In this paper, the nonlinear vibration responses of a dual-rotor system supported on the ball bearings considering coupling misalignment are investigated with inevitable uncertainties included. Moreover, uncertain parameters are modelled by non-probabilistic interval variables, alleviating the hash demands in fitting into a sophisticated probability law. It is then more suited for engineering problems that have sparse prior data on uncertainties. The deterministic vibration responses, orbits and frequency spectrum are provided first to exhibit the evolution of the vibrations. Then, several physical parameters are studied to reveal the effects of their uncertainty on the nonlinear vibrations at different rotating speeds. It is worth noting that uncertainty in the speed ratio between the higher-pressure and lower-pressure rotors has great impacts. Moreover, the sensitivity also depends on the rotating speed.

In order to extract and enhance the weak fault feature of rolling element bearings in strong noise conditions, the Empirical Wavelet Transform (EWT) is improved and a novel fault feature extraction and enhancement method is proposed by combining the Maximum Correlated Kurtosis Deconvolution (MCKD) and improved EWT method. At first, the MCKD method is conducted to de-noise the signal by eliminating the non-impact components. Then, the Fourier spectrum is segmented by local maxima or minima in the envelope of the amplitude spectrum with a pre-set threshold based on the noise level. By building up the wavelet filter banks based on the spectrum segmentation result, the signal is adaptively decomposed into several sub-signals. Finally, by choosing the most meaningful sub-signal with the maximum kurtosis, the fault feature can be extracted in the squared envelope spectrum and teager energy operator spectrum of the chosen component. Both simulations and experiments are performed to validate the effectiveness of the proposed method. It is shown that the spectrum segmentation result of improved EWT is more reasonable than the traditional EWT in strong noise conditions. Furthermore, compared with commonly used methods, such as the Fast Kurtogram (FK) and the Optimal Wavelet Packet Transform (OWPT) method, the proposed method is more effective in the fault feature extraction and enhancement of rolling element bearings.

Data-driven fault diagnosis primarily involves the identification of different fault locations and fault severities. Focusing on a challenging task for which the target fault severities do not exist in the training samples, this article proposes a generalized cross-severity bearing fault diagnosis scheme based on a novel hierarchical cross-category inference framework. The proposed method uses an outlier detection scheme based on unsupervised feature mapping and local outlier probability calculation to identify the unseen samples. A neural network embedded with a tree-structured decision layer acts as a backbone to execute fault diagnosis at different hierarchies for different sample types, seen or unseen. Additionally, the metric learning method is used to support the approximate severity inference of the unseen samples after the fault locations are identified in the hierarchical model. Experiments performed on an aeronautical bearing test rig revealed that the proposed scheme is both feasible and superior to existing methods.

As one of the most crucial issues in the condition monitoring of mechanical systems, the remaining useful life (RUL) estimation of bearings suffers from the limitation of run-to-failure data, which makes the cross-operating condition RUL estimation of bearings a challenging subject. To address this issue, a cross-operating condition degradation knowledge learning method is proposed by constructing a shared latent feature space across different operating conditions, in which the bearing degradation features are effectively extracted and the cross-domain feature differences are weakened. Focusing on learning the cross-operating condition degradation knowledge, the time-frequency representation (TFR) samples generated by the continuous wavelet transform (CWT) method in both the source and target domains were projected onto a shared latent feature space constructed by a novel joint dictionary matrix factorization (JDMF) method. The joint-domain dictionaries solved by the JDMF method have played a core role in extracting the domain-invariant features that do not change along with the operating conditions of bearings. As a result, the convolutional neural network (CNN) model trained with the source samples in the shared latent feature space can be directly generalized to the target bearings for the degradation indicator (DI) modeling and RUL estimation. The cross-operating condition bearing RUL estimation experiment executed in the PRONOSTIA platform has demonstrated the effectiveness and superiority of the proposed JDMF-CNN framework.

Vibration characteristic plays an important role in vibrothermography, which directly affects the heating during the test. In this work, involving all the contacts in the vibrothermography, the “double-mass-three-spring” model is established to explore the vibration characteristic. The obtained results show that ultrasonic gun vibrates at fundamental frequency (FF), while the specimen vibrates at multi-frequencies including FF, 2FF, 3FF, and 4FF, which is validated through experimental investigation results. Additionally, the model proposed in this work reveals a high order harmonics in the vibrothermography test and makes the specimen conduct the steady vibration, which indicates that the model is closer to the practical equipment and can ensure the heating efficiency induced by vibration of specimen to improve the detection capability.