多传感器信息融合技术在液压系统的故障诊断中的应用.pdf

Application of multi-sensor information fusion technology on fault diagnosis of hydraulic system L Q Zhang1, 2, G L Yang1, 2, LG Zhang3 and S Y Zhang4 1 School of Energy and Power Engineering, Lanzhou University of Technology, Qilihe District, Lanzhou, 730050, China 2 Wenzhou Academy of Pump and Valve Engineering, Lanzhou University of Technology, Madao West Road, Oubei, Yongjia, Wenzhou, 325105, China 3 Handan Special Sinking Limited Company of China Coal, China Coal Fifth Construction Company, Fuxing District, Handan, 056003, China 4 Chinese Academy of Agricultural Mechanization Sciences, Chaoyang District, Beijing, 100083, China E-mail: izlq Abstract. The structural layers and methods of multi-sensor information fusion technology are analysed, and its application in fault diagnosis of hydraulic system is discussed. Aiming at hydraulic system, a model of hydraulic fault diagnosis system based on multi-sensor information fusion technology is presented. Choosing and implementing the method of information fusion reasonably, the model can fuse and calculate various fault characteristic parameters in hydraulic system effectively and provide more valuable result for fault diagnosis of hydraulic system. 1. Introduction Hydraulic system plays an important role in engineering industry. To ensure that hydraulic system is working safely, reliably and without any potential accident, its fault diagnosis is very important. But engineering practice shows that fault diagnosis based on one parameter cannot make sure whether the system is out of order or not all the time. And by multi-sensor information fusion technology, different parameters about the operating conditions of the hydraulic system from different angles can be obtained. Integrating and fusing all of the parameters effectively, the fault diagnosis of hydraulic system is successfully carried out and the fault of hydraulic system can be identified and located more accurately 1, 2. In this paper, the structural layers and methods of multi-sensor information fusion technology are analyzed, and then its application in fault diagnosis of hydraulic system is discussed. 2. Technology of multi-sensor information fusion Multi-sensor information fusion is a multilayer, allround processing procedure. It can detect, fuse, correlate, estimate and combine all of the parameters measured in hydraulic system to achieve the state estimation, including situation estimation and risk estimation of the system accurately 1, 2. To fault diagnosis system of hydraulic system, multi-sensor information fusion consists of data fusion and knowledge fusion, in addition data-to-knowledge fusion, that is data mining, is also included. 2.1. Layers of multi-sensor information fusion As shown in figure 1, information fusion can be parted into 3 layers 3. Figure 1. Schematic diagram of layer model of information fusion Information fusion of detecting layer and fault diagnosis. Information fusion of detecting layer is to fuse original information measured by the same kind sensors before their pretreatment. By this, in first time, the operating conditions of the system can be monitored intuitively and perceptually. At the same time all of the information is inputted into data base to carry data mining out. Information fusion of feature layer and fault diagnosis. Information fusion of feature layer is to fuse original information measured by all kinds sensors and related theoretical knowledge. By this the fault of hydraulic system can be identified and located, but it is all. The specific methods and technology aiming at the fault diagnosis cannot be presented here. Information fusion of decision layer and fault diagnosis. This is the fusion of the highest layer. All information measured by different kinds sensors and related theoretical knowledge are fused and the countermeasures, that is the specific methods and technology aiming at the fault diagnosis including fault isolation, redundancy controlling and so on are achieved. And if the countermeasures are proved to be workable, the experience of this typical case can also be inputted into the data base to use sometime. 2.2. Methods of multi-sensor information fusion There are many methods of multi-sensor information fusion, such as based on Bayes theory, Demper- Shafer (D-S) theory, neural network technology and some estimation theory and so on4, 5. As a modified theory of Bayes theory, D-S theory, also called evidence theory, has a wider application in multi-sensor information fusion technology. This method avoids the simple assumption to an unbeknown probability and shows the determinacy and indeterminacy of information. The basic method of D-S theory is dividing the evidence set into mutually independent parts. Each evidence part has a probability distribution function to the related theoretical diagnosis, also called belief function. Based on the fusion of different evidence and the related theoretical diagnosis, that is to integrate all of the belief functions, the total belief degree of integrated evidence based on the related theoretical knowledge can be obtained6, 7. Figure 2 shows the course of reasoning of D-S theory. Figure 2. Schematic diagram of the course of reasoning of D-S theory 3. Application of multi-sensor information fusion technology on fault diagnosis of hydraulic system 3.1. Structure and principle of the fault diagnosis system The common failure modes of hydraulic system consist of oil leakage, abrasion, corrosion, fatigue, cavitations, hydraulic pressure seizure, and impact break and so on. Thus to a hydraulic system the monitoring parameters include hydraulic pressure, flow quantity, temperature, oil leakage and so on. The fault diagnosis system of hydraulic system based on multi-sensor information fusion technology includes two function modules, data acquisition module and central process module. The data acquisition module is installed at each major component, including sensor, signal conditioning circuit, A/D convertor, and bus interface and so on, to achieve each status signal of the hydraulic system acquisition and transmission. The central process module consists of CPU and the software. Considering that the complexities of hydraulic system, use IPC as the CPU of information fusion to achieve the data analysis, fusion, fault diagnosis and giving countermeasure 7-9. Figure 3 shows the block diagram of the model of fault diagnosis system. Figure 4 shows the flow chart of the diagnostic program. 3.2. Characteristics of the fault diagnosis system All of the operating parameters, including hydraulic pressure, flow quantity, temperature, oil leakage and so on, can be monitored anytime. Based on D-S theory, by fusing all of the operating parameters measured by sensors the state recognition, the typical fault diagnosis, and the safety protection can be achieved successfully. 3.3. Key technology of the fault diagnosis system In order to make all the signals accurate, how to choose each sensor reasonably and pretreat all the signals availably. In order to make sure that the fault diagnosis is right, how to choose and implement the method of information fusion. Because the method of D-S theory may not work well under certain condition, the other methods, for example, neural network technology may be a better choice. Figure 3. Block diagram of the model of fault diagnosis system Figure 4. Flow chart of the diagnostic program 4. Conclusions Based on multi-sensor information fusion technology, the fault diagnosis system makes full use of multiple signals that can be measured from hydraulic system to realize condition alarming and diagnosis of the hydraulic systems. This can increase work efficiency and reliability of the hydraulic systems. The model of fault diagnosis system of hydraulic system presented in this paper is a generalized model. In specific engineering practice the monitoring parameters and the actual structure and implementation of the fault diagnosis system depend on the corresponding hydraulic system. Acknowledgments We would like to thank the support of Science and Technology Project of Wenzhou City (H20110007) and Natural Science Fund of Gansu Province (1014RJZA023). References 1 Varshney P K 1997 Multi-sensor Data Fusion Electronics& Communication Engineering Jo