基于光学灰度图像辨识的系统故障诊断方法

故障诊断系统已成为确保工业系统及设备安全运行的重要辅助工具。在故障发生的早期阶段,故障诊断系统可以快速提供早期预警信息,并为故障缓解方案制定提供参考。为此,提出了一种基于光学灰度图像辨识的故障诊断方法。该方法根据实时监测数据构造系统的运行状态图像,通过CCD摄录的方式截取灰度图像,并从不同分辨率的系统灰度图像近似直方图中提取出系统的灰度图像特征。根据这些特征与标准特征之间的欧式距离比较,将当前状态划分为相距最小的标准特征类。实验结果表明该方法能够快速且正确地检测出故障类型,为系统相关设计与维护人员提供有效的支持信息。

Abstract

Fault diagnosis systems have become an important aid to ensure the safe operation of industrial systems and equipments. In the early stages of the fault, the diagnosis system can quickly provide early warning information and reference for the development of the fault mitigation plan. For this purpose, a fault diagnosis method based on optical gray image recognition is proposed. The method constructs the running state image of the system according to the real-time monitoring data, intercepts the gray image by CCD recording, and extracts the gray image from different resolution systems. The grayscale image features of the system are extracted from the approximate histogram. Based on the Euclidean distance between these features and the standard features, the current state is divided into the standard feature classes with the smallest distance. The experimental results show that the method can detect the fault type quickly and correctly, and can provide effective support information for the system related design and maintenance personnel.

参考文献

[1] Wang Chuanjun, Li Huaizhen, Yao Jinsheng. Discussion on electric motor and controller fault diagnosis expert system [J]. Electric Machines and Control Application (电机与控制应用), 2016, 43(5): 69-72 (in Chinese).

[2] Lu Baichuan, Zhang Kai, Ma Qinglu, et al. Data fault diagnosis model based on data-driven [J]. Journal of Chongqing Jiaotong University(Natural Science) (重庆交通大学学报(自然科学版)), 2014, 33(5): 111-115 (in Chinese).

[3] Wang L, Liu Z, Miao Q, et al. Time-frequency analysis based on ensemble local mean decomposition and fast Kurtogram for rotating machinery fault diagnosis [J]. Mechanical Systems and Signal Processing, 2018, 103: 60-75.

[4] Zhao Zhiguo, Li Hangyu, Lei Dan. Fault diagnosis based on dual kalman filter of clutch brushless DC actuator motor for dry dual clutch transmission [J]. Journal of Mechanical Engineering (机械工程学报), 2018, 54(2): 138-149 (in Chinese).

[5] Qi Shaoyang, Cheng Hang, Chen Fafa. Study on fault diagnosis of bearing based on stable distribution parameter estimation [J]. Journal of Taiyuan University of Technology (太原理工大学学报), 47(3): 299-303 (in Chinese).

[6] Xie D, Li B. A hierarchical deep neural network for fault diagnosis on Tennessee-Eastman process [C]. IEEE International Conference on Machine Learning and Applications, 2016.

[7] Zhang Kailin. Research on Method of TE Process Monitoring Based on Principal Component Analysis and Partial Least Squares (基于主元分析和偏最小二乘的TE过程监测方法的研究) [D]. Tianjin: Master Thesis of Tianjin University of Technology, 2015 (in Chinese).

[8] Zhu Wenying, Feng Zhipeng. Fault diagnosis of planetary gearbox based on improved empirical wavelet transform [J]. Chinese Journal of Scientific Instrument (仪器仪表学报), 2016, 37(10): 2193-2201 (in Chinese).

[9] Sun Guiling, Zhang Cuilan, Fang Yonghua, et al. A method based on wavelet transform for spectra feature extraction [J]. Chinese Journal of Quantum Electronics (量子电子学报), 2006, 23(1): 22-2(in Chinese).

[10] Zuo W, Zhang L, Song C, et al. Gradient histogram estimation and preservation for texture enhanced image denoising [J]. IEEE Transactions on Image Processing, 2014, 23(6): 2459-2472.

[11] Sun Kai, Gao Jianmin, Gao Zhiyong, et al. Plant-wide quantitative assessment of a process industry system’s operating state based on color-spectrum [J]. Mechanical Systems and Signal Processing, 2015, 60-61: 644-655.

[12] Ma Bin, Ma Degui, Liao Jiangmin. Multimodality images registration based on multi-resolution [J]. Chinese Journal of Quantum Electronics (量子电子学报), 2012, 29(1): 15-20 (in Chinese).

[13] Liu Z, Geng X, Xie Z, et al. The multi-core parallel algorithms of wavelet/wavelet packet transforms and their applications in power system harmonic analysis and data compression [J]. International Transactions on Electrical Energy Systems, 2015, 25(11): 2800-2818.

[14] Budinich R. A region-based easy-path wavelet transform for sparse image representation [J]. International Journal of Wavelets Multiresolution and Information Processing, 2017, 15(5): 1750045.

[15] Wu Yican, Bai Yunqing, Song Yong, et al. Conceptual design of China lead-based research reactor CLEAR-I [J]. Nuclear Science and Engineering (核科学与工程), 2014, 34(2): 201-208 (in Chinese).

[16] Minghan Y, Jianye W, Shuai C, et al. Development of NPP digital I & C system closed-loop online test system based on signal transmission array [J]. Progress in Nuclear Energy, 2018, 108: 270-280.

汪炜怡, 徐青山, 杨明翰. 基于光学灰度图像辨识的系统故障诊断方法[J]. 量子电子学报, 2019, 36(6): 699. WANG Weiyi, XU Qingshan, YANG Minghan. Method of system fault diagnosis based on optical gray-image recognition[J]. Chinese Journal of Quantum Electronics, 2019, 36(6): 699.

基于光学灰度图像辨识的系统故障诊断方法

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