Fault diagnosis of rotating machinery plays a significant role in the industrial production and engineering field. Owing to the drawbacks of traditional fault diagnosis methods, such as heavily dependence on human knowledge and professional experience, intelligent fault diagnosis based on deep learning (DL) has aroused the interest of researchers. DL achieves the desirable automatic feature learning and fault classification. Therefore, in this review, DL and DL-based intelligent fault diagnosis techniques are overviewed. DL-based fault diagnosis approaches for rotating machinery are summarized and discussed, primarily including bearing, gear/gearbox and pumps. Finally, with respect to modern intelligent fault diagnosis, the existing challenges and possible future research orientations are prospected and analyzed.

Rotating machinery is of vital importance in the field of engineering, including aviation and navigation. Its failure will lead to severe loss to personnel safety and the stability of the equipment system. It is a long way to investigate the relevant fault diagnosis method, especially the intelligent fault diagnosis method on the basis of deep learning. In consideration of the limitations of traditional fault diagnosis approaches based on shallow layer network structure, the methods based on deep neural network (DNN) are worthy of thorough exploration. As a common DNN with special structure, deep convolutional neural network is of great concern in intelligent fault diagnosis due to its advantages in processing nonlinear problems. This review will play an emphasis on convolutional neural network (CNN). The basic structure and principle are introduced. The applications of CNN-based fault diagnosis method in rotating machinery are summarized and analyzed. Furthermore, the diagnosis performance and potential mechanism from different CNN methods are discussed. In the end, this review is highlighted on the challenges and the potential key points in research on novel intelligent fault diagnosis strategies. The corresponding analysis and discussion will provide some references and lay the foundation for the investigation in related fields. INDEX TERMS Deep learning, convolutional neural network, intelligent fault diagnosis, rotating machinery.

Multistage pump can provide high-pressure liquid, which is widely used in various areas of national economy. In order to improve the stability and reduce the noise of multistage pump, the relationships among the pressure fluctuation, vibration, and noise were studied deeply by using computational fluid dynamics and experimental measurement. Based on the unsteady numerical calculation, the phase of the pressure fluctuation wave in the middle section of the impeller and the diffuser was obtained, and the unsteady velocity distribution was acquired in the rotor-stator interaction (RSI) region between the rotational impeller and the stationary diffuser. Moreover, the vibration and noise tests of a five-stage pump with radial diffuser were performed. The results show that the phase distribution of the pressure fluctuation wave in the impeller and diffuser can be divided into four regions: the impeller flow channel region, the impeller transition region, the diffuser transition region, and the diffuser flow channel region. In addition, the pressure fluctuation, vibration and noise of the multistage pump are strongly related to each other, that is, RSI induces strong unsteady flow and pressure fluctuation in the pump, which makes the pump produce serious vibration and cause the corresponding noise. The key to controlling the vibration and noise is to reduce the effect of RSI between the impeller and the diffuser.

A self-priming centrifugal pump can be used in various areas such as agricultural irrigation, urban greening, and building water-supply. In order to simulate the gas-water two-phase flow in the self-priming process of a self-priming centrifugal pump, the unsteady numerical calculation of a typical self-priming centrifugal pump was performed using the ANSYS Computational Fluid X (ANSYS CFX) software. It was found that the whole self-priming process of a self-priming pump can be divided into three stages: the initial self-priming stage, the middle self-priming stage, and the final self-priming stage. Moreover, the self-priming time of the initial and final self-priming stages accounts for a small percentage of the whole self-priming process, while the middle self-priming stage is the main stage in the self-priming process and further determines the length of the self-priming time.

Rotating machinery plays a critical role in many significant fields. However, the unpredictable machinery faults may lead to the severe damage and losses. Hence, it is of great value to explore the precise approaches for fault diagnosis. With the development of the intelligent fault diagnosis methods based on deep learning, convolutional neural network (CNN) has aroused the attention of researchers in machinery fault diagnosis. In the light of the reduction of difficulty in feature learning and the improvement of final diagnosis accuracy, data preprocessing is necessary and crucial in CNN-based fault diagnosis methods. This review focuses on CNN-based fault diagnosis approaches in rotating machinery. Firstly, data preprocessing methods are overviewed. Then, we emphatically analyze and discuss several main techniques applied in CNN-based intelligent diagnosis, principally including the fast Fourier transform, wavelet transform, data augmentation, S-transform, and cyclic spectral analysis. Finally, the potential challenges and research objects are prospected on data preprocessing in intelligent fault diagnosis of rotary machinery. INDEX TERMS Data preprocessing, convolutional neural network, intelligent fault diagnosis, rotating machinery.

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Hydraulic automatic gauge control (AGC) system is the core control system to ensure the thickness accuracy of cold-rolled plate, and the reliability of its load roll system is the key to guarantee the rolling process with high precision, high speed, continuity and stability. However, the working mechanism of hydraulic AGC system is complex. And it possesses some features such as high nonlinearity, time variability and strong coupling. The vertical vibration easily happens in its working process. Moreover, the stability of system is seriously affected. Nevertheless, the incentive is difficult to determine. In this paper, the theory and method of nonlinear dynamics were introduced to establish the load vertical vibration (LVV) model of HAGC system under nonlinear action. The model was solved with multi-scale method, and the amplitude-frequency characteristic equation of system was obtained. Moreover, the research object of this article also focused on the nonlinear elastic force, nonlinear damping force and nonlinear excitation force, and deeply explored the effect of nonlinear parameters change on the amplitude-frequency characteristics of LVV system. The mechanism and incentive for LVV of hydraulic AGC system were further revealed. The research results lay a theoretical foundation for the vibration inducement and suppression of hydraulic AGC system.

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