Rolling bearing is a key component of rotating machinery, and its fault diagnosis technology is very important to ensure the safety of equipment. With the rapid development of deep learning, the convolutional neural network (CNN) is widely used in bearing fault diagnosis, showing obvious advantages in diagnostic accuracy. However, the deep CNN model generally requires a lot of computing resources and storage space, and it is not easy to apply in practical engineering. Aiming at this problem, a lightweight CNN model for rolling bearing fault diagnosis is designed. This model is combined with a network pruning algorithm and neural architecture search, which not only ensures the accuracy of diagnosis but also reduces computing resources. By constructing the search space of the complete Cell class unit, using the multi-objective reinforcement learning search strategy, and applying the deep learning pruning method to prune and search the network, the lightweight CNN model with higher accuracy is efficiently searched. The rolling bearing fault data set is utilized to validate the use of the lightweight CNN model for diagnosing rolling bearing faults. This model significantly enhances operational efficiency without compromising accuracy, achieving a fault diagnosis accuracy of up to 98.56%.

Multisensor information fusion, when applied to fault diagnosis, the time-space scope, and the quantity of information are expanded compared to what could be acquired by a single sensor, so the diagnostic object can be described more comprehensively. This paper presents a methodology of fault diagnosis in rotating machinery using multisensor information fusion that all the features are calculated using vibration data in time domain to constitute fusional vector and the support vector machine (SVM) is used for classification. The effectiveness of the presented methodology is tested by three case studies: diagnostic of faulty gear, rolling bearing, and identification of rotor crack. For each case study, the sensibilities of the features are analyzed. The results indicate that the peak factor is the most sensitive feature in the twelve time-domain features for identifying gear defect, and the mean, amplitude square, root mean square, root amplitude, and standard deviation are all sensitive for identifying gear, rolling bearing, and rotor crack defect comparatively.

The transmission paths of vibration signals in the planetary gearboxes are complex. The signals have the characteristics of strong background noise, instability and non-Gaussian. Bi-spectrums can suppress Gaussian colored noise and are suitable for vibration signal processing of planetary gearboxes. In the traditional fault diagnosis methods based on bi-spectrums, the amplitudes of fault characteristic frequency, or the other further quantitative calculations values, are generally used as the basis of fault diagnosis processes. It has been found that bi-spectrum images can directly characterize the faults of the planetary gearboxes. Convolutional neural networks (CNNs) have been used in mechanical fault diagnoses in recent years. One-dimensional original signals are converted into two-dimensional images as CNN input, which is an effective method for mechanical fault diagnoses. At the present time, there has not been any relevant research conducted using bi-spectral images as CNN input. In this study, a fault diagnosis method based on local bi-spectrum and CNN was proposed. A bi-spectral analysis of the vibration signals of the planetary gearbox was first carried out in order to reveal the fault information while retaining the non-Gaussian information. Then, according to the bi-spectrum symmetry, local images containing the main information were taken as the input of the CNN, which reduced the redundancy of the fault information. Then, in order to improve the diagnostic accuracy of the CNN, the key parameters of CNN architecture were optimized. Finally, a CNN diagnosis model was built to realize the classification diagnoses of different fault positions and different fault degrees of planetary gearboxes. This study’s comparison of the diagnosis results of the full bi-spectrum + CNN, original vibration signal + CNN, local bi-spectrum + (support vector machines), and local bi-spectrum + (stacked auto-encoder) showed that the proposed method in this study had achieved both accuracy and rapidity in the fault diagnoses of planetary gearboxes.

The finite element model of a dual-rotor system was established by Timoshenko beam element. The dual-rotor system is a coaxial rotor whose supporting structure is similar to that of an aero-engine rotor system. The inner rotor is supported by three bearings, which makes it a redundantly supported rotor. The outer rotor connects the inner rotor by an intershaft bearing. The spectrum characteristics of the dual-rotor system under unbalanced excitation and misalignment excitation were analysed in order to study the influence of coupling misalignment of the inner rotor on the spectral characteristics of the rotor system. The results indicate that the vibration caused by the misaligned coupling of the inner rotor will be transmitted to the outer rotor through the intershaft bearing. Multiple harmonic frequency components, mainly 1x and 2x, will be excited by the coupling misalignment. The amplitudes of all harmonic frequencies increase with the misalignment in both the inner and outer rotors. The vibration level of the outer rotor affected by the misalignment is lower than that of the inner rotor because it is far from the misaligned coupling. Harmonic resonance occurs when any harmonic frequencies of the misalignment response coincide with a natural frequency of the system. In order to verify the theoretical model, experiments are performed on a test rig. Both the experimental and simulation results are in good accordance with each other.

With its formidable nonlinear mapping capabilities, deep learning has been widely applied in bearing remaining useful life (RUL) prediction. Given that equipment in actual work is subject to numerous disturbances, the collected data tends to exhibit random missing values. Furthermore, due to the dynamic nature of wind turbine environments, LSTM models relying on manually set parameters exhibit certain limitations. Considering these factors can lead to issues with the accuracy of predictive models when forecasting the remaining useful life (RUL) of wind turbine bearings. In light of this issue, a novel strategy for predicting the remaining life of wind turbine bearings under data scarcity conditions is proposed. Firstly, the average similarity (AS) is introduced to reconstruct the discriminator of the Generative Adversarial Imputation Nets (GAIN), and the adversarial process between the generative module and the discriminant is strengthened. Based on this, the dung beetle algorithm (DBO) is used to optimize multiple parameters of the long-term and short-term memory network (LSTM), and the complete data after filling is used as the input data of the optimized LSTM to realize the prediction of the remaining life of the wind power bearing. The effectiveness of the proposed method is verified by the full-life data test of bearings. The results show that, under the condition of missing data, the new strategy of AS-GAIN-LSTM is used to predict the RUL of wind turbine bearings, which has a more stable prediction performance.