Compressive sampling for accelerometer signals in structural health monitoring

In structural health monitoring (SHM) of civil structures, data compression is often needed to reduce the cost of data transfer and storage, because of the large volumes of sensor data generated from the monitoring system. The traditional framework for data compression is to first sample the full signal and, then to compress it. Recently, a new data compression method named compressive sampling (CS) that can acquire the data directly in compressed form by using special sensors has been presented. In this article, the potential of CS for data compression of vibration data is investigated using simulation of the CS sensor algorithm. For reconstruction of the signal, both wavelet and Fourier orthogonal bases are examined. The acceleration data collected from the SHM system of Shandong Binzhou Yellow River Highway Bridge is used to analyze the data compression ability of CS. For comparison, both the wavelet-based and Huffman coding methods are employed to compress the data. The results show that the values of compression ratios achieved using CS are not high, because the vibration data used in SHM of civil structures are not naturally sparse in the chosen bases

An Efficient Algorithm for Structural Reliability Based on Dichotomy Method

Monte Carlo Simulation (MCS) method is obviously a feasible and easy method for structural reliability evaluation, by which the multiple integral is replaced by sampling statistics. However, MCS is time-consuming because of its large number of simulations. To reduce the number of simulations, a structural reliability method based on dimensionality reduction and dichotomy has been presented, in the proposed method the dimensionality reduction technique is employed in grouping samples and the dichotomy method is applied to determining the partitioned limit state function (LSF). First, samples of direct MCS generated in original space are mapped to the independent standard Gaussian space and bi-dimensional space successively. Then the samples are divided into many groups according to the value of horizontal axis in the bi-dimensional space. Finally, the critical samples of each group are located by dichotomy method, and the partitioned LSF are approximated by the critical samples. With this method, the failure samples can be distinguished from whole samples by a relative little number of simulations. By several examples, the efficiency and robustness of the proposed algorithm were demonstrated, and the optimal number of the samples and the groups were respectively studied

Investigation of Compressive Sampling for Structural Vibration Data

In structural health monitoring (SHM) of civil structures, data compression is often needed for saving the cost of data transfer and storage because of the large volumes of sensor data' generated from the monitoring system. The traditional framework for data compression is to first sample the full signal, then to compress it. Recently, a new data compression method named compressive sampling (CS) has been presented, that can acquire the data directly in compressed form by using special sensors. In this paper, the potential of CS for data compression of vibration data is investigated using simulation of the CS sensor algorithm. The acceleration data collected from the SHM system of Shandong Binzhou Yellow River Highway Bridge and China National Aquatics Center are used to analyse the data compression ability of CS. For comparison, the wavelet transform based and Huffman coding methods are also employed to compress the data. The results show that CS is useful for compression of vibration data in SHM of civil structures and that CS works better for narrowband signals such as the Shandong Binzhou Yellow River Highway Bridge vibration signal than wideband signals such as the vibration signal from the National Aquatics Center. Finally, a design of analog-to-digital converter (ADC) based on CS technique (CSADC) is proposed in this paper and a simulation with analog signal is carried out to illustrate the ability of CSADC for acquiring data directly with compressed form

An improved ultrasonic computerized tomography (UCT) technique for damage localization based on compressive sampling (CS) theory

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/172332/1/stc2938.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/172332/2/stc2938_am.pd

Identification of time-varying cable tension forces based on adaptive sparse time-frequency analysis of cable vibrations

For cable bridges, the cable tension force plays a crucial role in their construction, assessment and long-term structural health monitoring. Cable tension forces vary in real time with the change of the moving vehicle loads and environmental effects, and this continual variation in tension force may cause fatigue damage of a cable. Traditional vibration-based cable tension force estimation methods can only obtain the time-averaged cable tension force and not the instantaneous force. This paper proposes a new approach to identify the time-varying cable tension forces of bridges based on an adaptive sparse time-frequency analysis method. This is a recently developed method to estimate the instantaneous frequency by looking for the sparsest time-frequency representation of the signal within the largest possible time-frequency dictionary (i.e. set of expansion functions). In the proposed approach, first, the time-varying modal frequencies are identified from acceleration measurements on the cable, then, the time-varying cable tension is obtained from the relation between this force and the identified frequencies. By considering the integer ratios of the different modal frequencies to the fundamental frequency of the cable, the proposed algorithm is further improved to increase its robustness to measurement noise. A cable experiment is implemented to illustrate the validity of the proposed method. For comparison, the Hilbert–Huang transform is also employed to identify the time-varying frequencies, which are then used to calculate the time-varying cable-tension force. The results show that the adaptive sparse time-frequency analysis method produces more accurate estimates of the time-varying cable tension forces than the Hilbert–Huang transform method

Identification of time-varying cable tension forces based on adaptive sparse time-frequency analysis of cable vibrations

For cable bridges, the cable tension force plays a crucial role in their construction, assessment and long-term structural health monitoring. Cable tension forces vary in real time with the change of the moving vehicle loads and environmental effects, and this continual variation in tension force may cause fatigue damage of a cable. Traditional vibration-based cable tension force estimation methods can only obtain the time-averaged cable tension force and not the instantaneous force. This paper proposes a new approach to identify the time-varying cable tension forces of bridges based on an adaptive sparse time-frequency analysis method. This is a recently developed method to estimate the instantaneous frequency by looking for the sparsest time-frequency representation of the signal within the largest possible time-frequency dictionary (i.e. set of expansion functions). In the proposed approach, first, the time-varying modal frequencies are identified from acceleration measurements on the cable, then, the time-varying cable tension is obtained from the relation between this force and the identified frequencies. By considering the integer ratios of the different modal frequencies to the fundamental frequency of the cable, the proposed algorithm is further improved to increase its robustness to measurement noise. A cable experiment is implemented to illustrate the validity of the proposed method. For comparison, the Hilbert–Huang transform is also employed to identify the time-varying frequencies, which are then used to calculate the time-varying cable-tension force. The results show that the adaptive sparse time-frequency analysis method produces more accurate estimates of the time-varying cable tension forces than the Hilbert–Huang transform method

The State of the Art of Data Science and Engineering in Structural Health Monitoring

Structural health monitoring (SHM) is a multi-discipline field that involves the automatic sensing of structural loads and response by means of a large number of sensors and instruments, followed by a diagnosis of the structural health based on the collected data. Because an SHM system implemented into a structure automatically senses, evaluates, and warns about structural conditions in real time, massive data are a significant feature of SHM. The techniques related to massive data are referred to as data science and engineering, and include acquisition techniques, transition techniques, management techniques, and processing and mining algorithms for massive data. This paper provides a brief review of the state of the art of data science and engineering in SHM as investigated by these authors, and covers the compressive sampling-based data-acquisition algorithm, the anomaly data diagnosis approach using a deep learning algorithm, crack identification approaches using computer vision techniques, and condition assessment approaches for bridges using machine learning algorithms. Future trends are discussed in the conclusion. Keywords: Structural health monitoring, Monitoring data, Compressive sampling, Machine learning, Deep learnin