Fault diagnosis methods based on signal analysis techniques are widely used to diagnose faults in gear and bearing. This paper introduces a fault diagnosis model that includes a multi-scale deep residual learning with a stacked long short-term memory (MDRL-SLSTM) to address sequence data in a gearbox health prediction task in an internal combustion (IC) engine. In the MDRL-SLSTM network, CNN and residual learning is firstly utilized for local feature extraction and dimension reduction. The experiment is carried out on the gearbox of an IC engine setup, two datasets are used; one is from bearing and the other from 2nd driving gear of gearbox. To reduce the number of parameters, down-sampling is carried out on input data before giving to the architecture. The model achieved better diagnostic performance with vibration data of gearbox. Classification accuracy of 94.08% and 94.33% are attained on bearing datasets and 2nd driving gear of gearbox respectively.

基于信号分析技术的故障诊断方法被广泛用于诊断齿轮和轴承的故障。本文介绍了一种故障诊断模型，该模型包括具有堆叠长短期记忆 (MDRL-SLSTM) 的多尺度深度残差学习，用于处理内燃机 (IC) 发动机变速箱健康预测任务中的序列数据。在MDRL-SLSTM网络中，首先利用CNN和残差学习进行局部特征提取和降维。实验在内燃机装置的变速箱上进行，使用了两个数据集；一个来自轴承，另一个来自变速箱的第二个驱动齿轮。为了减少参数的数量，在提供给架构之前对输入数据进行下采样。该模型利用齿轮箱的振动数据获得了更好的诊断性能。