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| Deep anomaly detection method for low-rotating speed rolling bearing faults of aero-engine |
| KANG Yuxiang1, CHEN Guo2, SHENG Jiajiu1, WANG Hao3, WEI Xunkai3 |
| 1. College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016， China;
2. College of General Aviation and Flight, Nanjing University of Aeronautics and Astronautics, Liyang 213300, China;
3. Beijing Aeronautical Engineering Technology Research Center, Beijing 100076, China |
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Abstract Aiming at the problem that the faults of aero-engine rolling bearings are difficult to detect at low speed, a deep support vector description method based on Transformer framework was proposed to detect the faults of low speed rolling bearings. Firstly, a vibration feature extraction backbone network based on Transformer model is constructed. Then, the extracted features are input into a three-layer autoencoder structure, which is used to calculate the loss function of the network model. In order to reduce the amount of network calculation and improve the training speed, the frequency spectrum results obtained by Fast Fourier Transform (FFT) of the vibration acceleration time domain signal of rolling bearing were used as the input of the network in the preprocessing, and the model training was completed only by normal data. Finally, the experiments were carried out on an aero-engine rotor tester with casing and a real aero-engine. The results show that the proposed method can accurately detect the faults of low speed rolling bearings, and the detection accuracy is 93% and 100%, respectively, which fully indicates that the proposed method has good anomaly detection ability and application value.
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Received: 30 May 2023
Published: 15 April 2024
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