Abstract
The shape characteristic of the axis orbit plays an important role in the fault diagnosis of rotating machinery. However, the original signal is typically messy, and this affects the identification accuracy and identification speed. In order to improve the identification effect, an effective fault identification method for a rotor system based on the axis orbit is proposed. The method is a combination of ensemble empirical mode decomposition (EEMD), morphological image processing, Hu invariant moment feature vector, and back propagation (BP) neural network. Experiments of four fault forms are performed in single-span rotor and double-span rotor test rigs. Vibration displacement signals in the X and Y directions of the rotor are processed via EEMD filtering to eliminate the high-frequency noise. The mathematical morphology is used to optimize the axis orbit including the dilation and skeleton operation. After image processing, Hu invariant moments of the skeleton axis orbits are calculated as the feature vector. Finally, the BP neural network is trained to identify the faults of the rotor system. The experimental results indicate that the time of identification of the tested axis orbits via morphological processing corresponds to 13.05 s, and the identification accuracy rate ranges to 95%. Both exceed that without mathematical morphology. The proposed method is reliable and effective for the identification of the axis orbit and aids in online monitoring and automatic identification of rotor system faults.
Highlights
A rotor system is a core component of rotating machinery, and the rotor system plays a key role in the stable operation of rotating machinery
The intelligent level of fault diagnosis for the rotor system is determined by the automatic identification accuracy of the axis orbit
Automatic identification of the axis orbit is realized by the back propagation (BP) neural network
Summary
A rotor system is a core component of rotating machinery, and the rotor system plays a key role in the stable operation of rotating machinery. Fault diagnosis by the vibration signal of mechanical equipment is a common and effective method, but vibration signals typically contain a considerable amount of fault information in conjunction with environmental noise. It is difficult to identify faults by only using vibration signals. Identifying the axis orbit is one of the important methods for fault diagnosis of the rotor system. The axis orbit diagram is composed of two sets of vibration displacement signals, which are perpendicular to each other on the same cross-section. The axis orbit contains a lot of significant information, and the running state of the equipment can be visually and intuitively reflected by the axis orbit diagram. The intelligent level of fault diagnosis for the rotor system is determined by the automatic identification accuracy of the axis orbit. It is of great significance to study the automatic identification of the axis orbit
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