Abrupt Event Monitoring for Water Environment System Based on KPCA and SVM

Ni, Jianjun; Zhang, Chuanbiao; Ren, Li; Yang, Simon X.

doi:10.1109/tim.2011.2173000

Cited by 31 publications

(16 citation statements)

References 28 publications

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“…Therefore, SVM has advantages in the classification of small sample sets and pattern recognition of high dimensional data sets. The principles of SVM are briefly described below [35][36][37][38][39].…”

Section: Svm Theorymentioning

confidence: 99%

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A Fusion Feature Extraction Method Using EEMD and Correlation Coefficient Analysis for Bearing Fault Diagnosis

Jiang

Zhu

et al. 2018

Applied Sciences

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Acceleration sensors are frequently applied to collect vibration signals for bearing fault diagnosis. To fully use these vibration signals of multi-sensors, this paper proposes a new approach to fuse multi-sensor information for bearing fault diagnosis by using ensemble empirical mode decomposition (EEMD), correlation coefficient analysis, and support vector machine (SVM). First, EEMD is applied to decompose the vibration signal into a set of intrinsic mode functions (IMFs), and a correlation coefficient ratio factor (CCRF) is defined to select sensitive IMFs to reconstruct new vibration signals for further feature fusion analysis. Second, an original feature space is constructed from the reconstructed signal. Afterwards, weights are assigned by correlation coefficients among the vibration signals of the considered multi-sensors, and the so-called fused features are extracted by the obtained weights and original feature space. Finally, a trained SVM is employed as the classifier for bearing fault diagnosis. The diagnosis results of the original vibration signals, the first IMF, the proposed reconstruction signal, and the proposed method are 73.33%, 74.17%, 95.83% and 100%, respectively. Therefore, the experiments show that the proposed method has the highest diagnostic accuracy, and it can be regarded as a new way to improve diagnosis results for bearings.

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Section: Svm Theorymentioning

confidence: 99%

“…SVM techniques are easier to use and address nonlinearity classification problems. Therefore, numerous SVM-based mechanical fault diagnosis strategies have been proposed [35][36][37].…”

Section: Introductionmentioning

confidence: 99%

A Fusion Feature Extraction Method Using EEMD and Correlation Coefficient Analysis for Bearing Fault Diagnosis

Jiang

Zhu

et al. 2018

Applied Sciences

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“…Based on PCA, kernel principal component analysis (KPCA) firstly maps the original data to a high-dimensional linear feature space via kernel function and then uses the PCA method to extract features. The KPCA algorithm has a wide range of applications in the fields of feature extraction, data compression, and pattern recognition [13][14][15]. In order to solve the problem of transformer fault diagnosis, the authors of [16] obtained 34 eigenvectors by combining electrical experiment data with dissolved gases in oil and uses the KPCA method to reduce dimensions.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Dissolved Gas Concentrations in Transformer Oil Based on the KPCA-FFOA-GRNN Model

et al. 2018

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Abstract:The purpose of analyzing the dissolved gas in transformer oil is to determine the transformer's operating status and is an important basis for fault diagnosis. Accurate prediction of the concentration of dissolved gas in oil can provide an important reference for the evaluation of the state of the transformer. A combined predicting model is proposed based on kernel principal component analysis (KPCA) and a generalized regression neural network (GRNN) using an improved fruit fly optimization algorithm (FFOA) to select the smooth factor. Firstly, based on the idea of using the dissolved gas ratio of oil to diagnose the transformer fault, gas concentration ratios are also used as characteristic parameters. Secondly, the main parameters are selected from the feature parameters using the KPCA method, and the GRNN is then used to predict the gas concentration in the transformer oil. In the training process of the network, the FFOA is used to select the smooth factor of the neural network. Through a concrete example, it is shown that the method proposed in this paper has better data fitting ability and more accurate prediction ability compared with the support vector machine (SVM) and gray model (GM) methods.

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“…In 1998, Schölkopf put forward a new kind of nonlinear PCA approach (kernel principal component analysis, KPCA) [5]. The essential characteristic of KPCA is to use inner product and kernel function to implement linearity, and obtain principle component efficiently in the nonlinear high dimensional eigenspace [6,7]. The drawback of the method is that it cannot reveal the intrinsic topological of the data and it is very hard to find a suitable kernel function.…”

Section: Introductionmentioning

confidence: 99%

An improved LLE-based cluster security approach for nonlinear system fault diagnosis

Zhang

Gao

2017

Cluster Comput

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This paper proposes a novel improved LLE-based (local linear embedding) approach (TLLE) for solving the fault diagnosis problem of the nonlinear system. Firstly, tangent space distance is introduced to LLE approach, which can satisfy the local linearity of LLE and better preserve the local manifold features of the original data simultaneously. Then, to solve the problem of inner dimension in LLE approach is hard to estimate, the method of intrinsic dimension estimation based on fractal dimension is employed in the approach by means of linear fitting. Furthermore, a fault diagnosis scheme is presented based on the TLLE approach. We combine fault state with special distribution to complete the fault diagnosis, which can simplify the computation obviously and improve the real-time capability of the approach. Finally, numerical simulations of the TE process data are performed to illustrate the effectiveness of the proposed fault diagnosis scheme.

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Abrupt Event Monitoring for Water Environment System Based on KPCA and SVM

Cited by 31 publications

References 28 publications

A Fusion Feature Extraction Method Using EEMD and Correlation Coefficient Analysis for Bearing Fault Diagnosis

A Fusion Feature Extraction Method Using EEMD and Correlation Coefficient Analysis for Bearing Fault Diagnosis

Prediction of Dissolved Gas Concentrations in Transformer Oil Based on the KPCA-FFOA-GRNN Model

An improved LLE-based cluster security approach for nonlinear system fault diagnosis

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