1. A Novel Intelligent Fault Diagnosis Approach for Early Cracks of Turbine Blades via Improved Deep Belief Network Using Three-Dimensional Blade Tip Clearance
- Author
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Yiwei Xiong, Xin Huang, Hongcheng Liu, Xiaodong Zhang, and Yingjie Zhang
- Subjects
0209 industrial biotechnology ,Turbine blade ,General Computer Science ,three-dimensional blade tip clearance ,Computer science ,Feature extraction ,02 engineering and technology ,law.invention ,Deep belief network ,Tip clearance ,020901 industrial engineering & automation ,law ,0202 electrical engineering, electronic engineering, information engineering ,General Materials Science ,business.industry ,Deep learning ,feature extraction ,020208 electrical & electronic engineering ,deep belief networks ,General Engineering ,Pattern recognition ,fault diagnosis ,Feature (computer vision) ,Softmax function ,Artificial intelligence ,lcsh:Electrical engineering. Electronics. Nuclear engineering ,business ,Feature learning ,lcsh:TK1-9971 - Abstract
Aero-turbines usually work in various non-stationary and harsh operating environments. Its blades easily appear early crack faults in long-time operation, and it is difficult to dig out discriminant features of early crack. To solve these problems, in the paper, a novel intelligent approach for early crack diagnosis of turbine blades using three-dimensional blade tip clearance is presented. In order to improve feature learning ability to obtain better generalization ability, the paper firstly develops a novel deep learning method based on deep belief networks (DBNs). Considering the fact that the feature degradation easily occurs in deeper layers because of the change of the distribution in each layer's outputs with the increase of the layers, it is hard to decide which layer to learn features is useful for fault diagnosis. Accordingly, in the pre-training process, the global back-reconstruction (GBR) mechanism is introduced into DBNs to optimize the feature learning ability. The GBR mechanism can be realized between the input layer and hidden layers by “shortcut connection”, and the layer to learn more discriminant features can be determined automatically without prior knowledge. Moreover, due to three-dimensional blade tip clearance (3-DBTC) acquired from three different directions of turbine blades contains much more useful crack failure multiscale information, it is suitable to be used as an input from which to extract multiscale discriminant features. Eventually, in the supervised training, the softmax regression model is employed to classify the health conditions of turbine blades using these sensitive features learned from 3-DBTC. The experimental results show that the proposed method can effectively identify the crack of turbine blades with fairly high diagnostic accuracies and significantly outperform other methods considered in the paper.
- Published
- 2021