Intelligent fault diagnosis methods based on signal analysis have been widely used for bearing fault diagnosis. These methods use a pre-determined transformation (such as empirical mode decomposition, fast Fourier transform, discrete wavelet transform) to convert time-series signals into frequency domain signals, the performance of dignostic system is significantly rely on the extracted features. However, extracting signal characteristic is fairly time consuming and depends on specialized signal processing knowledge. Although some studies have developed highly accurate algorithms, the diagnostic results rely heavily on large data sets and unreliable human analysis. This study proposes an automatic feature learning neural network that utilizes raw vibration signals as inputs, and uses two convolutional neural networks with different kernel sizes to automatically extract different frequency signal characteristics from raw data. Then long short-term memory was used to identify the fault type according to learned features. The data is down-sampled before inputting into the network, greatly reducing the number of parameters. The experiment shows that the proposed method can not only achieve 98.46% average accuracy, exceeding some state-of-the-art intelligent algorithms based on prior knowledge and having better performance in noisy environments.

基于信号分析的智能故障诊断方法已被广泛用于轴承故障诊断。这些方法使用预定的变换（例如经验模式分解，快速傅立叶变换，离散小波变换）将时间序列信号转换为频域信号，诊断系统的性能在很大程度上取决于提取的特征。但是，提取信号特征相当耗时，并且取决于专门的信号处理知识。尽管一些研究已经开发出高度准确的算法，但是诊断结果在很大程度上依赖于大数据集和不可靠的人体分析。这项研究提出了一种利用原始振动信号作为输入的自动特征学习神经网络，并使用两个具有不同内核大小的卷积神经网络从原始数据中自动提取不同的频率信号特征。然后根据学习到的特征，使用很长的短期记忆来识别故障类型。数据在输入网络之前先进行下采样，从而大大减少了参数数量。实验表明，该方法不仅可以达到98.46％的平均精度，而且超过了一些基于先验知识的最新智能算法，并且在嘈杂的环境中具有更好的性能。