OrbitNet: A new CNN model for automatic fault diagnostics of turbomachines
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Authors
Xiaomo Jiang
Shuhua Yang
Fumin Wang
Shengli Xu
Xiaofang Wang
Xueyu Cheng
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Journal Article, Academic Journal
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Abstract
Unplanned outage due to faults in a high-fidelity turbomachine such as steam turbine and centrifugal compressor often results in the reduced reliability and productivity of a factory while increasing its maintenance costs. Shaft orbit images generated from turbomachine vibration signals have been used to diagnose component faults. However, the existing methods were developed mostly by either using features extracted from orbits or utilizing simulation data which may produce inaccurate results in practical applications due to system complexity and data uncertainties. This paper presents a novel deep learning convolution neural network methodology for accurately automatic diagnostics of multiple faults in general rotating machines by adeptly integrating advanced signal processing with orbit images augmentation, considering the high non-linearity and uncertainty of sensed vibration signals. Environmental noise in vibration signals are filtered through the integration of multiresolution discrete wavelet packet transform and Bayesian hypothesis testing-based automatic thresholding. Shaft orbit images generated from the cleansed vibration data are augmented to increase their representativity and generalization. A novel multi-layer convolutional neural network model, OrbitNet, is specially designed to improve its generality and robustness while avoid possible overfitting in fault identification of various turbomachines. The proposed model retains the pattern information in the axis trajectory to the greatest extent, with the ability of accurately capturing features of various faults in different turbomachines. A generic implementation procedure is proposed for automatic fault diagnosis of rotating machinery based on the presented methodology. A comparison study is conducted to demonstrate the effectiveness and feasibility of the proposed methodology by using the sensed vibration signals collected from three real-world centrifugal compressors, two steam turbines and one generator with four different fault modes including imbalance, friction, misalignment and oil whirl.