Deep learning (DL) method are extensively used for bearing fault diagnosis (BFD). Due to severe data distribution difference under variable working conditions, they have unsatisfactory performance of the BFD. Although the existing transfer learning (TL) methods might improve the diagnostic performance in different data distributions, fault data from these different domains in training have to be obtained. When a given bearing operates in a new working condition and fault data are not available, the TL methods might be invalid, and the BFD would be postponed. To solve the above problem, a novel zero-shot learning method named deep attention relation network (DARN) is proposed for the BFD under multiple unknown domains. The built DARN only trained by the data from a known domain might be used to diagnose fault types from unknown, but related domains without prior data input. In this method, a feature extraction module is constructed to generate representations of input samples, and a relation module is designed to calculate the relation score between the sample pairs to determine their categories. Meanwhile, a parallel attention mechanism is introduced into the DARN so as to enhance the representative ability of the built model. The results of experimental study indicate that the proposed method can make use of fault knowledge learnt from the single known domain for the BFD in the several unknown domains. The proposed DARN significantly outperforms the existing popular TL methods in diagnostic performance.

Timely sensing the abnormal condition of the bearings plays a crucial role in ensuring the normal and safe operation of the rotating machine. Most traditional bearing fault diagnosis methods are developed from machine learning, which might rely on the manual design features and prior knowledge of the faults. In this paper, based on the advantages of CNN model, a two-step fault diagnosis method developed from wavelet packet transform (WPT) and convolutional neural network (CNN) is proposed for fault diagnosis of bearings without any manual work. In the first step, the WPT is designed to obtain the wavelet packet coefficients from raw signals, which then are converted into the gray scale images by a designed data-to-image conversion method. In the second step, a CNN model is built to automatically extract the representative features from gray images and implement the fault classification. The performance of the proposed method is evaluated by a real rolling-bearing dataset. From the experimental study, it can be seen the proposed method presents a more superior fault diagnosis capability than other machine-learning-based methods.

Fault diagnosis is very important to ensure the efficiency and reliability of rotating machinery. Traditional fault diagnosis methods often require manual feature design and extraction, which is an exhausting work in industrial applications. Convolutional neural network (CNN)-based methods have presented a great potential to automatically extract and select representative features for fault diagnosis. In this article, a novel three-step intelligent fault diagnosis method is proposed based on CNN and Bayesian Gaussian mixture (BGM) for rotating machinery. In the fault dataset construction step, multiple binary training datasets are constructed to amplify the difference between one fault mode and the others using a defined fault labeling rule. In the fault feature extraction step, multiple binary classification tasks are, respectively, implemented to learn the representative features for each fault mode through a proposed group of CNN models based on the above-obtained training datasets. In the fault diagnosis step, multiple Gaussian mixture models (GMMs) are adopted to fit the data distributions of the learned fault features, and an BGM model based on GMM parameters and Bayesian network is designed to establish a cause-and-effect relationship between corresponding parameters and fault modes. Two case studies are used to evaluate the effectiveness of the proposed method. The experimental results show that the proposed method has a superior performance over the existing methods.