面向泵车的故障诊断技术研究
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摘要
混凝土泵车是工程机械中技术含量高、维护难度大、价格较昂贵的复杂装备之一。利用先进的物联网技术和人工智能故障诊断技术对泵车进行实时、远程、在线故障诊断，对于保障重点行业大型装备运营的技术安全，实现节能降耗和绿色环保，提高制造业的可持续发展能力具有重要的意义。
本文针对目前泵车液压系统中的故障诊断问题，给出了基于物联网的泵车液压故障诊断系统方案；在综合考虑提高系统实时性、可靠性以及降低系统成本的情况下，完成了面向物联网体系结构的安全车载终端系统的搭建，并以智能故障诊断技术为理论基础，研究了适合该研究背景的智能诊断算法，将人工神经网络、微粒群优化算法、D-S证据理论、多传感信息融合理论、模糊向量机等智能信息处理方法引入泵车液压系统的故障诊断中，分别对液压系统中液压机控制子系统和动力子系统关键部位进行故障诊断。
本文的主要创新点如下：
(1)给出了一种基于物联网的泵车液压故障诊断系统方案，从终端平台安全方面考虑，将TPM芯片加入到车载终端中，使得车载终端具有更高的安全性。
(2)提出了一种基于PSO-Elman神经网络的故障诊断方法。通过对PSO算法的惯性权重和学习因子进行改进，应用于Elman神经网络的训练学习中，使得网络在训练时间、收敛率和诊断精度方面得到提高；给出了基于PSO-H-BP神经网络的故障诊断方法，将PSO与Hopfield神经网络和BP神经网络相结合，利用PSO算法优化Hopfield网络的权值矩阵，对BP网络的输入数据进行预处理，获得稳定的网络结构，再利用BP神经网络进行故障诊断，提高网络的收敛速度和诊断准确度。最后通过实验验证了算法的有效性。
(3)提出了一种基于双层FSVM模型结构的故障诊断方法，并将其应用于液压系统电磁换向阀故障诊断中，取得了良好效果；同时对模糊支持向量机的训练算法和参数选取方法进行了优化，实验结果表明，采用优化后的参数可明显提高支持向量机的学习性能。
(4)提出了一种基于三级多源信息融合的故障诊断方法，采用多并行的PSO-BP和MPSO-RBF神经网络组成振动子网和温度子网进行局部诊断；给出了基于修正的D-S证据理论的多传感器时空域信息融合方法，针对本文研究背景提出了基于pl&bl的决策方法；最后，将三级多源信息融合故障诊断方法应用于液压系统动力子系统关键部位液压泵的故障诊断中进行验证。
Concrete pump truck is one of the complex equipment which is high technical content、huge difficult maintenance and more expensive in engineering machinery. It has important significance to diagnose the pump fault with real-time、remote and online through using the advanced things technology and artificial intelligence fault diagnostic technology in ensuring the technical security of the key industries large equipment operatoring, achieving the energy saving and environmental protection, and improving the manufacturing ability of the sustainable development.
In this paper, in consideration of the current pump hydraulic system fault diagnostic problem, the pump hydraulic fault diagnostic system based on Internet of Things is proposed; In the case of considering to improve the real-time and reliability of the system and reduce cost of the system comprehensively, the paper completes the establishing of the security vehicle terminal system which is facing to the Internet of Things structures, what's more,the paper studies the intelligent diagnostic algorithm which are suitable for the paper background and uses the intelligent diagnostic technology as the theoretical basis, the intelligent information processing method include artificial neural networks, particle swarm optimization algorithm, D-S evidence theory, multi-sensor information fusion theory and fuzzy vector machines are introduced into the pump hydraulic system fault diagnosis. The control subsystem and power subsystem key parts of the hydraulic system of the pump are respectively diagnosed. The main innovations of this paper are as follows:
(1) The paper proposes a fault diagnostic system solutions of hydraulic system of the pump based on Internet of things, taking the terminal platform security into considered, the TPM chip is added to the vehicle terminal in order to make the vehicle terminal have higher security.
(2) This paper presents a fault diagnosis method based on PSO-Elman neural network.Through improving the inertia weight and learning factors of PSO algorithm and applying it to the training learning of Elman neural network, the Elman neural network gets better in terms of training time, the convergence rate and diagnostic accuracy; this paper puts forward a fault diagnosis method based on PSO-H-BP neural network, the method combinate the method of PSO algorithm、 Hopfield neural network and BP neural network. First, using the PSO algorithm to optimize the Hopfield network to obtain the stable network structure, and then using the Hopfield network to preprocess the input data of BP network, finally, using the BP neural network to diagnose the fault, this method has improved the speed of convergence and diagnostic accuracy of the network. Finally, the effectiveness of the algorithm is verified by experiments.
(3) The paper presents a fault diagnosis method based on double-layer FSVM model structure, and the method is applied to the hydraulic system solenoid valve fault diagnosis, it achieves good results; Additionally,in this method, the training algorithms and parameters selection method of fuzzy support vector machine are optimized, the experimental results show that learning performance of the support vector machine can significantly improve by using the optimized parameters.
(4) The paper puts forward a fault diagnosis method based on the information fusion model with three fusion levels,it adopts multiple PSO-BP and MPSO-RBF neural network to compose the vibration subnet and temperature subnet which is make used of local diagnosis; According to the study background the multi-sensor information fusion method based on the modified D-S evidence theory is proposed and the paper puts forward a decisions method based the pl&bl; finally, the three levels multi-source information fusion fault diagnostic method is used in the key parts of the hydraulic system power subsystem, this study has been verified.
本文针对目前泵车液压系统中的故障诊断问题，给出了基于物联网的泵车液压故障诊断系统方案；在综合考虑提高系统实时性、可靠性以及降低系统成本的情况下，完成了面向物联网体系结构的安全车载终端系统的搭建，并以智能故障诊断技术为理论基础，研究了适合该研究背景的智能诊断算法，将人工神经网络、微粒群优化算法、D-S证据理论、多传感信息融合理论、模糊向量机等智能信息处理方法引入泵车液压系统的故障诊断中，分别对液压系统中液压机控制子系统和动力子系统关键部位进行故障诊断。
本文的主要创新点如下：
(1)给出了一种基于物联网的泵车液压故障诊断系统方案，从终端平台安全方面考虑，将TPM芯片加入到车载终端中，使得车载终端具有更高的安全性。
(2)提出了一种基于PSO-Elman神经网络的故障诊断方法。通过对PSO算法的惯性权重和学习因子进行改进，应用于Elman神经网络的训练学习中，使得网络在训练时间、收敛率和诊断精度方面得到提高；给出了基于PSO-H-BP神经网络的故障诊断方法，将PSO与Hopfield神经网络和BP神经网络相结合，利用PSO算法优化Hopfield网络的权值矩阵，对BP网络的输入数据进行预处理，获得稳定的网络结构，再利用BP神经网络进行故障诊断，提高网络的收敛速度和诊断准确度。最后通过实验验证了算法的有效性。
(3)提出了一种基于双层FSVM模型结构的故障诊断方法，并将其应用于液压系统电磁换向阀故障诊断中，取得了良好效果；同时对模糊支持向量机的训练算法和参数选取方法进行了优化，实验结果表明，采用优化后的参数可明显提高支持向量机的学习性能。
(4)提出了一种基于三级多源信息融合的故障诊断方法，采用多并行的PSO-BP和MPSO-RBF神经网络组成振动子网和温度子网进行局部诊断；给出了基于修正的D-S证据理论的多传感器时空域信息融合方法，针对本文研究背景提出了基于pl&bl的决策方法；最后，将三级多源信息融合故障诊断方法应用于液压系统动力子系统关键部位液压泵的故障诊断中进行验证。
Concrete pump truck is one of the complex equipment which is high technical content、huge difficult maintenance and more expensive in engineering machinery. It has important significance to diagnose the pump fault with real-time、remote and online through using the advanced things technology and artificial intelligence fault diagnostic technology in ensuring the technical security of the key industries large equipment operatoring, achieving the energy saving and environmental protection, and improving the manufacturing ability of the sustainable development.
In this paper, in consideration of the current pump hydraulic system fault diagnostic problem, the pump hydraulic fault diagnostic system based on Internet of Things is proposed; In the case of considering to improve the real-time and reliability of the system and reduce cost of the system comprehensively, the paper completes the establishing of the security vehicle terminal system which is facing to the Internet of Things structures, what's more,the paper studies the intelligent diagnostic algorithm which are suitable for the paper background and uses the intelligent diagnostic technology as the theoretical basis, the intelligent information processing method include artificial neural networks, particle swarm optimization algorithm, D-S evidence theory, multi-sensor information fusion theory and fuzzy vector machines are introduced into the pump hydraulic system fault diagnosis. The control subsystem and power subsystem key parts of the hydraulic system of the pump are respectively diagnosed. The main innovations of this paper are as follows:
(1) The paper proposes a fault diagnostic system solutions of hydraulic system of the pump based on Internet of things, taking the terminal platform security into considered, the TPM chip is added to the vehicle terminal in order to make the vehicle terminal have higher security.
(2) This paper presents a fault diagnosis method based on PSO-Elman neural network.Through improving the inertia weight and learning factors of PSO algorithm and applying it to the training learning of Elman neural network, the Elman neural network gets better in terms of training time, the convergence rate and diagnostic accuracy; this paper puts forward a fault diagnosis method based on PSO-H-BP neural network, the method combinate the method of PSO algorithm、 Hopfield neural network and BP neural network. First, using the PSO algorithm to optimize the Hopfield network to obtain the stable network structure, and then using the Hopfield network to preprocess the input data of BP network, finally, using the BP neural network to diagnose the fault, this method has improved the speed of convergence and diagnostic accuracy of the network. Finally, the effectiveness of the algorithm is verified by experiments.
(3) The paper presents a fault diagnosis method based on double-layer FSVM model structure, and the method is applied to the hydraulic system solenoid valve fault diagnosis, it achieves good results; Additionally,in this method, the training algorithms and parameters selection method of fuzzy support vector machine are optimized, the experimental results show that learning performance of the support vector machine can significantly improve by using the optimized parameters.
(4) The paper puts forward a fault diagnosis method based on the information fusion model with three fusion levels,it adopts multiple PSO-BP and MPSO-RBF neural network to compose the vibration subnet and temperature subnet which is make used of local diagnosis; According to the study background the multi-sensor information fusion method based on the modified D-S evidence theory is proposed and the paper puts forward a decisions method based the pl&bl; finally, the three levels multi-source information fusion fault diagnostic method is used in the key parts of the hydraulic system power subsystem, this study has been verified.
引文
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