In the process of condition monitoring and fault diagnosis, the probabilities of failures and the probability that each fault affects overall faults is shown in the radar view int (figure 13)。 In the process of diagnosis, all the probabilities of each failure are saved to the radar data table。 When radar is monitoring, the radar data table is displayed and refreshed to the data variations, which is synchronized to the diagnosis result。

4 Conclusions

    This paper presents our design and implementation of the fault  diagnosis  model  of  fuzzy  neural  network  of  the hydraulic  system  of cranes  outriggers。  An  approach  of combining the fuzzy theory and artificial neural network is proposed。 The model's input and output signals, the range of input signal,  the  selection of membership  functions and fuzzification processing is discussed, etc。 The implementation on a software and hardware platform is elaborated。 This paper clarified  the  theoretical  basis  and  contributed  to  an

implementation method for the monitoring and fault diagnosis of hydraulic systems of crane outriggers。 And this system can also be used in other similar hydraulic systems, such as the hydraulic system of shield machine, the hydraulic system of loader machine, etc。

     References

[1] Jia Hongxia, Li Wanli, Yu Haojie。 Dynamic analysis and modeling of correction system for the hydraulic grab of underground continuous wall[J], Journal of Tongji   University:   Natural   Science   Edition,2009,37(10): 1393。

[2] Lu Jiang。 Analysis and countermeasures of the causes of accident on the operation of lifting machinery[J],Construction Decoration, 2009,10(67):11一13。

[3] Liu Binpeng。 The current situation and development trend  of  engineering  machinery  industry  of  our country[Z], The Management Learning Nets, 2010,3。 

[4]Zhao Keli, Wen Yuming。 The application of electronic technology in the hydraulic excavator[J], Construction Machinery, 1997(3):33-34。

[5] Wang Shirring, Yang Weimin, Li Tianshi, etc。 New technology,new structure and development trend of engineering   machinery   of   foreign   country[J], Engineering Machinery, 2004(1):61-66。

[6]Wang Shirring。 The current situation and development trend of failure monitoring diagnosis technology of the hydraulic  system  of  engineering  machinery[J], Machine and Hydraulic, 2009,37(2):175-180。

摘要  随着起重机液压系统越来越复杂，要求故障诊断更加快速和全面。根据起重机支腿液压系统的结构特点，本文提出了一种快速而广泛的硬件和软件体系结构模型的空调监测与故障诊断系统。在本文中,树的诊断方法和模糊神经网络理论的应用为液压系统的故障诊断提供了理论基础以及实现方法。

关键词   起重机，故障诊断，神经网络

1、引言

  汽车起重机是一种重要的工程机械。以其日趋复杂的结构和功能,它更倾向于复杂的问题,所以很难诊断起重机支架液压系统的故障。在这样的场景中,一个单一的理论或方法,无论是聪明还是经典的都不足以实现全面、准确、快捷的故障诊断。论文网

  然而,结合了两个或更多经典和智能的方法,它可能是一个准确、快速的折中诊断方法。本文利用联合诊断算法（模糊神经网络的故障诊断）为支架的起重机液压系统诊断。该算法实现了硬件平台和软件模型的联合诊断,实现了液压系统的状态监测和故障诊断。

2、建立基于模糊神经网络稳定支撑的液压系统的故障诊断模型