Fault Diagnosis of Complex Industrial Process Using KICA and Sparse SVM

Xu, Jie; Zhao, Jin; Ma, Baoping; Shou-song, HU

doi:10.1155/2013/987345

Cited by 8 publications

(5 citation statements)

References 12 publications

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“…Rolling bearings are an extremely vital part of mechanical equipment, whose running state determines directly the performance of the whole rotating mechanical system, so the research on bearing fault diagnosis is very important [1,2]. In recent years, the research methods about bearing fault diagnosis are mostly under the assumption of constant working condition [3][4][5]. When these methods are applied under variable working conditions, the diagnostic effect will be reduced.…”

Section: Introductionmentioning

confidence: 99%

The fault diagnosis method of rolling bearing under variable working conditions based on deep transfer learning

Dong

Tang

2020

J Braz. Soc. Mech. Sci. Eng.

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The vibration signals of rolling bearing obtained under variable working conditions do not obey the same independent distribution so that the traditional method of bearing fault diagnosis has low accuracy, a fault diagnosis method about rolling bearing based on sparse denoising autoencoder (SDAE) for deep feature extraction combining transfer learning is proposed. First, the bearing vibration signal in the time domain is transformed for frequency domain signal via Fourier transform, which is input into the SDAE for adaptive deep feature extraction. Then, the joint geometrical and statistical alignment is introduced to deal with the deep feature samples for reducing the domain discrepancy both statistically and geometrically. Finally, the k-nearest neighbor classification algorithm is used for completing the fault diagnosis of rolling bearing under variable working conditions. The experimental results show that the method presented in the paper improves the accuracy rate of fault diagnosis about rolling bearing under variable working conditions, verifies its feasibility and effectiveness.

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Section: Introductionmentioning

confidence: 99%

The fault diagnosis method of rolling bearing under variable working conditions based on deep transfer learning

Dong

Tang

2020

J Braz. Soc. Mech. Sci. Eng.

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“…Moreover, a SVM classifier has been merged with statistical process monitoring techniques to make hybrid models. These hybrid models are composed of fault detection and feature extraction, the extracted feature employed by SVM for fault diagnosis. − Moreover, the kernel ICA and SVM were developed by Zhang for the application of fault detection and diagnosis method for nonlinear processes. The wavelet transform decomposition and multiclass SVM was modeled by Du .…”

Section: Introductionmentioning

confidence: 99%

Multiple Fault Diagnosis in Distillation Column Using Multikernel Support Vector Machine

Taqvi

Tufa

Zabiri

et al. 2018

Ind. Eng. Chem. Res.

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Fault detection and diagnosis (FDD) in process industries is an important task for efficient process monitoring and plant safety. It is also essential for improving product quality and reducing production cost by reducing process downtime. Real-time multiscale classification of faults plays a vital role in process monitoring. However, some major issues such as high correlation, complexity, and nonlinearity of data are yet to be addressed. In this paper, a fault diagnosis approach based on multikernel support vector machines is proposed to classify the internal and external faults such as reflux failure, change in reboiler duty, column tray upsets, and change in feed composition, flow, and temperature in a distillation column. The data set is generated using Aspen plus dynamics simulation at normal and faulty states. The classification has been done by various methods such as decision tree, K-nearest neighbors, linear discriminant analysis, artificial neural network, subspace discriminant, and multikernel support vector machine. It is observed that the SVM based diagnostic system provides more accurate root cause isolation. The proposed MK-SVM method was evaluated by using the confusion matrix as the performance evaluator. The result showed that the proposed model has a high FDR which is 99.77% and a very low FAR, i.e., 0.23%.

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“…Independent component analysis (ICA) has attracted much attention since it proposed [8]- [10], and it has been applied in many fields, such as voice recognition field [8], [11], image recognition field [9] and data extraction field [10]. Feature extraction is of great importance in analog circuit diagnosis.…”

Section: Introductionmentioning

confidence: 99%

An Analog Circuit Diagnosis Method Based on Adaptive-Kernel ICA

Jing¹,

Liu²,

He³

2016

IJEEE

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An analog circuit fault diagnosis method based on an adaptive kernel function is presented. The adaptive kernel function is built by using the linear properties of the kernel functions. There are three steps for analog circuit diagnosis: preprocessing, feature extraction and faults classifying. The data is de-noised and centralized by preprocessing step firstly. Then a feature extraction method based on AMK-ICA technology is presented to lower the dimension of the data. Finally, the support vector machine is used to identify the fault mode of the circuit under test. Experimental study shows that the method can effectively improve the samples training time, test time and identification precision compared with the main reference cited in this paper.

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Fault Diagnosis of Complex Industrial Process Using KICA and Sparse SVM

Cited by 8 publications

References 12 publications

The fault diagnosis method of rolling bearing under variable working conditions based on deep transfer learning

The fault diagnosis method of rolling bearing under variable working conditions based on deep transfer learning

Multiple Fault Diagnosis in Distillation Column Using Multikernel Support Vector Machine

An Analog Circuit Diagnosis Method Based on Adaptive-Kernel ICA

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