新型调制窗-RBF神经网络及其在大型衡器容错中的应用研究

A large-scale weighing instrument (LSWI) is the key apparatus of measuring bulk goods, and it is a typical complex and nonlinear multi-sensor system. LSWI is not a fault-tolerant system, and its faults cannot be detected online. Therefore, it does not work if any of its load cells is faulty, which is one of the most urgent problems of the large weighing filed. The model of fault detection and fault tolerance for LSWI based on the conventional neural network(NN) will encounter several bottlenecks because of the NN’s complexity and the number of the samples for training an NN. The aim of this project is to study a new modulation window radial basis neural network (MW-RBFNN) and its optimization algorithms, and an approach of fault diagnosis and fault tolerance for LSWI with this new NN. The major works include: ①constructing a small-calculation and fast-response modulation window function to meet the NN’s activation function, and the constraints for optimizing an NN based on the system’s prior knowledge, and then creating the modulation window radial basis neural network and its optimization algorithm; ②designing a programme of detecting the LSWI’s load cells online without state sensors, and establishing the model of the fault diagnosis (FD) and fault-tolerant measurement (FTM) for LSWI that is applied to accurately weighing loads when the LSWI is faulty. Based on this project, a new theory and method of fault diagnosis and fault-tolerant measurement for LSWI will be obtained, which will solve this proposed problem of LSWI, and improve the LSWI’s reliability. In addition, a new modulation window radial basis neural network (MW-RBFNN) and its optimization algorithms will be established, which will break through the limitations of the calculation and the generalization ability in a convention neural network, and will provide a new ideal for optimizing, simplifying and implementing neural networks.

大型衡器是大宗货物称重计量的关键设备，是典型的复杂非线性多传感器系统，因不具备故障在线检测与容错功能，任一传感器故障都将导致称重系统失效，是目前大型称重领域亟待解决的难题；受计算复杂性和样本数量等限制，基于传统神经网络方法的大型衡器故障检测与容错建模将遭遇若干瓶颈问题。本项目提出一种新型调制窗-RBF神经网络及优化算法，在此基础上研究大型衡器故障诊断与容错方法，主要包括：①构造新型调制窗激励函数，构建基于先验知识的约束条件，建立调制窗-RBF神经网络及优化算法，减少运算量、提高泛化能力；②构建一种基于无附加传感器的称重传感器状态在线检测方案，建立大型衡器故障诊断和容错测量模型，实现故障状态下的准确称量。这种故障诊断与容错测量方法可解决大型衡器称重难题，提高称量可靠性；形成的调制窗-RBF神经网络及优化算法，可望突破传统神经网络计算量与泛化能力的局限，为神经网络优化与实用性研究开辟新思路。

大型衡器(汽车衡、轨道衡等)是大宗货物称重计量的关键设备，称量范围从几吨到上千吨，广泛应用于仓储贸易、交通运输等部门。大型衡器多路称重传感器输出相互关联，具有强耦合性与高度非线性，是典型的复杂非线性多传感器系统。现有大型衡器因不具备故障在线检测与容错功能，任一传感器发生故障都将导致称重系统失效，是目前大型称重领域亟待解决的难题。因此，本项目主要开展大型衡器多传感器故障诊断与智能容错方法研究，主要包括：（1）针对大型衡器实际工况中不便增加额外的监测用传感器的问题，提出了一种无附加传感器的称重传感器在线检测方案，利用多传感器拓扑结构和关联特性，构建了基于约束条件下神经网络的称重传感器状态观测器，提高了系统泛化能力，为大型衡器故障诊断与容错测量奠定了基础；（2）针对传统RBF神经网络计算复杂性与拟合精度不能兼顾的矛盾，以升余弦-RBF函数为基础，通过构造一种简单的、运算量小、形状可调的调制窗函数作为神经网络激励函数，创造性地提出了一种新型调制窗-RBF神经网络（MW-RBFNN），验证了MW-RBFNN的有效性；（3）提出并实现了几种大型衡器故障诊断方法，包括基于递推主元分析的汽车衡称重传感器故障诊断方法、基于CoifN小波变换的汽车衡称重传感器突变故障检测方法、基于先验知识约束的神经网络汽车衡故障诊断方法等，实现了故障状态下的汽车衡准确称量，为提高称重系统可靠性与准确性提供了独特的解决方案；（4）构建了大型衡器多传感器故障诊断实验平台，为算法研究提供了验证平台，也为其工程化应用奠定了基础。本项目提出的新型调制窗-RBF神经网络，是对传统RBF神经网络的重要改进，为神经网络优化与实用性研究开辟了一种新思路；提出的大型衡器故障诊断与容错测量新方法，解决了大型衡器故障诊断的难题，为提高称量可靠性提供了有效的解决方案，对其他多传感器测控系统的容错控制具有重要借鉴价值。