In this paper, we propose a novel fault diagnosis (FD) approach for micro-electromechanical systems (MEMS) inertial sensors that recognize the fault patterns of MEMS inertial sensors in an end-to-end manner. We use a convolutional neural network (CNN)-based data-driven method to classify the temperature-related sensor faults in unmanned aerial vehicles (UAVs). First, we formulate the FD problem for MEMS inertial sensors into a deep learning framework. Second, we design a multi-scale CNN which uses the raw data of MEMS inertial sensors as input and which outputs classification results indicating faults. Then we extract fault features in the temperature domain to solve the non-uniform sampling problem. Finally, we propose an improved adaptive learning rate optimization method which accelerates the loss convergence by using the Kalman filter (KF) to train the network efficiently with a small dataset. Our experimental results show that our method achieved high fault recognition accuracy and that our proposed adaptive learning rate method improved performance in terms of loss convergence and robustness on a small training batch.

中文翻译：

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使用基于 CNN 的数据驱动方法的 MEMS 惯性传感器故障诊断

在本文中，我们提出了一种用于微机电系统 (MEMS) 惯性传感器的新型故障诊断 (FD) 方法，该方法以端到端的方式识别 MEMS 惯性传感器的故障模式。我们使用基于卷积神经网络 (CNN) 的数据驱动方法对无人机 (UAV) 中与温度相关的传感器故障进行分类。首先，我们将 MEMS 惯性传感器的 FD 问题表述为深度学习框架。其次，我们设计了一个多尺度 CNN，它使用 MEMS 惯性传感器的原始数据作为输入，并输出指示故障的分类结果。然后我们在温度域中提取故障特征来解决非均匀采样问题。最后，我们提出了一种改进的自适应学习率优化方法，该方法通过使用卡尔曼滤波器 (KF) 来使用小数据集有效地训练网络来加速损失收敛。我们的实验结果表明，我们的方法实现了很高的故障识别精度，并且我们提出的自适应学习率方法在小批量训练的损失收敛和鲁棒性方面提高了性能。