Soft Fault Detection Algorithms for Multi-Parallel Data Streams Under the Cloud Computing

Hongbing Meng

doi:10.20965/jaciii.2018.p1114

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JACIII Vol.22 No.7 pp. 1114-1119

doi: 10.20965/jaciii.2018.p1114

(2018)

Paper:

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Soft Fault Detection Algorithms for Multi-Parallel Data Streams Under the Cloud Computing

Hongbing Meng

College of Information Engineering, Tarim University
Alar, Xinjiang 843300, China

Received:

April 26, 2018

Accepted:

June 4, 2018

Published:

November 20, 2018

Keywords:

cloud computing, multiple parallel, data stream, soft fault detection

Abstract

In the fault detection of multi-parallel data streams, the error probability of traditional methods is large, which cannot effectively meet the soft fault detection for multi-parallel data stream, causing the problem of low detection efficiency. A soft fault detection algorithm based on adaptive multi-parallel data stream is proposed. The soft fault feature in the data stream is extracted, and the adaptive soft fault detection algorithm is used to detect the fault of the multi-parallel data stream, which can overcome the disadvantages of traditional methods, effectively improve the efficiency, safety and the accuracy. Experimental results showed that the proposed method can effectively improve the efficiency of fault detection.

Classification results of the method proposed in this paper

Cite this article as:

H. Meng, “Soft Fault Detection Algorithms for Multi-Parallel Data Streams Under the Cloud Computing,” J. Adv. Comput. Intell. Intell. Inform., Vol.22 No.7, pp. 1114-1119, 2018.

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References

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[11] S. M. Zhang, D. Mao, and B. Y. Wang, “Application of Big Data Processing Technology in Fault Diagnosis and Early Warning of Wind Turbine Gearbox,” Automation of Electric Power Systems, Vol.40, No.14, pp. 129-134, 2016.
[12] D. Wu and Y. Li, “Fault diagnosis of variable pitch for wind turbines based on the multi-innovation forgetting gradient identification algorithm,” Nonlinear Dynamics, Vol.79, No.3, pp. 2069-2077, 2015.
[13] G. Yang, Y. Zhang, and J. Yang et al., “Automated classification of brain images using wavelet-energy and biogeography-based optimization,” Multimedia Tools & Applications, Vol.75, No.23, pp. 15601-15617, 2016.
[14] Z. Zhu et al., “PSO-SVR-Based Resource Demand Prediction in Cloud Computing,” J. Adv. Comput. Intell. Intell. Inform., Vol.20, No.2, pp. 324-331, 2016.

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] F. R. Blánquez, C. A. Platero, and E. Rebollo et al., “Novel Rotor Ground-Fault Detection Algorithm for Synchronous Machines With Static Excitation Based on Third-Harmonic Voltage-Phasor Comparison,” IEEE Trans. on Industrial Electronics, Vol.63, No.4, pp. 2548-2558, 2016.

[2] [2] C. Cecati, A. O. D. Tommaso, and F. Genduso et al., “Comprehensive Modeling and Experimental Testing of Fault Detection and Management of a Nonredundant Fault-Tolerant VSI,” IEEE Trans. on Industrial Electronics, Vol.62, No.6, pp. 3945-3954, 2015.

[3] [3] S. Das, T. Sidhu, and M. R. D. Zadeh et al., “A Novel Hybrid Differential Algorithm for Turn to Turn Fault Detection in Shunt Reactors,” IEEE Trans. on Power Delivery, Vol.32, No.6, pp. 2537-2545, 2017.

[4] [4] G. Yang, X. Deng, and C. Liu, “Facial expression recognition model based on deep spatiotemporal convolutional neural networks,” Zhongnan Daxue Xuebao (Ziran Kexue Ban)/J. of Central South University (Science and Technology), Vol.47, No.7, pp. 2311-2319, 2016.

[5] [5] J. Kwak, T. Lee, and O. K. Chang, “An Incremental Clustering-Based Fault Detection Algorithm for Class-Imbalanced Process Data,” IEEE Trans. on Semiconductor Manufacturing, Vol.28, No.3, pp. 318-328, 2015.

[6] [6] Z. J. Li, L. L. Liang, and C. W. Han et al., “Multi-rate Process Fault Detection Based on Partial Least Squares,” Computer Simulation, Vol.33, No.10, pp. 445-449, 2016.

[7] [7] S. Yin and X. Zhu, “Intelligent Particle Filter and Its Application to Fault Detection of Nonlinear System,” IEEE Trans. on Industrial Electronics, Vol.62, No.6, pp. 3852-3861, 2015.

[8] [8] S. L. Wu, Z. M. Tang, and C. Y. Chen, “A Real-time Fault Detection Method for the Air-fuel Ratio of Gasoline Engine Based on the Trend and Correlation Analysis of Dataflow,” Automotive Engineering, Vol.39, No.4, pp. 462-470, 2017.

[9] [9] E. Yao, J. Zhang, and M. Chen et al., “Detection of soft errors in LU decomposition with partial pivoting using algorithm-based fault tolerance,” Int. J. of High Performance Computing Applications, Vol.29, No.4, pp. 422-436, 2015.

[10] [10] J. Yi, D. Huang, S. Fu et al., “Optimized Relative Transformation Matrix Using Bacterial Foraging Algorithm for Process Fault Detection,” IEEE Trans. on Industrial Electronics, Vol.63, No.4, pp. 2595-2605, 2016.

[11] [11] S. M. Zhang, D. Mao, and B. Y. Wang, “Application of Big Data Processing Technology in Fault Diagnosis and Early Warning of Wind Turbine Gearbox,” Automation of Electric Power Systems, Vol.40, No.14, pp. 129-134, 2016.

[12] [12] D. Wu and Y. Li, “Fault diagnosis of variable pitch for wind turbines based on the multi-innovation forgetting gradient identification algorithm,” Nonlinear Dynamics, Vol.79, No.3, pp. 2069-2077, 2015.

[13] [13] G. Yang, Y. Zhang, and J. Yang et al., “Automated classification of brain images using wavelet-energy and biogeography-based optimization,” Multimedia Tools & Applications, Vol.75, No.23, pp. 15601-15617, 2016.

[14] [14] Z. Zhu et al., “PSO-SVR-Based Resource Demand Prediction in Cloud Computing,” J. Adv. Comput. Intell. Intell. Inform., Vol.20, No.2, pp. 324-331, 2016.