To precisely diagnose the rotating machinery structural faults, especially structural faults under low rotating speeds, a novel scheme based on combination of empirical mode decomposition (EMD), sample entropy, and deep belief network (DBN) is proposed in this paper. EMD can decompose a signal into several intrinsic mode functions (IMFs) with different signal-to-noise ratios (SNRs) and sample entropy is performed to extract the signals that carry fault information with high SNR. The extracted fault signal is reconstructed into a new vibration signal that will carry abundant fault information. DBN has strong feature extraction and classification performance. It is suitably performed to build the diagnosis model based on the reconstructed signal. The effectiveness of the proposed method is validated by structural faults signal and the comparative experiments (BPNN, CNN, time-domain signal only, frequency-domain signal only). The results show that the diagnosis accuracy of the proposed method is between 99% and 100%, the BPNN is less than 25%, and the CNN is between 70% and 95%, which means the verified, proposed method has a superior performance to diagnose the structural fault.

Singular value decomposition (SVD) is an effective method used in bearing fault diagnosis. Ideally two important problems should be solved in any diagnosis: one is how to decide the dimension embedding of the trajectory matrix (TM); the other is how to select the singular value (SV) representing the intrinsic information of the bearing condition. In order to solve such problems, this study proposed an effective method to find the optimal TM and SV and perform fault signal filtering based on false nearest neighbors (FNN) and statistical information criteria. First of all, the embedded dimension of the trajectory matrix is determined with the FNN according to the chaos theory. Then the trajectory matrix is subjected to SVD, which is helpful to acquire all the combinations of SV and decomposed signals. According to the similarities of the signal changed back and signal in normal state based on statistical information criteria, the SV representing fault signal can be obtained. The spectrum envelope demodulation method can be used to perform effective analysis on the fault. The effectiveness of the proposed method is verified with simulation signals and low-speed bearing fault signals, and compared with the published SVD-based method and Fast Kurtogram diagnosis method.

In this paper, two kinds of dynamic models for shaft misalignment of rotating machinery are proposed for vibration analysis and diagnosis of the shaft misalignment state. In order to obtain the solution of the dynamic models and clarify the vibration signal features measured in the shaft misalignment state, the calculation method of vibration forces caused by misalignments is also shown. The results of computer simulation and experiment using the same rotating machine are shown to verify the efficiency of the dynamic analysis method proposed in this paper. Finally, the method for distinguishing structure faults of rotating machines (shaft misalignment state, unbalance state and looseness state) is discussed by using symptom parameters and spectra of the vibration signal measured in these states.

Transmission machinery is widely used in railway vehicles and is an important component in driving the operation of trains. Such transmission components are prone to faults under long exposure to harsh environments and complex working conditions. This affects normal operation and order, and thus it is important to ensure their safe and reliable operation. Electrical signal-based diagnosis technology has advantages of easy signal acquisition, with no need to install additional sensors, nor embedded monitoring of the object components. It has gradually become a research hotspot in the field of rail transportation diagnosis. This paper describes the fault modes of transmission machinery, takes the electrical signal-based diagnosis method as the entry point, collates and compares the existing diagnosis methods and research results in this field. It analyses their advantages and disadvantages, and finally puts forward problems for current and future research and development.