Characterization of gradually evolving structural deterioration in jack arch bridges using support vector machine

This paper presents research work that aims at developing a robust method for condition assessment of real-life concrete structures for the detection of small cracks at an early stage of development. A method is presented that utilises an unsupervised one-class support vector machine (SVM).Measured acceleration data from the current state of a structure are used as input parameter. The first singular value of the measured response data is utilized for training and testing of new data sets. Two damage identification approaches are demonstrated, one implementing the SVM for each measurement sensor separately, and another one implementing the SVM for all sensors combined. The use of one-class SVM is well suited for the condition assessment in structural health monitoring since they can detect the advancement of cracks by assigning progressively negative decision values. The presented method is based on unsupervised and non-model-based approaches, and hence there is no need for any representative numerical/finite element model of the structure to be created. To demonstrate the feasibility of the method in the detection and assessment of gradually evolving deterioration, it is tested on a replicate structure of a concrete jack arch which is a main structural component on the Sydney Harbor Bridge – one of Australia’s iconic structures. The test structure is a concrete cantilever beam with an arch section which is located on the eastern side of the bridge underneath the bus lane. A cut is introduced to the structure using a saw and its length is progressively increased in four stages while the depth is kept constant; these four damage cases correspond to less than 0.5% reduction in the first three vibrational modes of the structure which is considered a very small damage. It is demonstrated that the presented method can reliably detect the progression of the crack in the structure and thus can enable the real-time monitoring of infrastructures.

Recent failures of bridges worldwide highlight the need for robust structural health surveillance systems to provide alerts for early damage and prevent severe loss. Structural health monitoring (SHM) continues to evolve and gain wide adoption among asset managers to mitigate catastrophic failure risks of structural systems. With the prolific integration of SHM with artificial intelligence and internet of things systems becoming prevalent, several data-driven approaches have gained popularity in SHM. However, it is challenging to obtain ground-truth-labelled damage samples for supervised machine learning methods. A class of shallow machine learning methods known as unsupervised methods (e.g. the one-class support vector machine (OCSVM)), which only uses unlabelled data from a healthy state, has been shown to achieve promising results. However, shallow machine learning methods require laborious expert-crafted feature inputs for optimal performance. Inspired by the automatic feature-extraction ability of deep neural networks, unsupervised methods (deep learning models trained without supervisory ground truth labels) such as autoencoder (AE)-based methods were explored to detect structural damage. Results obtained on both real-world data from Sydney Harbour Bridge, one of Australia's iconic structures, and data collected from laboratory specimens demonstrate the effectiveness of the AE-based method over conventional shallow methods such as the OCSVM.

Structural health monitoring has been increasingly used due to the advances in sensing technology and data analysis, facilitating the shift from time-based to condition-based maintenance. This work is part of the efforts which have applied structural health monitoring to the Sydney Harbour Bridge – one of Australia’s iconic structures. It combines dimensionality reduction and pattern recognition techniques to accurately and efficiently distinguish faulty components from well-functioning ones. Specifically, random projection is used for dimensionality reduction on the vibration feature data. Then, healthy and damaged patterns of bridge components are learned in the lower dimensional projected space using supervised and unsupervised machine learning methods, namely, support vector machine and one-class support vector machine. The experimental results using data from a laboratory-based building structure and the Sydney Harbour Bridge showed high feasibility of applying machine learning techniques to dimensionality reduction and damage detection in structural health monitoring. Random projection combined with support vector machine significantly reduces the computational time while maintaining the detection accuracy. The proposed method also outperformed popular dimensionality reduction techniques. The computational time of the method using random projection can be more than 200 times faster than that without using dimensionality reduction while still achieving similar detection accuracy.

Machine learning algorithms have been employed extensively in the area of structural health monitoring to compare new measurements with baselines to detect any structural change. One-class support vector machine (OCSVM) with Gaussian kernel function is a promising machine learning method which can learn only from one class data and then classify any new query samples. However, generalization performance of OCSVM is profoundly influenced by its Gaussian model parameter \(\sigma \). This paper proposes a new algorithm named Appropriate Distance to the Enclosing Surface (ADES) for tuning the Gaussian model parameter. The semantic idea of this algorithm is based on inspecting the spatial locations of the edge and interior samples, and their distances to the enclosing surface of OCSVM. The algorithm selects the optimal value of \(\sigma \) which generates a hyperplane that is maximally distant from the interior samples but close to the edge samples. The sets of interior and edge samples are identified using a hard margin linear support vector machine. The algorithm was successfully validated using sensing data collected from the Sydney Harbour Bridge, in addition to five public datasets. The designed ADES algorithm is an appropriate choice to identify the optimal value of \(\sigma \) for OCSVM especially in high dimensional datasets.

Huberized one-class support vector machine with truncated loss function in the primal space

The traditional template matching strategy of optical and synthetic aperture radar (SAR) is sensitive to the nonlinear transformation between two images. In some cases, the optical and SAR image pairs do not conform to the affine transformation condition. To address this issue, this study presents a novel template matching strategy which uses the One-Class Support Vector Machine (SVM) to remove outliers. First, we propose a method to construct the similarity map dataset using the SEN1-2 dataset for training the One-Class SVM. Second, a four-step strategy for optical and SAR image registration is presented in this paper. In the first step, the optical image is divided into some grids. In the second step, the strongest Harris response point is selected as the feature point in each grid. In the third step, we use Gaussian pyramid features of oriented gradients (GPOG) descriptor to calculate the similarity map in the search region. The trained One-Class SVM is used to remove outliers through similarity maps in the fourth step. Furthermore, the number of improve matches (NIM) and the rate of improve matches (RIM) are designed to measure the effect of image registration. Finally, this paper designs two experiments to prove that the proposed strategy can correctly select the matching points through similarity maps. The experimental results of the One-Class SVM in dataset show that the One-Class SVM can select the correct points in different datasets. The image registration results obtained on the second experiment show that the proposed strategy is robust to the nonlinear transformation between optical and SAR images.

In this paper, we propose a method by engaging the One Class Support Vector Machine (OC-SVM) in the identification of Diffractive Optically Variable Images (DOVIs). OC-SVM, as a special SVM, can solve the problems of high-dimensional data sets and small sample size (SSS) with positive and negative unbalance training data. Image feature matrix is built by extracting image features from texture aspects. OC-SVM can be trained with the high-dimensional matrix directly, and does not have to reduce the dimensionality of feature matrix as the usual methods. The experiment results show the effectiveness of the proposed approach against Linear Discriminant Analysis. Considering time cost and correct classification rate, OC-SVM is suitable for the identification of DOVIs.

Structural Health Monitoring (SHM) is a condition-based maintenance technology using sensing systems. In SHM, the use of domain knowledge is essential: it motivates the use of machine learning approaches; it can be used to extract damage sensitive features and interpret the results by machine learning. This work focuses on two SHM problems: damage identification and substructure clustering. Our solutions to address them are based on machine learning techniques and robust feature extraction using domain knowledge. In the first problem, damage sensitive features were extracted using a frequency domain decomposition, followed by a robust one-class support vector machine for damage detection. In the second problem, a novel clustering technique and spectral moment feature were utilised for substructure grouping and anomaly detection. These methods were evaluated using data from lab-based structures and data collected from the Sydney Harbour Bridge. We obtained high damage detection accuracies and were able to assess damage severity. Furthermore, the clustering technique was able to group substructures of similar behaviour and detect spatial anomalies.

One-class support vector machine (OCSVM) is one of the most popular algorithms in the one-class classification problem, but it has one obvious disadvantage: it is sensitive to noise. In order to solve this problem, the fuzzy membership degree is introduced into OCSVM, which makes the samples with different importance have different influences on the determination of classification hyperplane and enhances the robustness. In this paper, a new calculation method of membership degree is proposed and introduced into the fuzzy multiple kernel OCSVM (FMKOCSVM). The combined kernel is used to measure the local similarity between samples, and then, the importance of samples is determined based on the local similarity between training samples, so as to determine the membership degree and reduce the impact of noise. The proposed membership requires only positive data in the calculation process, which is consistent with the training set of OCSVM. In this method, the noise has a smaller membership value, which can reduce the negative impact of noise on the classification boundary. Simultaneously, this method of calculating membership has a higher efficiency. The experimental results show that FMKOCSVM based on proposed local similarity membership is efficient and more robust to outliers than the ordinary multiple kernel OCSVMs.

Soft sensors are widely used to estimate process variables that are difficult to measure online. By using soft sensors, analyzer faults can be detected by estimation errors. However, it is difficult to detect abnormal data and determine the reasons because estimation errors increase not only due to analyzer faults but also due to variations caused by changes in the state of chemical plants. To separate those factors, we previously proposed to construct the relationships between distances to soft sensor models (DMs) and the accuracy of prediction of the models quantitatively and estimate the prediction accuracy of new data online. In this article, we used a one‐class support vector machine (OCSVM) to estimate data density and the output of an OCSVM as a DM. The proposed method was applied to real industrial data and the superiority of the proposed DM to the traditional ones was demonstrated by comparing their results. © 2013 American Institute of Chemical Engineers AIChE J, 59: 2046–2050, 2013

Recently, One Class Support Vector Machines (OCSVM) have been the subject of much research. This paper introduces a novel pattern classification approach for Broken Rotor Bar (BRB) detection in Induction Motors (IM), which combines Stationary Wavelet Packet Transform (SWPT) and OCSVM. Among all the kernels available to OCSVM, wavelet kernels are tuned to improve accuracy and detection time of fault. The SWPT is applied for the relevant data under lower sampling rate and a reduced number of samples. The experimental results on different load levels prove that the proposed method has a better performance than other methods.

In order for a machine to operate safely, it is one of the very important factors to quickly and accurately detect an abnormality. However, it is difficult to detect accurately and always monitor the machine by human decision. Daily inspections of machines are generally performed by monitoring images, vibrations, temperature and pressure. However, it is commonly thought that inspections using sound is difficult because sound is different how to hear depending on the person. In addition, machine learning came to be used easily on these days. In this study, we examined whether machine abnormalities can be detected using machine learning. To prepare for the research, the abnormal machine sounds were needed. However, it is difficult to collect the abnormal machine sound. In this research, to generate the abnormal sound easily, we build a machine operation sound generator. Some abnormal sounds were generated by applying a load to this device. We extracted features quantity called Mel-Frequency Cepstrum Coefficient(MFCC) from machine operation sounds. MFCC is one of the feature quantity and is popular as a method of sound recognition. Subsequently, abnormal sounds were detected by using One Class Support Vector Machine (OCSVM). OCSVM is a method to regard normal data as one class and detect other than normal data. The one of the merits of OCSVM can detect abnormal data by using results of learning only normal data. We did some experiments that we assumed that the sounds of first day were normal sounds and the other sounds assumed abnormal sounds. As a result, the abnormality rate on the fourth day was 78.94% and we conclude that abnormal sounds could be detected from machine operation sounds.

A novel hybrid undersampling method for mining unbalanced datasets in banking and insurance

One-Class Support Vector Machines (OCSVMs) distinguish outliers by computing a hyper-plane in feature space. The choice of the origin as separation point is arbitrary, which affects the decision boundary, and the distribution of samples has impact on the performance. Expanding the algorithm into solving two-class classification problems overcomes the drawbacks to a certain degree. However, the class imbalance problem is serious and the labeled outliers are rare or even non-existing. In this paper, a new Outlier OCSVM is proposed and a framework is designed for unsupervised outlier detection. Respectively scored by distance from hyper-plane and probabilistic output value, two definitions of outlier degree are presented. After picking out some suspicious outliers via combining the two criterions of outlier degree, the adjusted Outlier OCSVM starts the training operations, two parts of the dataset are updated interactively through comparison of the outputs. Experiment results on benchmark datasets show that the method can effectively improve the detection rate and reduce false positive rate, easy and reliable.

Image segmentation is of great importance in the field of image processing. A wide variety of approaches have been proposed for image segmentation. However, SAR image segmentation poses a difficult challenge owing to the high levels of speckle noise. In this paper, we proposed a SAR image segmentation method based on one-class support vector machines (SVM) to solve this problem. One-class SVM and two-class SVM for segmentation is discussed. One-class way is a kind of unsupervised learning, and one-class SVM based segmentation method reduces greatly human interactions, while yielding good segmentation results compared to two-class SVM based segmentation method. The segmentation results based on SVM are also compared to threshold method and adaptive threshold method. Experimental results demonstrate that the proposed method works well for image segmentation while reducing the speckle noise.