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.

Online bearing fault diagnosis is still a challenge in real applications because of the complex modulation features and the nonstationary conditions. To realize the online fault diagnosis, a method robust to speed fluctuation and background noise is necessary. In this paper, an iterative generalized demodulation with tunable energy factor (IGDTEF) is proposed, which can map the time-varying trajectories of interest components to their corresponding energy factors. To exploit it in the online fault diagnosis, a phase function estimation strategy is further developed. First, the optimal frequency band of the raw signal is identified by fast kurtogram and then a bandpass filter is designed to separate the impulsive component. Second, Hilbert transform and short-time Fourier transform are applied to the filtered signal jointly obtaining the envelope time–frequency representation (TFR). Then, the instantaneous fault characteristic frequency (IFCF) is estimated roughly by applying an amplitude-sum-based peak search to the TFR. Next, the phase functions of the IFCF, the potential modulation rotating frequency, and their harmonics are calculated. Finally, the IGDTEF is performed to the filtered signal and then fast Fourier transform is applied to the demodulated signal generating the demodulation spectrum. The effectiveness of the method is evaluated by simulated and experimental data.

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.

With the development of industrial robots and other mechanical equipment to a higher degree of automation, mechanical systems have become increasingly complex. This represents a huge challenge for condition monitoring. The separation of vibration source signals plays an important role in condition monitoring and fault diagnosis. The key to the separation method of the vibration source signal is prior knowledge, such as of the statistical features of the vibration source signal, the number of vibration sources, and so forth. However, effective prior knowledge is difficult to obtain in engineering applications. This study found that low rank is a common feature of rotating machinery vibration source signals. To address the problem of the difficulty obtaining the signal feature of a vibration source, the multi-low-rank constrained vibration source signal separation method was proposed. Its advantages and effectiveness have been verified through simulations and experimental tests. Compared with the blind source separation method of independent component analysis (BSS-ICA) and the ensemble empirical mode decomposition (EEMD) methods, it obtained better clustering results and higher signal-to-signal ratio (SSR) values.

The railway system is an important part of the transportation system. Its scheduling process is carried out by the switch machines. The accuracy of determining the health status of the switch machines is related to the operational efficiency and reliability of the whole system. However, manual fault diagnosis for these machines is always unstable and expensive. The intelligent fault diagnosis (IFD) method can perform accurate fault diagnosis at low cost and high efficiency, but requires a large amount of labeled data. In this case, this study realizes the Feature Pseudo-Fusion (FPF) of left and right oil pressure signals of the electro-hydraulic switch machine. It uses contrastive learning to regularize the feature representation of original signals. Based on FPF, a fault diagnosis method applicable to electro- hydraulic switch machines is constructed. This method reduces the need for labeled data laterally without introducing additional measurement content to the field signal acquisition system. The effectiveness of FPF and the superiority of the fault diagnosis method have been verified through experiments.