper presents a comprehensive summary review of SHM for the prediction of modal frequency and the elimination of environment-induced masking effects based on the data normalization method. The influence mechanisms of external variations on modal frequencies extensively reported in the literature are first described. Next, the research progress in predicting and eliminating the operational modal variability is reviewed emphatically; this progress can be primarily divided into an inputoutput method that focuses on the establishment of the relationship model between structural frequency and environmental conditions and an outputonly method that separates the embedded environmental variable-induced changes depending on whether the environmental measurements are measured. Finally, the conclusions and future studies are summarized and discussed. As an overview, the major contribution of this paper is to provide objective technical references for engineers and owners and to further evaluate structural safety conditions more effectively and in a timely manner.

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“…There are also methods proposed to eliminate the influence of changing environmental conditions in the case when temperature is not measured [ 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 ]. The singular value decomposition method and the auto-associative neural network method have been applied to implicitly model the underlying relationship between environmental variables and damage-sensitive features.…”

Section: Introductionmentioning

confidence: 99%

“…William Soo Lon Wah and Yining Xia [ 19 ] proposed a method to eliminate the effects of outlier measurements based on a multiple-regression model and smaller levels of damage have been identified. Yansong Diao et al [ 20 ] conducted singular spectrum analysis to transform a measured frequency sequence into several independent components, and the impact of variable environmental situations can be eliminated by re-selecting suitable components. Numerical simulation and experiment are used to validate the proposed method.…”

Section: Introductionmentioning

confidence: 99%

Temperature Effect Separation of Structure Responses from Monitoring Data Using an Adaptive Bandwidth Filter Algorithm

Hu,

Liu,

Deng

et al. 2024

Materials

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Temperature is one of the most important factors significantly affecting damage detection performance in civil engineering. A new method called the Adaptive Bandwidth Filter Algorithm (ABFA) is proposed in this paper to separate the temperature effect from quasi-static long-term structural health monitoring data. The Adaptive Bandwidth Filter Algorithm (ABFA) is referred to as an algorithm of automatically adjusting the frequency bandwidth filter via the particle swarm optimization (PSO) algorithm. Considering the obvious multi-scale feature of the collected data of civil structure, the acquired time series are divided into different time scales (for example, day, month, year, etc.), and these scales in the frequency domain correspond to the center frequencies of the adaptive bandwidth filter. The temperature effect on structure responses across different time scales is thereafter explored by adaptively adjusting the frequency bandwidth of the filter based on the known center frequencies of different scales. The relationship between the temperature and the structure responses is established through statistical regression facilitated by sufficient in situ monitoring data. Simulation and experiment results show the very promising performance of the proposed algorithm and decouple the temperature effect accurately from the contaminated data; thus an enhanced capability of damage detection is achieved.

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Structural damage identification under variable environmental/operational conditions based on singular spectrum analysis and statistical control chart

Cited by 10 publications

References 32 publications

Experimental detection of train wheel defects using wayside vibration signal processing

Experimental detection of train wheel defects using wayside vibration signal processing

Eliminating environmental and operational effects on structural modal frequency: A comprehensive review

Temperature Effect Separation of Structure Responses from Monitoring Data Using an Adaptive Bandwidth Filter Algorithm

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