An output‐only multiple‐crack localization method is proposed in this paper to detect and localize breathing cracks in a stepped rotor, which utilizes the crack‐induced local shape distortions in super‐harmonic characteristic deflection shapes (SCDSs). To minimize the noise effects on SCDSs and improve the accuracy of SCDS‐based crack localization, singular value decomposition is adopted to estimate the SCDS as the dominant singular vector of output power spectral density matrix at a super‐harmonic frequency. Then, in order to better reveal shape distortions in the SCDSs, an after‐treatment technique called gapped smoothing method is applied to derive a damage index. Numerical experiments are carried out to investigate the performance of the proposed method based on a two‐disc stepped rotor‐bearing system with breathing cracks established by the finite element method. Results show that the method is effective for single and multiple crack localization in stepped rotors and interference of steps can be excluded. Furthermore, the method is robust to noise. Influences of crack depths and rotating speeds are also investigated, and how to choose the rotating speed for better crack localization is discussed.

Crack and shaft misalignment are two common types of fault in a rotor system, both of which have very similar dynamic response characteristics, and the vibration signals are vulnerable to noise contamination because of the interaction among different components of rotating machinery in the actual industrial environment, resulting in great difficulties in fault identification of a rotor system based on vibration signals. A method for identification of faults in the form of crack and shaft misalignments is proposed in this paper, which combines variational mode decomposition (VMD) and probabilistic principal component analysis (PPCA) to denoise the collected vibration signals from a test rig and then achieve signal feature extraction and fault classification with convolutional artificial neural network (CNN). The key parameters of the CNN are optimized and determined by genetic algorithm (GA) firstly, and the domain adaptability of the trained network is verified by the signals with different signal-to-noise ratio (SNR) values; then, the noisy vibration signals are decomposed into multiple band-limited intrinsic modal functions by VMD, and further data dimension reduction is performed by PPCA to realize the separation of the useful signals from noise; finally, the crack and shaft misalignment of the rotor system are identified by the optimized CNN. The results show that the proposed method can effectively remove the interference noise and extract the intrinsic features of the vibration signals, and the recognition rates of crack and shaft misalignment faults for the rotor system with different SNR values are more than 99%, which is considered to be very effective and useful.

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Parameters identification of cracked rotors has been gaining importance in recent years, but it is still a great challenge to determine the crack parameters including crack location, depth, and angle for operating rotors. This work proposes a new method to identify crack parameters in a rotor-bearing system based on a Kriging surrogate model and an improved nondominated sorting genetic algorithm-III (NSGA-III). A rotor-bearing system with a breathing crack is established by the finite element method and the superharmonic components are used as index to detect the cracks, the Kriging surrogate model between crack parameters and the superharmonic component amplitudes of the vibration response for rotors are constructed, and an improved NSGA-III is proposed to obtain the optimal crack parameters. Numerical experiments show that the proposed method can identify the crack location, depth, and angle accurately and efficiently for operating rotors.

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