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Structure-based connectionist network for fault diagnosis of helicopter gearboxes.

机译:基于结构的连接器网络,用于直升机齿轮箱的故障诊断。

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A diagnostic method is introduced for helicopter gearboxes that uses the gearbox structure and characteristics of the 'features' of vibration to define the influences of faults on features. The structural influences in this method are defined based on the root mean square value of vibration obtained from a simplified lumped-mass model of the gearbox. Featural influences characterize the frequency-specific information of the vibration features which correspond to the type of gearbox faults the features represent. These influences are defined as fuzzy variables to account for the approximate nature of the simplified model of the gearbox. The fuzzy structural and featural influences are then incorporated as the weights of a connectionist network for diagnosis, so as to avoid supervised training of the network. Diagnosis in this Structure-Based Connectionist Network (SBCN) is performed by propagating the abnormal features through the weights of SBCN to obtain fault possibility values for the components in the gearbox.; In the proposed diagnostic method, vibration features obtained from raw vibration are first utilized by an unsupervised Fault Detection Network (FDN) for identifying the presence of faults. Fault diagnosis is then performed by SBCN only if the presence of a fault is prompted by FDN. Since SBCN uses abnormal vibration features as inputs, an unsupervised pattern classifier is designed for abnormality-scaling of features. The abnormality-scaled features are then propagated through the weights of SBCN for isolating faulty components.; The proposed diagnostic method is experimentally evaluated in application to two helicopter gearboxes: OH-58A and S-61. Experimental vibration data for the OH-58A gearbox were collected at the NASA Lewis Research Center, and vibration data from three S-61 gearboxes rejected in field operation were collected at Sikorsky Aircraft. The proposed method is evaluated in diagnosis of the OH-58A gearbox faults as well as isolating the faults within the three S-61 gearboxes. The diagnostic results indicate that the SBCN is able to correctly diagnose about 80% of the OH-58A gearbox faults and all the faults in S-61 gearboxes. In addition to evaluation of the structural influences based on diagnostic results, they are validated by comparing them with influences obtained from experimental RMS values as well as the weights of a neural network structurally similar to SBCN, but trained through supervised learning. Moreover a sensitivity analysis is performed to study the effect of variations in structural influences on diagnostic results. The structural influences developed in this method can also be utilized for assessing the importance of various gearbox accelerometers in diagnosis. Three indices are defined based on the structural influences to quantify various aspects of accelerometer significance and are evaluated using the data from the OH-58A gearbox.
机译:针对直升机齿轮箱引入了一种诊断方法,该方法使用齿轮箱结构和振动“特征”的特征来定义故障对特征的影响。该方法的结构影响是根据从齿轮箱的简化集总质量模型获得的振动的均方根值定义的。固有的影响表征了振动特征的特定于频率的信息,这些信息与特征所代表的齿轮箱故障的类型相对应。将这些影响定义为模糊变量,以说明齿轮箱简化模型的近似性质。然后,将模糊的结构和特性影响作为连接器网络的权重进行合并,以进行诊断,从而避免对网络进行有监督的训练。通过基于SBCN的权重传播异常特征来获取变速箱中组件的故障可能性值,从而进行基于结构的连接器网络(SBCN)的诊断。在提出的诊断方法中,首先由无监督故障检测网络(FDN)利用从原始振动获得的振动特征来识别故障的存在。只有当FDN提示存在故障时,SBCN才会执行故障诊断。由于SBCN使用异常振动特征作为输入,因此设计了无监督模式分类器来对特征进行异常缩放。然后,通过SBCN的权重传播异常缩放的特征,以隔离故障组件。对两种直升机变速箱的应用进行了实验评估,提出了诊断方法:OH-58A和S-61。在NASA刘易斯研究中心收集了OH-58A变速箱的实验振动数据,并在Sikorsky飞机上收集了在野外操作中被拒绝的三个S-61变速箱的振动数据。该方法在诊断OH-58A变速箱故障以及隔离三个S-61变速箱内的​​故障方面得到了评估。诊断结果表明,SBCN能够正确诊断大约80%的OH-58A变速箱故障以及S-61变速箱中的所有故障。除了根据诊断结果评估结构影响之外,还可以通过将它们与实验RMS值以及结构类似于SBCN但通过监督学习进行训练的神经网络的权重进行比较,来对它们进行验证。此外,进行敏感性分析以研究结构影响的变化对诊断结果的影响。这种方法产生的结构影响也可以用于评估各种齿轮箱加速度计在诊断中的重要性。根据结构影响定义了三个指标,以量化加速度计重要性的各个方面,并使用OH-58A变速箱的数据进行评估。

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