Gears and bearings are important components in rotating machinery and are crucial for the safety and operation of the whole mechanical system. Intelligent fault diagnosis methods based on deep-learning algorithms have undergone rapid development in recent years. Despite this, integrating fault features in a deep network construction remains a challenge for intelligent fault diagnosis of rotating machinery. In this paper, a novel impact feature extraction deep neural network (IFE-DN) is proposed for intelligent gear and bearing fault diagnosis. An improved three-layer Laplace wavelet kernel convolutional neural network (LW-CNN), where the Laplace wavelet kernel is designed in the first convolutional layer, is constructed to extract and enhance the impact features in the vibration signal. Using a visualized heat map, the physical meaning of the LW-CNN’s extracted features is explained and the interpretability of the network model is enhanced. The wavelet function selection in the deep neural network is also discussed. The extracted features are transferred to a primary capsule layer and a digital capsule layer. With a feature vector converting process and dynamic routing algorithm, more detailed features are optimized and the fault types are classified. Four experimental data sets from different laboratories are used to verify the performance of the proposed model, and t-distributed stochastic neighbour embedding is carried out to visually analyze the extracted features in different layers. The results of the analysis of gear and bearing faults of different types and defect sizes show that the IFE-DN presents significant accuracy and satisfactory generalization ability.

Reasonable and in-depth analysis of supervisory control and data acquisition (SCADA) dataset can improve the accuracy and reliability of anomaly detection of wind turbines. In this paper, a multi-variable correlation learning network named AMTCN-GRU is proposed to extract multidirectional features of SCADA data for wind turbine condition monitoring. Firstly, the parameters with greater relevance to the prediction target are selected as inputting parameters of this method. Meanwhile, the cabin vibration signal contains the transient characteristics of the operating system. If the component connected to the cabin fails, the vibration signal will change immediately. Then the vibration parameter is selected as one of the inputs. In this paper a novel AMTCN model is proposed for enhancing the feature extraction capability which is constituted with convolutional block attention mechanism (CBAM) embedding to TCN’s residual block structure. The extracted features can be weighted again to make the output more relevant to the predicted target. Gated recurrent unit (GRU) is performed to construct the connections of feature and output for the condition prediction of wind turbine. Finally, it is proved that this proposed method can accurately and reliably realize the anomaly detection of wind turbines by analyzing the SCADA data of the actual wind farm.

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