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This study aims to extract the natural frequencies and mode shapes of washing machines during operation using only vibration responses. To ensure the quality and detect defects, selected mass-produced washing machines are evaluated based on their vibrational characteristics. This process involves conducting modal experiments in a stationary state and operating the products under various extreme conditions for prolonged periods to assess their condition. If the vibration characteristics and modal parameters of the structure can be concurrently verified during operation, it could significantly save time and cost. Therefore, this research utilizes Operational Modal Analysis (OMA) techniques to identify the structural modal parameters through vibration responses alone. Specifically, the operation of washing machines not only involves primary operating frequencies but also slight frequency variations during operation. These manifest in the frequency domain as the primary operating frequency, its harmonics, and sidebands. These components interfere with the application of conventional OMA techniques, necessitating their removal. For this purpose, cepstral techniques were applied as a preprocessing step before applying OMA techniques in this study, and the employed cepstral techniques were evaluated through the extracted modal parameter results.

Visual representation learning is ubiquitous in various real-world applications, including visual comprehension, video understanding, multi-modal analysis, human-computer interaction, and urban computing. Due to the emergence of huge amounts of multimodal heterogeneous spatial/temporal/spatial-temporal data in the big data era, the lack of interpretability, robustness, and out-of-distribution generalization are becoming the challenges of the existing visual models. The majority of the existing methods tend to fit the original data/variable distributions and ignore the essential causal relations behind the multi-modal knowledge, which lacks unified guidance and analysis about why modern visual representation learning methods easily collapse into data bias and have limited generalization and cognitive abilities. Inspired by the strong inference ability of human-level agents, recent years have therefore witnessed great effort in developing causal reasoning paradigms to realize robust representation and model learning with good cognitive ability. In this paper, we conduct a comprehensive review of existing causal reasoning methods for visual representation learning, covering fundamental theories, models, and datasets. The limitations of current methods and datasets are also discussed. Moreover, we propose some prospective challenges, opportunities, and future research directions for benchmarking causal reasoning algorithms in visual representation learning. This paper aims to provide a comprehensive overview of this emerging field, attract attention, encourage discussions, bring to the forefront the urgency of developing novel causal reasoning methods, publicly available benchmarks, and consensus-building standards for reliable visual representation learning and related real-world applications more efficiently.

The present paper discusses a new approach for the experimental determination of modal parameters (resonant frequencies, modal shapes and damping coefficients) based on measured displacement values, using the non-contact optical method of digital image correlation (DIC). The output is a newly developed application module that, based on a three-dimensional displacement matrix from the experimental measurement results, can construct a frequency response function (FRF) for the purpose of experimental and operational modal analysis. From this frequency response function, the modal parameters of interest are able to be determined. The application module has been designed for practical use in Scilab 6.1.0, and its code interfaces directly with the ISTRA4D high-speed camera software. The module was built on measurements of a steel plate excited by an impact hammer to simulate experimental modal analysis. Verification of the correctness of the computational algorithm or the obtained modal parameters of the excited sheet metal plate was performed by simulation in the numerical software Abaqus, whose modal shapes and resonant frequencies showed high agreement with the results of the newly developed application.

In vitro diagnosis (IVD) is one vital component of medical tests that detects biological samples of tissues or bio‐fluids. Recently, mass spectrometry and spectroscopy have been increasingly employed in the field of IVD, due to their high accuracy, facile sample preparation, and rapid detection. Notably, the large datasets generated by these two technology methods provide a wealth of information but subsequently involve complex and time‐consuming processing works. Machine learning (ML), an important branch of artificial intelligence (AI), has emerged as a promising solution for the decoding of big data. ML imitates the human brain to process data, significantly improving accuracy and efficiency compared with traditional processing methods. In this review, we first introduce the commonly used ML algorithms and advanced mass spectrometry and spectroscopy techniques in the field of IVD, respectively. The ML algorithms are summarized as four aspects according to different learning tasks. Then, the combinations of ML with mass spectrometry, spectroscopy, and multi‐modal analysis for IVD are presented, and the roles of ML in these combinations are elucidated by some representative examples. This review aims to provide a systematic and comprehensive summary of the literature on ML‐assisted mass spectrometry or spectroscopy. We believe that it will facilitate researchers to select suitable ML algorithms for supplementing existing detection techniques or to develop the potential of coupling more detection techniques with ML, thus promoting the development of mass spectrometry and spectroscopy in IVD. Recently, mass spectrometric and spectroscopic methods coupled with machine learning have been increasingly employed in the field of in vitro diagnoses, such as pathogen identification, cancer diagnosis, and cell classification. In this review, the authors focus on the combinations of machine learning with mass spectrometry, spectroscopy, and multi‐modal analysis for in vitro diagnoses, and they highlight the roles of machine learning in these combinations through some representative examples. The authors furthermore discuss the challenges and perspectives of mass spectrometry, spectroscopy, multi‐modal analysis, and machine learning.

The accuracy of modal parameter estimates from estimation methods built in the framework of experimental modal analysis (EMA) can be assessed by several methods provided the information of input loadings and output responses. However, a deeper investigation is necessary for estimation methods built in the operational modal analysis (OMA) framework to establish confidence boundaries for the modal parameter estimates. This paper aims to estimate uncertainties of the modal parameter estimates by testing the sensitivity of the damped natural frequencies, damping ratios, and mode shapes to the choice of the estimation method and data acquisition settings used in this method. The uncertainty estimates of the modal parameter estimates are tested for changes in the boundary conditions. The tests provide insights into tendencies for the accuracy of the modal parameter estimates when the boundary conditions provide a different set of “true” modal parameter values. A simple cantilever beam experimental setup is used to obtain benchmark modal parameter estimates from EMA that are used to estimate the accuracy of the modal parameter estimates from OMA. The method’s robustness in estimating uncertainties and accuracies for modal parameter estimates is tested for several independent experiments. The boundary conditions for the cantilever beam are changed to test the influence of altering modal parameter estimates on the estimated uncertainties and accuracies. The boundary conditions are changed using materials with different elastic properties and the connection pressure between the clamping device and beam. A method presented in Paulsen et al. (Proceedings of the 9th International Operational Modal Analysis Conference, IOMAC, 2022) calculates a representative modal parameter estimate from a set of modal parameter estimates based on weightings obtained from the modal assurance criterion. The method’s ability to reduce uncertainties and increase accuracies of the representative estimate is demonstrated by repeating a test setup ten times. The results present simultaneously a reduction of uncertainties and an increase of accuracies for the representative modal parameter when the weighting method is used. An evaluation of dependencies between the estimated uncertainties and accuracies finds a proportional relation for the damped natural frequency when the uncertainties are described as a relative standard deviation. The analysis presents higher accuracy for the damped natural frequency and mode shape estimates compared to the damping factor, and the accuracy of the damping factor is dependent on the actual value of the damping factor. These phenomena for accuracy are well known and indicate that the uncertainties of modal parameter estimates can be assessed through a sensitivity test of estimation methods and data acquisition parameters.

Structural health monitoring systems have been employed throughout history to assess the structural responses of bridges to both natural and man-made hazards. Continuous monitoring of the integrity and analysis of the dynamic characteristics of bridges offers a solution to the limitations of visual inspection approaches and is of paramount importance for ensuring long-term safety. This review article provides a thorough, straightforward examination of the complete process for performing operational modal analysis on bridges, covering everything from data collection and preprocessing to the application of numerous modal identification techniques in both the time and frequency domains. It also incorporates advanced methods to address and overcome challenges encountered in previous approaches. The paper is distinguished by its thorough examination of various methodologies, highlighting their specific advantages and disadvantages, and providing concrete illustrations of their implementation in practical settings.

This study proposes to carry out the experimental modal analysis of nonlinear systems under the assumption of almost invariant modal shapes by coupling video analysis from a high speed/resolution camera and extended Kalman filtering. A clamped-clamped beam with a local nonlinearity is considered, and its vibrations are measured by detecting and tracking a large set of (virtual) sensors bonded to the beam outer surface. Specific image processing and video tracking techniques are employed and detailed herein. Then, the instantaneous natural frequencies and modal amplitudes are identified by means of a data assimilation method based on extended Kalman and modal filters. Finally, the proposed method of identification is assessed using a numerical example possessing 3 degrees of freedom and a strong nonlinearity. The performance and limits of the identification process are discussed.

Despite significant advances in the development of high-resolution digital cameras in the last couple of decades, their potential remains largely unexplored in the context of input-output modal identification. However, these remote sensors could greatly improve the efficacy of experimental dynamic characterisation of civil engineering structures. To this end, this study provides early evidence of the applicability of camera-based vibration monitoring systems in classical experimental modal analysis using an electromechanical shaker. A pseudo-random and sine chirp excitation is applied to a scaled model of a cable-stayed bridge at varying levels of intensity. The performance of vibration monitoring systems, consisting of a consumer-grade digital camera and two image processing algorithms, is analysed relative to that of a system based on accelerometry. A full set of modal parameters is considered in this process, including modal frequency, damping, mass and mode shapes. It is shown that the camera-based vibration monitoring systems can provide high accuracy results, although their effective application requires consideration of a number of issues related to the sensitivity, nature of the excitation force, and signal and image processing. Based on these findings, suggestions for best practice are provided to aid in the implementation of camera-based vibration monitoring systems in experimental modal analysis.

Summary In the present study, output‐only modal dynamic identification and monitoring of building properties is attempted successfully by processing real earthquake‐induced structural response signals. This is achieved through an enhanced version of a recently‐developed refined Frequency Domain Decomposition (rFDD) approach, which in the earlier implementation was adopted to analyse synthetic seismic response signals only. Despite that short duration, nonstationary seismic response data and heavy structural damping shall not fulfil traditional Operational Modal Analysis assumptions, the present rFDD response‐only algorithm allows for the effective estimation of strong‐motion natural frequencies, mode shapes, and modal damping ratios, with real seismic response signals. The present rFDD enhancement derives from a preprocessing time‐frequency analysis and from an integrated approach for Power Spectral Density matrix computation, which constitute crucial innovative issues for the treatment of real earthquake response data. A monitoring case study is analysed by taking the real strong‐motion response records from a seven‐storey reinforced concrete building in Van Nuys, California, from 1987 to the latest 2014 events (Center of Engineering Strong Motion Data database), as recorded before, during and after the 1994 Northridge earthquake, which severely damaged the building (then retrofitted). This paper proves the effectiveness of the proposed enhanced rFDD algorithm as a robust method for monitoring current structural modal properties under real earthquake excitations. This shall allow for identifying possible variations of structural features along experienced seismic histories, providing then a fundamental tool towards Earthquake Engineering and Structural Health Monitoring purposes.