Using a Support Vector Machine Based Decision Stage to Improve the Fault Diagnosis on Gearboxes

Monteiro, Rodrigo P.; Cerrada, Mariela; Cabrera, Diego; Sánchez, René–Vinicio; Bastos-Filho, Carmelo J. A.

doi:10.1155/2019/1383752

Cited by 21 publications

(12 citation statements)

References 24 publications

(43 reference statements)

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“…The inputs are the features extracted by the CNN model. We validate the proposed solution with a gearbox diagnosis data set, already used in works like [13,14]. We compare our results with the ones obtained by deep learningbased techniques commonly found in the literature.…”

Section: Introductionmentioning

confidence: 86%

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Feature Extraction Using Convolutional Neural Networks for Anomaly Detection

Monteiro¹,

Filho²

2020

Anais Do 14. Congresso Brasileiro De Inteligência Computacional

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Anomaly detection is an import field of study, which has many applications, e.g., fraud and disease detection. It consists of identifying non-conforming patterns regarding an expected behavior. Despite the improvements provided by deep learning techniques in several areas, their use for anomaly detection is not widespread. The main reason is the difficulty to learn discriminative models when all the information available regards one class, or the classes are highly unbalanced. We propose a new deep learning-based solution for the anomaly detection problem. It consists of a hybrid system, composed of a feature extractor and a one-class classifier. The feature extractor is a convolutional neural network, trained as a regressor to learn a predefined distribution. The classifier is the one-class support vector machine, which performs the anomaly detection by using the outputs provided by the feature extractor. We used a gearbox failure diagnosis data set to assess the performance of our proposal. We also compared our anomaly detection system with other deep learning-based techniques commonly found in the literature. Our proposal presented an average accuracy close to 0.95, outperforming techniques based on the reconstruction error and hybrid models.

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Section: Introductionmentioning

confidence: 86%

“…The strides of those layers were 1 and 2, respectively. The model architecture was inspired by [13,14], and it is illustrated in Fig. 2.…”

Section: Proposed Model: Feature Extraction Based On Convolutionmentioning

confidence: 99%

Feature Extraction Using Convolutional Neural Networks for Anomaly Detection

Monteiro¹,

Filho²

2020

Anais Do 14. Congresso Brasileiro De Inteligência Computacional

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“…3) Long training time for large datasets [41]. SVM attracted the researcher's attention for many applications as seismic liquefaction potential [42]- [46], data classification [47]- [50], texture classification [51], [52], face and speech recognition [53]- [54] [55], cancer diagnosis and prognosis [56], [57], protein fold and remote homology detection [58]- [61] and others [62]- [65].…”

Section: Figure 7 Support Vector Machine Algorithmmentioning

confidence: 99%

A Novel Approach of Diabetic Retinopathy Early Detection Based on Multifractal Geometry Analysis for OCTA Macular Images Using Support Vector Machine

Abdelsalam

Zahran

2021

IEEE Access

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Diabetic Retinopathy (DR) is a complication of diabetes that affects the eyes. It is caused by blood vessel damage of the light-sensitive tissue at the back of the retina. Neovascularization are emerged and the small blood vessels are blocked. The prevention or delaying vision loss can be obtained by DR early detection. The retinal microvascular network as a biological system has its own multifractal features as generalized dimensions, lacunarity and singularity spectrum. In this study, a novel approach for DR early detection based on the multifractal geometry has been proposed in some details. Analyzing the macular optical coherence tomography angiography (OCTA) images for diagnosing early non-proliferative diabetic retinopathy (NPDR). Using a supervised machine learning method as a Support Vector Machine (SVM) algorithm to automate the diagnosis process and improving the resultant accuracy. The classification technique had achieved 98.5 % accuracy. This approach also can classify easily other diabetic retinopathy stages or other retinal diseases, which affect the vessels or neovascularization distribution.

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“…This approach is capable of reducing the computational load and enhancing the classifier's performance. Moreover, Monteiro et al used SVM in order to improve the decision level of a CNN model, which produced a great improvement in training time and diagnosis accuracy [169]. Cheng et al successfully diagnosed wind turbine gearboxes based on the current signal using autoencoder and SVM models, achieving an overall performance of 89.3% [170].…”

Section: A Combination Of Deep Architecture With Sml Modelmentioning

confidence: 99%

Challenges and Opportunities of Deep Learning Models for Machinery Fault Detection and Diagnosis: A Review

et al. 2019

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In the age of industry 4.0, deep learning has attracted increasing interest for various research applications. In recent years, deep learning models have been extensively implemented in machinery fault detection and diagnosis (FDD) systems. The deep architecture's automated feature learning process offers great potential to solve problems with traditional fault detection and diagnosis (TFDD) systems. TFDD relies on manual feature selection, which requires prior knowledge of the data and is time intensive. However, the high performance of deep learning comes with challenges and costs. This paper presents a review of deep learning challenges related to machinery fault detection and diagnosis systems. The potential for future work on deep learning implementation in FDD systems is briefly discussed. INDEX TERMSDeep learning, fault detection and diagnosis, current challenges, future developments. I. INTRODUCTION Safety and reliability are key factors in industrial operations. Rotating machinery is a vital component in many industries, and it is prone to failure due to harsh working conditions and long operational times [1], [2]. Examples of rotating machinery components including gears [3], pumps [4], bearings [5], shafts [6], blades [7], motors [8] and engines [9]. Failures in rotating machinery should be detected as early as possible to prevent critical damage [10] and sudden halt of machine operation. Failures may cause delays in operations and, consequently, tremendous economic loss [11]. For example, petrochemical industries lose around 20 billion dollars per year due to faults in their machine components [12]. According to a report by Duan et al. maintenance accounts for more than 60% of the total cost of aircraft engine components [13]. In the worst case, a machinery component failure may lead to loss of human life. Elasha et al. discussed a caseThe associate editor coordinating the review of this article and approving it for publication was Kezhi Li.

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Using a Support Vector Machine Based Decision Stage to Improve the Fault Diagnosis on Gearboxes

Cited by 21 publications

References 24 publications

Feature Extraction Using Convolutional Neural Networks for Anomaly Detection

Feature Extraction Using Convolutional Neural Networks for Anomaly Detection

A Novel Approach of Diabetic Retinopathy Early Detection Based on Multifractal Geometry Analysis for OCTA Macular Images Using Support Vector Machine

Challenges and Opportunities of Deep Learning Models for Machinery Fault Detection and Diagnosis: A Review

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