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An Unsupervised Hybrid Approach for Detection of Damage with Autoencoder and one-class support vector machine

APPLIED SCIENCES-BASEL 2025年第8期15卷 4098-4098页

作者： Gunes, Burcu Gunes, Oguz Istanbul Tech Univ Dept Civil Engn TR-34467 Istanbul Turkiye MiTek Mitigat Technol AS TR-34467 Istanbul Turkiye

Progressive deterioration and accumulated damage due to overloading, extreme events, and fatigue necessitate the continuous monitoring of civil infrastructure to ensure serviceability and safety. With advances in sensor technology, data-driven structural health monitoring (SHM) strategies, particularly artificial neural networks (ANNs), have gained prominence for analyzing large datasets and identifying complex patterns. Among these, autoencoders (AEs), a specialized class of ANNs, are well-suited for unsupervised learning tasks, enabling dimensionality reduction and feature extraction. This study employs transmissibility functions (TFs) as training samples for the AE. TFs are directly derived from response measurements without the need to measure input and exhibit local sensitivity to changes in dynamic properties, making them an efficient feature for structural assessment. The reconstruction errors in TFs, quantifying the deviation between the original and AE-reconstructed data, are leveraged as damage-sensitive features for classification using a one-class support vector machine (OC-SVM). The proposed methodology is validated through numerical simulations with noise-contaminated data representing various damage scenarios in a shear-building model, as well as experimental tests on a masonry arch bridge model subjected to progressive damage. Numerical investigations demonstrate improved detection accuracy and robustness of the procedure through the incorporation of nonlinear encoding into the dimensionality reduction process, compared to the classical principal component analysis method.. Experimental results confirm the framework's effectiveness in detecting and localizing damage using unlabeled field data.

关键词： unsupervised learning autoencoder structural damage assessment one-class support vector machine transmissibility function principal component analysis

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The feasibility of using ATR-FTIR spectroscopy combined with one-class support vector machine in screening turmeric powders

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VIBRATIONAL SPECTROSCOPY 2024年 130卷

作者： Ballesteros, Joel I. Lim, Len Herald V. Lamorena, Rheo B. Univ Philippines Diliman Inst Chem Quezon City 1101 Philippines Univ Philippines Inst Chem R103Res Bldg Quezon City Philippines

Once turmeric has been ground into powder, it is difficult to tell visually if it has been tampered with. In this study, ATR-FTIR spectroscopy was used in tandem with one-class support vector machine (OCSVM) to detect adulteration in turmeric powder. The OCSVM models were trained using 42 pure turmeric powder samples, optimized using 30 pure turmeric powder samples, and subsequently evaluated by classifying 30 pure and 120 adulterated (cornstarch, Metanil Yellow, Orange II, and Sudan I) samples. Preprocessing methods, such as Savitzky-Golay (SG)-derivatives, standard normal variate (SNV), and multiplicative scatter correction (MSC), were used individually and in combination to obtain the best-performing model. Models were assessed by comparing the sensitivity, specificity, and efficiency values and compared with one-class soft independent modeling of class analogy (OCSIMCA). The best performing OCSVM model (sensitivity = 1.00, specificity = 0.89) was obtained by first conducting an MSC on the raw data followed by SG-2nd derivative transformation. It also has an efficiency value of 0.94, which was 0.14 higher than when data preprocessing was not done. Compared to the results of OCSIMCA, the OCSVM model gave a higher efficiency value and can detect lower levels of cornstarch adulteration. Also, the results showed that inclusion of data preprocessing can lead to a better classification model. With the obtained evaluation parameter values, ATR-spectroscopy coupled with OCSVM demonstrated its potential for screening turmeric powder products.

关键词： ATR-FTIR spectroscopy Turmeric powder one-class support vector machine one-class SIMCA Data preprocessing class-modeling

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Robust one-class support vector machine

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NEURAL NETWORKS 2025年 188卷 107416页

作者： Zhao, Xiaoxi Tian, Yingjie Zheng, Chonghua Hangzhou Dianzi Univ Sch Management Hangzhou 310018 Peoples R China Hangzhou Dianzi Univ Expt Ctr Data Sci & Intelligent Decis Making Hangzhou 310018 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China UCAS MOE Social Sci Lab Digital Econ Forecasts & Policy Beijing 100190 Peoples R China

one-class support vector machine (OCSVM) is an effective algorithm in one-class classification task. However, it exhibits sensitivity to noise and outliers. Current solutions often employ bounded loss functions that impose finite but relatively large penalties on noise or outliers, and these loss functions suffer from limitations of discontinuity and non-differentiability. To address these issues, this paper introduces a novel, continuous, smooth, and differentiable loss function, namely Quadratic Type Squared Error Loss Function (QTSELF), and proposes a more robust OCSVM (Q-OCSVM). Q-OCSVM not only differentiates samples based on their positions and applies distinct treatments accordingly but also enhances model robustness by imposing minimal penalties on noise and outliers. Moreover, the elegant mathematical properties of the loss function facilitate model optimization. Theoretical analysis utilizes Rademacher complexity theory to conduct the generalization error bound of the model. Momentum method is used to optimize Q-OCSVM. Extensive experiments convincingly demonstrate that Q-OCSVM outperforms the benchmark techniques.

关键词： one-class support vector machine Loss function Robustness Momentum

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Detection of Signal Integrity Issues in Vibration Monitoring Using one-class support vector machine

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JOURNAL OF VIBRATION ENGINEERING & TECHNOLOGIES 2024年第SUPPL 1期12卷 601-611页

作者： Kannan, Vigneshwar Dao, Dzung Viet Li, Huaizhong Griffith Univ Sch Engn & Built Environm Gold Coast Campus Gold Coast Qld 4222 Australia

This paper presents an analysis of the common signal integrity issues in vibration monitoring caused by sensor saturation and signal distortion, or sensor loosening and detachment, and the development of a method of detecting the occurrence of vibration signal integrity issues using a one-class support vector machine. For this, vibration signals with distortions due to sensor saturation and/or sensor detachment are analysed to determine parameters sensitive to common integrity issues. These features are then extracted from training data of good quality vibration signals. Principal Component Analysis is used to dimensionally reduce the parameters which are then used in the training of the one-class support vector machine. The proposed method was validated on trained and untrained health conditions. Additionally, model sensitivity was also tested on trained health conditions with varying defect sizes. The method successfully detected all signal integrity issues tested proving to be an effective pre-processing step in condition monitoring systems for increased reliability and accuracy.

关键词： Vibration Signal integrity Anomaly detection Condition monitoring Principal component analysis one-class support vector machine

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A Writer-Dependent Approach to Offline Signature Verification Based on one-class support vector machine

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PATTERN RECOGNITION AND IMAGE ANALYSIS 2024年第2期34卷 340-351页

作者： Starovoitov, V. V. Akhundjanov, U. Yu. Natl Acad Sci Belarus United Inst Informat Problems Minsk 220012 BELARUS European Humanities Univ EPAM Sch Digital Engn LT-03116 Vilnius Lithuania Tashkent Univ Informat Technol Ferghana Branch Ferghana 150100 Uzbekistan

A new solution to the problem of offline signature verification is presented. Digital images of signatures are processed and converted into a binary representation of a certain size. Then their contours are traced, and from them, two original features are calculated for describing the local structural features of the signature in the form of vectors of normalized frequency distributions of local binary pattern codes and values of local curvature of the signature contours. A new feature space is formed in which the pattern describes the proximity of pairs of signatures, and its coordinates are the rank correlation coefficients between the feature vectors of these signatures. In real practice, the expert has M (from 5 to 15) genuine signatures of a person;there are no forged signatures at all. On these M available genuine signatures of a single person, we train a one-class support vector machine model and obtain a single-writer-dependent classifier. A verifiable signature is considered forged if the classifier model considers it to be an outlier. The accuracy of our approach in verifying the genuineness of all 2640 signatures from the CEDAR database was 99.77%. All forged signatures in this database were correctly recognized.

关键词： offline signature verification features one-class support vector machine CEDAR dataset

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Integration of ASTER and Soil Survey Data by Principal Components Analysis and one-class support vector machine for Mineral Prospectivity Mapping in Kerkasha, Southwestern Eritrea

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NATURAL RESOURCES RESEARCH 2023年第6期32卷 2463-2493页

作者： Zerai, Finhas Tsehaye Gorsevski, Pece V. Panter, Kurt S. Farver, John Tangestani, Majid H. Bowling Green State Univ Sch Earth Environm & Soc Bowling Green OH 43403 USA Shiraz Univ Fac Sci Dept Earth Sci Shiraz Iran

This study evaluates the potential for mineral prospectivity mapping (MPM) within the Kerkesha area, southwestern Eritrea, using remote sensing and geochemical data analysis. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) remote sensing data were used for mapping zones of hydrothermal alteration, while assessment of geologic structures was based on automated extraction of lineaments from a digital elevation model (DEM). Integration of these alteration and structural datasets with surface geochemical data was carried out to identify pathfinder elements associated with Au-Cu-Zn mineralization as well as to evaluate and delineate anomalous regions in this relatively underexplored region of the Arabia Nubia Shield (ANS). Specifically, the modeling approach for the extraction and interpretation of mineralization-related spectral footprints was the selective principal component analysis (SPCA), and the lineament features extracted from DEM derivatives were integrated with the soil geochemical data and modeled by principal component analysis (PCA). The results revealed a NE-SW trend of lineaments, delineated zones of hydrothermal alteration indicating the presence of multi-deposit type mineralization, and identified pathfinder elements. In addition, Au-Cu-Zn anomalous zones were extracted by one-class support vector machine (OCSVM) and the performance of such classification was validated by the area under the curve (AUC) and the Youden index computed from receiver operating characteristic (ROC) curves and the Kruskal-Wallis test. The results showed significance in differences between the anomalous and non-anomalous zones and existence of a relationship between known mineral deposits and predicted anomalies. The proposed MPM shows promising results for robust automated delineation and understanding of mineralization processes.

关键词： SPCA one-class support vector machine Lineaments Predictive PCA Geochemical anomaly ASTER

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one-class support vector machine with Particle Swarm Optimization for Geo-Acoustic Anomaly Detection 17

One-Class Support Vector Machine with Particle Swarm Optimiz...

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17th IEEE International Conference on Mobility, Sensing and Networking (MSN)

作者： Zhang, Dan Liang, Yiwen Sun, Zhihong Mukherjee, Mithun Wuhan Univ Sch Comp Sci Wuhan Peoples R China Wuhan Univ Natl Engn Res Ctr Multimedia Software Wuhan Peoples R China Coll Artificial Intelligence Technol Nanjing Peoples R China

ISBN: (纸本)9781665406680

Without prediction and prior warning, earthquakes can cause massive damage to human society. The earthquake research has been exploring, and researchers discover that earthquakes happen with many natural phenomena, earthquake precursors. Geo-acoustic signals may contain a good precursor signal to a seismic event. The Acoustic Electromagnetic to AI (AETA) system, a high-density multi-component seismic monitoring system, is deployed to record geo-acoustic signals across 0.1Hz 10kHz. This paper aims to detect the anomalies of geo-acoustic signals that may contain earthquake precursors. This study employs the one-class support vector machine(OCSVM) to detect the anomalies and applies Particle Swarm Optimization(PSO) to optimize the parameters of OCSVM. The experimental results show that the proposed method obtains promising results concerning the abnormal detection in geo-acoustic signals of the AETA system.

关键词： one-class support vector machine Particle Swarm Optimization Anomaly Detection Geo-acoustic Signal AETA System

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Adaptive loss function based least squares one-class support vector machine

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PATTERN RECOGNITION LETTERS 2022年 156卷 174-182页

作者： Xing, Hong-Jie He, Zi-Chuan Hebei Univ Coll Math & Informat Sci Hebei Key Lab Machine Learning & Computat Intelli Baoding 071002 Peoples R China

Least squares one-class support vector machine (LS-OCSVM) can accurately describe the similarity between new sample and training set. However, LS-OCSVM is very sensitive to the outliers among training samples, which means that the separating hyperplane of LS-OCSVM may deviate from the normal data even with a few outliers. To enhance the anti-outlier performance of LS-OCSVM, a novel adaptive loss function based LS-OCSVM is proposed. In the proposed method, an adaptive loss function is utilized to substitute the square loss function in the objective function of LS-OCSVM. The property of Fisher consistency for the adaptive loss function is validated from the theoretical viewpoint. The optimization problem of the proposed method is solved by the iteratively reweighted least squares (IRLS) method. In comparison with its nine related methods, the proposed method demonstrates better anti-outlier and generalization abilities on synthetic and benchmark data sets.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Least squares one-class support vector machine one-class classification one-class support vector machine Loss function

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Bearing Fault Diagnosis Based on Diffusion Model and one-class support vector machine

Bearing Fault Diagnosis Based on Diffusion Model and One-Cla...

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Prognostics and Health Management Conference (PHM)

作者： Yan, Lijuan Pu, Ziqiang Yang, Zhe Li, Chuan Chongqing Technol & Business Univ Res Ctr Syst Hlth Maintenance Chongqing Peoples R China Dongguan Univ Technol Sch Mech Engn Dongguan Peoples R China

ISBN: (纸本)9798350300147

Fault diagnosis is of great importance for reducing economic losses and ensuring the safety of equipment. As an important part of industrial machine, it is necessary to perform fault diagnosis on bearings. However, in reality, normal data are often more abundant than fault data, making it challenging to recognize faults. To address this issue, the diffusion model with unet has been introduced, for its excellent feature extraction ability. In this paper, the feature knowledge extracted by diffusion model is sent into one-class support vector machine (OCSVM) for anomaly detection instead of using raw data. Firstly, train the diffusion model with normal data, and then take out the trained encoder in u-net. Next, input test data which includes both normal data and abnormal data into the trained encoder to extract feature knowledge. Finally, train OCSVM with normal data and then send the extracted feature knowledge into trained OCSVM for detection. Compared to directly using raw data for fault diagnosis, the proposed method achieves superior accuracy.

关键词： fault diagnosis diffusion model encoder feature extraction one-class support vector machine

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An unsupervised anomaly detection framework for onboard monitoring of railway track geometrical defects using one-class support vector machine

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年第PartB期133卷

作者： Ghiasi, Ramin Khan, Muhammad Arslan Sorrentino, Danilo Diaine, Cassandre Malekjafarian, Abdollah Univ Coll Dublin Sch Civil Engn Struct Dynam & Assessment Lab Dublin D04V1W8 Ireland SNCF Reseau Ind & Engn Gen Management La Plaine St Denis France

Track geometry is one of the critical indicators of railway tracks' condition which requires continuous monitoring and maintenance over time. In this paper, a novel artificial intelligence (AI) based framework is proposed for railway track geometry inspection using vibration data collected from a dedicated measuring high-speed train. This AI-based anomaly track detection approach consists of two main stages. Firstly, a subset of features that best characterizes the track condition is defined. Several dynamic features from time domain data are extracted and importance scores are assigned to them, to determine the most effective subset for the purpose of track condition monitoring. Secondly, a data-driven based anomaly detection approach is developed to assess and identify track geometrical defects. In this stage, the acceleration responses collected from an in-service train traversing on a healthy track zone are employed as input into a one-class support vector machine (OCSVM) algorithm. The proposed algorithm defines the anomalies as relative changes to the historical behaviour. A comprehensive dataset from field measurements using a Socie ' te ' Nationale des Chemins de Fer Francais (SNCF) Re ' seau IRIS320 highspeed train is used in this paper to implement the proposed approach. In addition, the impact of using different features and different locations/directions of the sensors on the accuracy of detecting geometrical defects is investigated. It is also shown that the OCSVM approach outperforms other algorithms based on Isolation Forest (IF), Local Outlier Factor (LOF), and Robust Mahalanobis Distance (RMD) in terms of recall, precision, and F1-score. The proposed anomaly detection approach has demonstrated a 12% increase in defect detection accuracy compared to the direct utilization of the raw acceleration response, which can facilitate track monitoring using in-service trains while providing cost-efficient maintenance in the future.

关键词： Anomaly detection Data driven Track geometry Feature extraction Onboard monitoring one-class support vector machine

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