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Rotorcraft virtual sensors via deep regression

机译:通过深度回归的旋翼飞机虚拟传感器

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Raw sensor data containing high-fidelity information is highly desirable for valuable post-processing. We developed a machine learning model that performs deep regression to infer rotorcraft component vibration spectra from a few flight conditional indicators (CI). The model consists of a deep neural network of fully connected layers (DNN) that performs high-dimensional and non-linear multivariate regression to reconstruct raw accelerometer data. The network architecture hyperparameters were optimized using an evolutionary genetic algorithm (GA) that was more effective than random and manual search methods. The best GA design was further tuned to achieve spectrum reconstruction accuracies above 95% on validation datasets. An automated model generator workflow was developed to train and evaluate thousands of DNN designs using parallel asynchronous execution on a Cray XC50, which were monitored and studied. Finally, as a verification step of the DNN inference model operation and performance, a detailed sensitivity analysis was performed using a modified Sobol sampling technique to understand response behavior and limitations. The sensitivity analysis method utilized Dask-distributed across multiple nodes on our HPC to evaluate millions of generated samples in parallel. Published by Elsevier Inc.
机译:对于有价值的后处理,非常需要包含高保真信息的原始传感器数据。我们开发了一种机器学习模型,该模型执行深度回归以从一些飞行条件指示器(CI)推断旋翼飞机组件的振动谱。该模型由一个全连接层(DNN)的深度神经网络组成,该网络执行高维和非线性多元回归以重建原始加速度计数据。使用比随机和手动搜索方法更有效的进化遗传算法(GA)对网络体系结构超参数进行了优化。进一步优化了最佳GA设计,以在验证数据集上实现95%以上的频谱重建精度。开发了一个自动模型生成器工作流程,以使用在Cray XC50上进行并行异步执行来训练和评估成千上万的DNN设计,并对其进行了监视和研究。最后,作为DNN推理模型操作和性能的验证步骤,使用改进的Sobol采样技术进行了详细的灵敏度分析,以了解响应行为和限制。敏感性分析方法利用分布在我们HPC上多个节点上的Dask分布来并行评估数百万个生成的样本。由Elsevier Inc.发布

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