The invention provides an intelligent diagnosis method for a load fault of a ship vibration isolation device, and aims to solve the current situation that fault prediction is not performed on a load object based on data information of the vibration isolation device in the prior art and the problem that an existing neural network model is low in prediction precision due to few fault samples. The method mainly comprises the steps of obtaining a training sample set, optimizing a sample data set and a neural network model parameter weight and the like, the method automatically obtains an initial working parameter sample of the vibration isolation device in real time, optimizes the sample data set and the neural network model parameter weight through a preset Bayesian network and an MCMC algorithm, and obtains the initial working parameter sample of the vibration isolation device. And the preset neural network model perfects the training sample confidence coefficient and the model parameter distribution through the Bayesian network and the MCMC algorithm, so that the fault information of the load equipment of the vibration isolation device is obtained based on the preset neural network model trained by the method, and the fault detection accuracy of the load equipment of the vibration isolation device is improved. The accuracy and timeliness of fault prediction can be effectively improved.
本发明提供了一种船舶隔振装置载荷故障智能诊断方法,旨在解决现有技术未基于隔振装置数据信息对载荷对象进行故障预测的现状,以及现有神经网络模型由于故障样本较少导致的预测精度低的问题。本发明主要包括获取训练样本集、优化样本数据集和神经网络模型参数权重等步骤,该方法实时自动获取隔振装置的初始工作参数样本,分别通过预设贝叶斯网络和MCMC算法,优化样本数据集和神经网络模型参数权重,得到新的预设神经网络模型,而由于该预设神经网络模型经由贝叶斯网络和MCMC算法完善了训练样本置信度和模型参数分布,因此,基于上述方法训练出来的预设神经网络模型获取隔振装置载荷设备的故障信息,可以有效提高故障预测的准确度和及时性。
Intelligent diagnosis method for load fault of ship vibration isolation device
一种船舶隔振装置载荷故障智能诊断方法
22.11.2024
Patent
Elektronische Ressource
Chinesisch
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