Article | 02-April-2018
In order to improve the work performance and mobility of the fault diagnosis expert system, a portable diagnostic client for embedded Linux operating system is developed. The data acquisition module is embedded in the device by analyzing fault-tree based troubleshooting process, using the fault symptom table for fault diagnosis. Maintenance staff determine whether the equipment is working properly in accordance with the output signal of the diagnostic Agent which is embedded in weapons
Geng Chaoyang,
Gao Fenli
International Journal of Advanced Network, Monitoring and Controls, Volume 1 , ISSUE 2, 25–33
Article | 01-June-2016
Instrument technology based on DataSocket and its application in equipment remote condition monitoring and fault diagnosis. DataSocket’s properties, structure and working principle were analyzed at the same time. By developing a set of equipment remote status monitoring and fault diagnosis system, the paper has tried to verify that the use of this technology can monitor real-time data and fault diagnosis.
Wu Tao,
Liu Zhihua,
Liang Miaoyuan
International Journal on Smart Sensing and Intelligent Systems, Volume 9 , ISSUE 2, 491–508
Research Article | 10-April-2013
A fault diagnosis method based on adaptive dynamic clone selection neural network (ADCSNN) is proposed in this paper. In this method the weights of neural network is encoded as the antibody, and the network error is considered as the antigen. The algorithm is then applied to fault detection of motor equipment. The experiments results show that the fault diagnosis method based on ADCS neural network has the capability in escaping local minimum and improving the algorithm speed, this gives better
Wu Hongbing,
Lou Peihuang,
Tang Dunbing
International Journal on Smart Sensing and Intelligent Systems, Volume 6 , ISSUE 2, 482–504
Article | 02-April-2018
In order to improve the correct rate of analog circuit fault diagnosis, a method based on SVM optimized with BQPSO is put forward. Firstly, BQPSO algorithm and its steps are presented; Then the performance impact factors of SVM are analyzed, and the steps of SVM parameters optimized with BQPSO are given; Finally, a filter circuit is taken as an example to simulate. The result shows that this method is effective.
Wang Zhongsheng,
Yang Sen,
Huang Shujuan
International Journal of Advanced Network, Monitoring and Controls, Volume 1 , ISSUE 2, 53–58
Research paper | 20-February-2013
This paper presents data fusion algorithm of fault diagnosis considering sensor measurement uncertainty. Random-fuzzy variables (RFV) are used to model testing patterns (TPs) and fault template patterns (FTPs) respectively according to on-line sensor monitoring data and typical historical sensor data reflecting every fault mode. A similarity measure is given to calculate matching degree between a TP and each FTP in fault database such that Basic Probability Assignment (BPA) can be obtained by
Xu Xiaobin,
Zhou Zhe,
Wen Chenglin
International Journal on Smart Sensing and Intelligent Systems, Volume 6 , ISSUE 1, 171–190
Article | 01-September-2012
) corrosion and (iii) excessive wear. The features of the PSD values of vibration signals were extracted using statistical and vibration parameters. The extracted features were used as inputs to the KNN and SVM for three-class identification. The roles of PSD technique and the KNN and SVM classifiers were investigated. Results showed that the accuracy rate of fault diagnosis was 100%. Also, the results demonstrated that the combined PSD-SVM model had the potential for fault diagnosis of engine journal
A. Moosavian,
H. Ahmadi,
A. Tabatabaeefar,
B. Sakhaei
International Journal on Smart Sensing and Intelligent Systems, Volume 5 , ISSUE 3, 685–700
Article | 12-April-2018
relationship were established through the adaptive network-based fuzzy inference system（ANFIS）, to achieve the machine tool spindle fault diagnosis. The results indicate that the roughness characteristic can accurately diagnose the machine tool spindle fault and can be an effective method to study the spindle fault of the machine tool.
Zhou Guang-wen,
Mao Chun-yu,
Tian Mei,
Sun Yan-hong
International Journal of Advanced Network, Monitoring and Controls, Volume 2 , ISSUE 3, 98–102
Article | 07-May-2018
Hu Nannan,
Su Xiaohao,
Baolong Liu
International Journal of Advanced Network, Monitoring and Controls, Volume 3 , ISSUE 1, 115–121