研究基于大数据的舰船故障趋势估计方法，提升故障趋势估计效果。利用改进的注意力机制，提取舰船各设备历史运行数据的多变量时间序列；在自回归预测模型内，输入多变量时间序列，输出舰船故障趋势估计值；在小波神经网络内，输入多变量时间序列，输出舰船故障趋势估计值，通过梯度修正法调整小波神经网络参数，降低故障趋势估计误差；计算自回归预测模型和小波神经网络，输出的故障趋势估计值的均值，作为最终故障趋势估计结果。实验证明：该方法可精准估计舰船故障趋势；发生不同故障时，该方法依旧能够精准估计故障趋势；舰船各设备故障趋势估计的赤池信息准则值较低，具备较优的故障趋势估计效果。
The ship fault trend estimation method based on big data is studied to improve the effect of fault trend estimation. Using the improved attention mechanism, the multivariable time series of the historical operation data of each ship equipment is extracted. In the autoregressive prediction model, the multivariable time series is input and the ship fault trend estimation is output. In the wavelet neural network, the multivariable time series is input, the ship fault trend estimation value is output, and the parameters of the wavelet neural network are adjusted by the gradient correction method to reduce the fault trend estimation error. The average value of the estimated fault trend is calculated by the autoregressive prediction model and the wavelet neural network, which is used as the final fault trend estimation result. Experiments show that this method can accurately estimate the ship fault trend. When different faults occur, the method can still accurately estimate the fault trend. The red pool information criterion value of ship equipment fault trend estimation is low, which has better fault trend estimation effect.
2022,44(16): 159-162 收稿日期：2022-01-30
DOI：10.3404/j.issn.1672-7649.2022.16.034
分类号：TP206
基金项目：中国博士后科学基金资助项目（2019M844）；江苏省高等学校优秀科技创新团队（海事大数据优秀科技创新团队）项目（苏教科函〔2019〕7号）；江苏省青蓝工程中青年学术带头人项目（苏教师〔2020〕10号）
作者简介：蒋玉婷(1981-)，女，硕士，副教授，研究方向为计算机应用、计算机软件、大数据及云计算等
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