基于自动编码器和长短时记忆网络的智能汽车故障诊断方法研究

智能汽车故障诊断技术对于保障智能汽车安全行驶具有重要意义，本文针对智能汽车传感器数据异常检测和车辆运动的异常检测提出了一系列故障诊断方法. 针对非时序传感器数据，采用基于超限学习框架的自动编码器，对正常数据进行特征压缩学习其特征表示，再利用压缩的特征重构数据，根据重构误差的大小判断数据是否异常. 针对时序传感器数据，采用多层长短时记忆网络学习时序数据之间的时间依赖关系来预测当下时刻的数据值，根据预测误差的大小判断数据是否异常. 提出一种阈值随误差大小动态变化的自适应阈值确定方法，使得决策变量对于异常值相对敏感. 进一步地，采用车辆自行车运动学模型和Kalman滤波，利用Jarque-Bera测试对预测值和量测值残差的正态性进行检验来检测车辆运动是否异常. 实际场地测试验证了本文所提出的方法可以有效检测非时序或时序传感器数据的异常，并对车辆运动是否异常进行检测.

Autoencoder and LSTM based fault diagnosis for intelligent vehicles

Fault diagnosis for intelligent vehicles is of great significance to ensure the safe driving. This paper proposes a series of fault diagnosis methods aiming at anomaly detection for sensor data and vehicle motion of intelligent vehicles. For the non-sequential sensor data, extreme learning machine based autoencoder is utilized to compress the normal data instances to learn the feature representation, and then reconstruct the data using the compressed feature. Whether an instance is normal or not is decided in accordance with the reconstruction error. To detect the anomaly in the sequential sensor data, multi-layer long-short time memory network is adopted to learn the time adherence of the sequential data to predict the current data value, and whether the data is normal or not is judged according to the prediction error. Besides, an adaptive threshold calculation method is proposed, where the threshold dynamically changes with the reconstruction error or prediction error and enable the decision variable sensitive to the anomaly. Furthermore, to detect whether the vehicle motion is abnormal, the vehicle bicycle kinematic model and Kalman filter are adopted and the normality of the residuals between the estimated and measured values is checked using Jarque-Berra test. The experiments verifies that the methods proposed in this paper can effectively detect the anomaly in the non-sequential or sequential sensor data, and detect the abnormality of the vehicle motion.