摘要
针对现有的多变量时间序列预测方法不能适用于船舶多设备故障预测的问题,提出一种基于改进的循环神经网络和向量自回归的船舶设备故障预测方法。该方法既能够学习多个变量之间的相互依赖关系和时间序列的长期依赖关系,又有助于减轻传统神经网络对预测时间序列的输入尺度不敏感性。首先,从船舶历史数据库中提取出正常状态数据和故障状态数据,将其多变量时间序列转化为监督学习问题的输入;然后,通过注意力机制捕获船舶多变量之间复杂的相关性;接着,将注意力机制的输出同时作为循环神经网络和向量自回归的输入,分别捕获船舶时间信号的非线性关系和线性关系;最后,将循环神经网络组件和向量自回归组件的输出进行处理后作为最终预测的结果。实验结果表明,提出的预测方法在船舶设备故障预测中训练过程的稳定性高,测试结果的均方根误差低于1.2,从而能更精确地预测船舶设备属性的趋势并避免故障的发生。
Aiming at the problem that the existing multivariable time series prediction methods cannot be applied to the multi-sensor fault prediction of ships,an improved recurrent neural network and vector autoregressive fault prediction method for ships equipment is proposed.This method can not only learn the interdependence of multiple variables and the long-term dependence of time series,but also help to reduce the insensitivity of traditional neural network to the input scale of time series prediction.Firstly,the data of normal state and fault state are extracted from the ship history database and converted into the input of the supervised learning problem.Then,the complex correlation between ship variables is captured by the attention mechanism.The nonlinear and linear relationship of ship time signals are captured by inputting the output of attention mechanism into recurrent neural network and vector autoregression.Finally,the outputs of recurrent neural network components and the outputs of vector autoregressive components are processed as the final prediction results.The experimental results show that the proposed method is more stable in the training process of ship equipment fault prediction,and the root-mean-square error of the test results below 1.2.It can more accurately predict the trend of ship equipment properties and fault occurrence.
作者
曾友渝
谢强
ZENG You-yu;XIE Qiang(College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China)
出处
《计算机科学》
CSCD
北大核心
2021年第6期184-189,共6页
Computer Science
关键词
注意力机制
循环神经网络
向量自回归
故障预测
船舶设备
Attention mechanism
Recurrent neural network
Vector autoregression
Fault prediction
Ship equipment
