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Skidding is an inevitable phenomenon in rolling element bearing but accelerates the bearing wear and shortens the bearing life. Recognizing skidding behavior under different clearances will benefit to bearing health monitoring. In this study, an improved bearing dynamic model is developed to investigate its vibration characteristics and skidding behavior taking into all bearing structure components, time-varying oil film characteristics and bearing clearances. A novel method is proposed to include time-varying oil film stiffness and thickness under different clearances into the bearing dynamic model. A fast algorithm is adopted through interpolation during the model solving process. Furthermore, vibration responses and skidding behavior under different clearances are analyzed. Finally, a designed synchronous differential encoder (SDE) system tests the skidding behavior of bearings and verifies the proposed model through measuring the angular velocity of cage transformed by distance test. Numerical result shows that both RMS and ball pass frequency of outer race (BPFO) increase with bearing clearances. The slip ratio represents growth with the increase of bearing clearances, which is verified by the designed experiment with good consistency with the simulation. It can be foreseen that this novel dynamic model with SDE system can be well performed for bearing skidding detection and fault detection. [ABSTRACT FROM AUTHOR]

Bearing waviness is a kind of geometric unevenness on the surface of bearing components, which has vital influence on lifetime, vibration and noise. In order to accurately evaluate the impact of waviness on bearing operating performance and diagnostic features, it is necessary to reveal the mapping relationship between waviness excitation and vibration characteristics. However, current simulated waviness when modeling bearing vibrations is usually simplified by a uniform or non-uniform sinusoidal function, which cannot characterize the real topography of bearing waviness and lead to inaccuracy of diagnostics and prognostics. To address this issue, a novel waviness characterizing method based on Gaussian filter is developed in this study. Based on the proposed waviness characterizing method, a bearing waviness dynamic model is developed and vibration responses under various amplitude and order of waviness are investigated by simulation and experiment. Results show that the established waviness characterizing method can generate waviness curves closer to the actual shape. The bearing waviness dynamic model and the based vibration responses reveals unusual random phenomenon due to different waviness effects. These findings provide theoretical support for accurate identification of waviness on vibration characteristics, which has great significance on condition monitoring and fault diagnosis of bearing. [ABSTRACT FROM AUTHOR]

• ORS is developed to be installed on the rotating shaft for vibration measurements. • The developed ORS largely improves the SNR of vibration collections than OHS. • The FFT-based fault diagnosis theory for the rolling bearing is presented. • ORS can achieve easier and more robust Hilbert envelope analysis for diagnosis. • Results validate the performance of ORS for early rolling bearing fault diagnosis. The traditional on-house sensing (OHS) accelerometer for vibration measurements causes poor signal-to-noise ratio (SNR) and complicated fault modulations, which increases the difficulty and complexity for early bearing fault diagnosis. To overcome these challenges, this paper develops a wireless triaxial on-rotor sensing (ORS) system to largely improve the SNR and deduces fast Fourier transform (FFT) and Hilbert envelope analysis for accurate early rolling bearing fault diagnosis, which largely improves accuracy and efficiency for early fault diagnosis. First, the development of the ORS system for wireless vibration measurements is given. Second, the theoretical diagnostic relationships between dynamic ORS signals and rolling bearing faults are derived for FFT and Hilbert envelope analysis for the first time. Finally, the induction motor tests with outer and inner race faults successfully validate that both simple FFT and Hilbert envelope analysis can achieve more robust early rolling bearing fault diagnosis compared to OHS measurements. [ABSTRACT FROM AUTHOR]

• The proposed method extends the cyclic frequency range to Fs /2. • The designed scale factor in CWT can help locate important frequency bands. • TKEO is improved to process the single-carrier signal in the frequency domain. • The developed method can enhance the fault features effectively. • Induction motor tests validate the superiority of the method for early fault detection. The cyclostationary analysis techniques have been extensively explored for the purpose of fault detection in rotating machinery. However, there are still huge challenges because of both limited detection frequency range and low fault identification accuracy. This paper proposes an improved cyclostationary method to enhance incipient fault features. Firstly, the continuous wavelet transform is used to accurately locate important frequency bands, and the fault modulation mechanism or fast kurtogram can be adopted to design the optimal wavelet transform scale factor. Secondly, the Teager-Kaiser energy operator is improved to be used in the frequency domain for the weak fault feature enhancement. Finally, fault features are presented in the cyclic frequency domain through spectral coherence and enhanced envelope spectrum. The proposed method is verified through both numerical simulation and experiments, including incipient half-broken rotor bar, and rolling bearing outer race faults in induction motors. [ABSTRACT FROM AUTHOR]

• A multiscale denoising branch is developed to extract multi-level information and reduce noise interference. • An improved flow direction strategy-based adaptive resonance branch is introduced to learn fault-related periodic impulsive features. • Case studies are conducted to validate the efficacy of the developed IFD-MDCN. Rolling bearings are the core components of rotating machinery, and their normal operation is crucial to the entire industrial production. Most existing condition monitoring methods have been devoted to extracting discriminative features from vibration signals that reflect bearing health status information. However, the complex working conditions of rolling bearings often make the periodic impulsive characteristics related to fault information easily buried in noise interferences. Therefore, it is challenging for existing approaches to learning discriminative fault-related features in these scenarios. To address this issue, a novel multibranch CNN named IFD-MDCN is developed in this paper, which represents multibranch denoising convolutional networks (MDCN) with an improved flow direction (IFD) strategy. The main contributions of this work include: (1) designing a multiscale denoising branch to extract multi-level information and reduce noise impact. More specifically, the multiscale denoising branch adopts a Gaussian multi-level noise reduction procedure to represent vibration signals at multiple levels and filter out the noise components, and then it uses a multiscale convolutional module to extract abundant features from these denoised signal representations; (2) establishing an improved flow direction strategy-based adaptive resonance branch to learn periodic impulsive features associated with fault information from vibration signals. Extensive experimental results reveal that the IFD-MDCN outperforms five state-of-the-art approaches, especially in strong noise scenarios. [ABSTRACT FROM AUTHOR]

Fault diagnosis of rolling bearings has attracted extensive attention in industrial fields, which plays a vital role in guaranteeing the reliability, safety, and economical efficiency of mechanical systems. Traditional data-driven fault diagnosis methods require obtaining a dataset of full failure modes in advance as the training data. However, this kind of dataset is not always available in some critical industrial scenarios, which impairs the practicability of the data-driven fault diagnosis methods for various applications. A digital twin, which establishes a virtual representation of a physical entity to mirror its operating conditions, would make fault diagnosis of rolling bearings feasible when the fault data are insufficient. In this paper, we propose a novel digital twin-driven approach for implementing fault diagnosis of rolling bearings with insufficient training data. First, a dynamics-based virtual representation of rolling bearings is built to generate simulated data. Then, a Transformer-based network is developed to learn the knowledge of the simulated data for diagnostics. Meanwhile, a selective adversarial strategy is introduced to achieve cross-domain feature alignments in scenarios where the health conditions of the measured data are unknown. To this end, this study proposes a digital twin-driven fault diagnosis framework by using labeled simulated data and unlabeled measured data. The experimental results show that the proposed method can obtain high diagnostic performance when the real-world data is unlabeled and has unknown health conditions, proving that the proposed method has significant benefits for the health management of critical rolling bearings. • A digital twin-driven intelligent diagnosis method is developed. • A high-fidelity digital twin model is built for the rolling bearing. • A partial domain adaptation algorithm is introduced for bearing condition assessment. • One test is conducted to validate the effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]