The multi-sensor data fusion based data-driven fault diagnosis method is a promising approach to detect faults of complex systems. However, in the actual industrial environment, the sampling rate of different sensors is often inconsistent. In order to apply this kind of data to fault diagnosis, the traditional methods are to preprocess it and convert it into single sampling rate data. However, these methods are all machine learning methods, which rely on manual feature extraction. To the best of our knowledge, few works have used deep learning (DL) methods to solve this problem. To fill this gap, a novel multi-rate sampling data fusion method for fault diagnosis is proposed in this paper. In the proposed method, signals with different sampling rates are fused. First, a convolutional neural network (CNN) is adopted to learn features from raw data automatically. Then, a long short-term memory (LSTM) network is utilized to mine the time correlation in extracted features and encode the temporal information. The methodology is validated on a public experimental dataset and data from a real industrial scenario. The proposed method is compared with some state-of-the-art machine learning (ML) and DL methods, the results show that the proposed method can distinguish different conditions satisfactorily and has the best diagnostic accuracy among all comparison methods.

基于多传感器数据融合的数据驱动故障诊断方法是一种很有前景的复杂系统故障检测方法。然而，在实际工业环境中，不同传感器的采样率往往不一致。为了将这类数据应用于故障诊断，传统的方法是对其进行预处理并将其转换为单采样率数据。但是，这些方法都是机器学习方法，依赖于人工提取特征。据我们所知，很少有作品使用深度学习（DL）方法来解决这个问题。为了填补这一空白，本文提出了一种用于故障诊断的新型多速率采样数据融合方法。在所提出的方法中，融合了具有不同采样率的信号。第一的，采用卷积神经网络（CNN）从原始数据中自动学习特征。然后，利用长短期记忆 (LSTM) 网络挖掘提取特征中的时间相关性并对时间信息进行编码。该方法在公共实验数据集和来自真实工业场景的数据上得到验证。将所提出的方法与一些最先进的机器学习 (ML) 和 DL 方法进行比较，结果表明，所提出的方法可以令人满意地区分不同的条件，并且在所有比较方法中具有最佳的诊断准确性。该方法在公共实验数据集和来自真实工业场景的数据上得到验证。将所提出的方法与一些最先进的机器学习 (ML) 和 DL 方法进行比较，结果表明，所提出的方法可以令人满意地区分不同的条件，并且在所有比较方法中具有最佳的诊断准确性。该方法在公共实验数据集和来自真实工业场景的数据上得到验证。将所提出的方法与一些最先进的机器学习 (ML) 和 DL 方法进行比较，结果表明，所提出的方法可以令人满意地区分不同的条件，并且在所有比较方法中具有最佳的诊断准确性。