Nondestructive Acoustic Testing of Ceramic Capacitors Using One-Class Support Vector Machine With Automated Hyperparameter Selection

Levikari, Saku; Kärkkäinen, Tommi J.; Andersson, Caroline; Tamminen, Juha; Nykyri, Mikko; Silventoinen, Pertti

doi:10.1109/access.2020.3045830

Cited by 4 publications

(3 citation statements)

References 41 publications

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“…The quartiles of iterations and corresponding time for CD-SVDD are significantly smaller than those for the other two deep models. This suggests that CD-SVDD can converge to the optimal AUC value more quickly, demonstrating the effectiveness of using optimization methods to solve the problem (9) and minimize the objective function (6). The outliers observed in the one-class deep SVDD solution process also suggest that CD-SVDD has a more stable solving process.…”

Section: B Results Of Prediction and Efficiencymentioning

confidence: 80%

“…One-class classification models are trained using normal data to detect abnormal instances in prediction [4]. The classical approach is the one-class support vector machine (OCSVM) [5], [6], which assumes the origin is an abnormal point and learns a hyperplane to separate normal data from it. Although OCSVM has achieved great performance in various applications, it is limited by the use of a hyperplane to separate the data.…”

Section: Introductionmentioning

confidence: 99%

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A Complete Deep Support Vector Data Description for One Class Learning

Jiang,

Yang,

Zhao

2023

IEEE Access

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In recent years, Deep Support Vector Data Description (Deep SVDD) has emerged as a leading method in the field of anomaly detection. However, inaccuracies in parameter solving have been identified as a limitation of this approach, which negatively affects its accuracy and efficiency. To address this issue, we propose a new method, called Complete Deep Support Vector Data Description (CD-SVDD). Our CD-SVDD is constructed with a traditional deep neural network and utilizes a modified SVDD as its last layer. Its parameters are solved by an alternate iteration algorithm that ensures both high precision and fast convergence of solutions. By keeping the network weights fixed, we solve the center and radius of the modified SVDD based on its convex dual optimization problem. We then update the parameters of the neural network by backpropagation. This approach enables us to maintain the ν-property found in shallow SVDD, which is beneficial for parameter selection and model interpretability. To evaluate the performance of CD-SVDD, we conducted extensive numerical experiments with five existing methods on two image datasets, CIFAR-10 and CIFAR-100, as well as five recorded benchmark datasets. Our results demonstrate that CD-SVDD achieves superior accuracy and efficiency in the detection of anomalies.

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Section: B Results Of Prediction and Efficiencymentioning

confidence: 80%

Section: Introductionmentioning

confidence: 99%

A Complete Deep Support Vector Data Description for One Class Learning

Jiang,

Yang,

Zhao

2023

IEEE Access

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“…In addition, one method that is also commonly used to detect mechanical damage to machines is the acoustic method. This method is used because it has a higher level of security compared to other methods, because measurements do not have to be performed via direct contact with the monitored equipment [ 17 ]. The use of acoustic methods to detect the irregularities in systems has a long history [ 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

Detection of Mechanical Failures in Industrial Machines Using Overlapping Acoustic Anomalies: A Systematic Literature Review

Qurthobi

Maskeliūnas

Damaševičius

2022

Sensors

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One of the most important strategies for preventative factory maintenance is anomaly detection without the need for dedicated sensors for each industrial unit. The implementation of sound-data-based anomaly detection is an unduly complicated process since factory-collected sound data are frequently corrupted and affected by ordinary production noises. The use of acoustic methods to detect the irregularities in systems has a long history. Unfortunately, limited reference to the implementation of the acoustic approach could be found in the failure detection of industrial machines. This paper presents a systematic review of acoustic approaches in mechanical failure detection in terms of recent implementations and structural extensions. The 52 articles are selected from IEEEXplore, Science Direct and Springer Link databases following the PRISMA methodology for performing systematic literature reviews. The study identifies the research gaps while considering the potential in responding to the challenges of the mechanical failure detection of industrial machines. The results of this study reveal that the use of acoustic emission is still dominant in the research community. In addition, based on the 52 selected articles, research that discusses failure detection in noisy conditions is still very limited and shows that it will still be a challenge in the future.

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