Planetary gearbox is a critical component for many mechanical systems. It is essential to monitor the planetary gearbox health and performance in order to maintain the whole machine works well. The methodology of mechanical fault diagnosis is increasingly intelligent with the extensive application of deep learning. However, the cross-domain issue caused by varying working conditions becomes an enormous encumbrance to fault diagnosis based on deep learning. In this paper, in order to fully excavate potentialities of deep neural network architectures, a novel generative adversarial learning method was introduced for a completely new fault diagnosis based on a deep convolution neural network. In addition, the intelligent fault diagnostic scheme for planetary gearbox under varying speed conditions was developed. After that, some experiments on measured vibration signals of planetary gearbox were conducted to verify the validity and efficiency of the fault diagnostic scheme. The results showed that the proposed method enhanced the capability of the intelligent diagnosis for planetary gear faults under varying speed conditions. Empirical Mode Decomposition (EEMD), machine learning algorithms of Kernel Principle Component Analysis (KPCA) and Learning Vector Quantization (LVQ) [6]. Li used the signal processing method based on Adaptive Multi-scale Morphological Filter (AMMF) and Modified Hierarchical Permutation Entropy (MHPE) to extract fault features in PG vibration signals and then diagnosed the fault by Binary Tree Support Vector Machine (BT-SVM) [7]. Lei also advanced a health condition identification method for multi-stage PG, adopting multi-class relevant vector machine as a classifier and introduced Accumulative Amplitudes of Carrier Orders (AACO) and Energy Ratio-based Difference Spectra (ERDS) as fault features which improved diagnosis performance and robustness [8]. These intelligent methods had certain limitations and uncertainties. Firstly, in the pre-processing stage of vibration signal, the expertise knowledge was highly required, such as signal processing and data statistics. On the other hand, the intrinsic features for the faults of planetary gearbox were not found in these existing literatures, which were independent of the fluctuation of working conditions. Thus, these led to the problem of cross-domain for intelligent fault diagnosis of PG.In recent years, Deep Learning (DL) has been attracting growing attention from various fields. The deep model hierarchical structures make DL capable of learning intrinsic representations of raw complex data, which provide the foundation for its popular application in visual recognition and natural language processing [9][10][11][12][13]. Researchers in the field of mechanical fault diagnosis have taken advantage of the deep learning ability to realize more adaptive feature learning from vibration signals [14]. Some researchers applied various sorts of deep learning models as an upgraded classifier with manually extracted features [15][16][17][18][19][20][21][22][2...

A turnout switch machine is key equipment in a railway, and its fault condition has an enormous impact on the safety of train operation. Electrohydraulic switch machines are increasingly used in high-speed railways, and how to extract effective fault features from their working condition monitoring signal is a difficult problem. This paper focuses on the sectionalized feature extraction method of the oil pressure signal of the electrohydraulic switch machine and realizes the fault detection of the switch machine based on this method. First, the oil pressure signal is divided into three stages according to the working principle and action process of the switch machine, and multiple features of each stage are extracted. Then the max-relevance and min-redundancy (mRMR) algorithm is applied to select the effective features. Finally, the mini batch k-means method is used to achieve unsupervised fault diagnosis. Through experimental verification, this method can not only derive the best sectionalization mode and feature types of the oil pressure signal, but also achieve the fault diagnosis and the prediction of the status of the electrohydraulic switch machine.

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Fault diagnosis is an important means to ensure the safe and reliable operation of mechanical equipment. In machinery fault diagnosis, collecting and mining the potential fault information of the vibration signal is the most commonly used method to reflect the operating status of the equipment. In engineering scenarios, in the face of rotating machinery with variable speed, simple time domain analysis or frequency domain analysis is difficult to solve the problem. The time-frequency analysis technology that combines time-frequency transformation and data analysis can solve practical engineering problems by capturing the transient information of the signal. At present, a large number of related literatures have been published in academic journals. This paper hopes to provide convenience for relevant researchers and motivate researchers to further explore by summarizing the published literature. First, this paper briefly explains the concept of time-frequency analysis and its development. Then, the time-frequency transformation method proposed for the characteristics of rotating machinery fault vibration signal and related works of literature are reviewed, and the key issues of the application of time-frequency transformation method in rotating machinery fault diagnosis are discussed. Next, this paper summarizes the relevant literature on the combination of data analysis technology and time-frequency transformation and sorts out its development route and prospects. The study reveals that time-frequency analysis technology is able to detect the rotating machinery fault effectively. The time-frequency analysis technology has made abundant achievements in the field of rotating machinery fault diagnosis. It is expected that this review would inspire researchers to explore the potential of time-frequency analysis as well as to develop advanced research in this field.