A sensible maintenance strategy must take into account the remaining usable life (RUL) estimation to maximize equipment utilization and avoid costly unexpected breakdowns. In view of some inherent drawbacks of traditional CNN and LSTM-based RUL prognostics models, a novel combination model of the ConvLSTM and the Transformer, which is based on the idea of “Extracting spatiotemporal features and applying them to RUL prediction”, is proposed for RUL prediction. The ConvLSTM network can directly extract low-dimensional spatiotemporal features from long-time degradation signals. The Transformer, based entirely on attention mechanisms, can deeply explore the mapping law between deep-level nonlinear spatiotemporal feature information and equipment service performance degradation. The proposed approach is validated with the whole-life degradation dataset of bearings from the PHM 2012 Challenge dataset and the XJTU-SY public dataset. The detailed comparative analysis shows that the proposed method has higher RUL prediction accuracy and outstanding comprehensive prediction performance.

Fault detection of axle bearings is crucial to promote the safe, efficient, and reliable running of high-speed trains. In recent decades, time−frequency analysis (TFA) techniques have been widely used in mechanical equipment fault diagnoses. Time-reassigned multisynchrosqueezing transform (TMSST), as a novel time−frequency representation (TFR) algorithm, is more suitable for dealing with strong frequency-varying signals. However, TMSST TFR results are subject to noise interference. It is difficult to extract the accurate time−frequency (TF) fault feature of the axle bearing under a complex working environment. In addition, determination of the TMSST algorithm parameters depends on the personnel’s subjective experience. Therefore, the TMSST result has a great randomicity and has the disadvantage of having a poor reliability. To address the above issues, a hybrid SVD-based denoising and self-adaptive TMSST is proposed for axle bearing fault detection in this paper. The main improvements of the proposed algorithm include the following two aspects: (1) An SVD-based denoising method using the maximum SV mean to determine the reasonable SV order is adopted to eliminate noise interference and to reserve useful fault impulse information. (2) A new evaluation metric, named time−frequency spectrum permutation entropy (TFS-PEn), is put forward for the quantitative evaluation of the performance of TFR for the TMSST, and then a water cycle algorithm (WCA)-based optimized TMSST can adaptively determine the optimal algorithm parameters. In both the simulation and experimental tests, the superiority and effectiveness of the proposed method is compared with the TMSST, short-time Fourier transform (STFT), MSST, wavelet transform (WT), and Hilbert-Huang transform (HHT) methods. The results show that the proposed algorithm has a better performance for extracting the weak fault features of axle bearing under a strong background noise environment.

Informative frequency band (IFB) selection is a challenging task in envelope analysis for the localized fault detection of rolling element bearings. In previous studies, it was often conducted with a single indicator, such as kurtosis, etc., to guide the automatic selection. However, in some cases, it is difficult for that to fully depict and balance the fault characters from impulsiveness and cyclostationarity of the repetitive transients. To solve this problem, a novel negentropy-induced multi-objective optimized wavelet filter is proposed in this paper. The wavelet parameters are determined by a grey wolf optimizer with two independent objective functions i.e., maximizing the negentropy of squared envelope and squared envelope spectrum to capture impulsiveness and cyclostationarity, respectively. Subsequently, the average negentropy is utilized in identifying the IFB from the obtained Pareto set, which are non-dominated by other solutions to balance the impulsive and cyclostationary features and eliminate the background noise. Two cases of real vibration signals with slight bearing faults are applied in order to evaluate the performance of the proposed methodology, and the results demonstrate its effectiveness over some fast and optimal filtering methods. In addition, its stability in tracking the IFB is also tested by a case of condition monitoring data sets.

A wheelset bearing is one of the main components of the train bogie frame. The early fault detection of the wheelset bearing is quite important to ensure the safety of the train. Among numerous diagnostic methods, envelope analysis is one of the most effective approaches in the detection of bearing faults which has been amply applied, but its validity greatly depends on the informative frequency band (IFB) determined. For the wheelset bearing faulty signal, it is often difficult to identify the IFB and extract fault characteristics due to the influence of complex operating conditions. To address this problem, a novel method to select optimal IFB, called the Mkurtogram, is proposed for railway wheelset bearings fault diagnosis. It takes the multipoint kurtosis (Mkurt) of unbiased autocorrelation (AC) of the squared envelope signal generated from sub-bands as assessment indicator for the first time. The fundamental concept which inspires this proposed method is to make full use of regular periodicity of AC of squared envelope signal. In the AC domain, the impulsiveness and periodicity, two distinctive signatures of the repetitive transients, have achieved a united representation by Mkurt. A simulated signal with multiple interferences and two experimental signals collected from wheelset bearings are applied to verify its performances and advantages. The results indicate that the proposed method is more effective to extract the wheelset bearings fault feature under complex interferences. It can not only decrease the influence of large impulse interference and the discrete harmonics interference, but also effectively overcome the influence of amplitude fluctuation caused by variable working conditions. Moreover, based on the periodic directivity of Mkurt, the proposed method also can be applied to the compound faults diagnosis of the wheelset bearing.

To address the problems of traditional fault diagnosis methods, such as the insensitivity of fault feature extraction, strong dependence on expert experience, insufficient generalization, and low fault recognition rate in gearbox running state detection, a gearbox fault diagnosis method based on the Markov transition field (MTF) and a Swin Transformer is proposed. First, the original one-dimensional vibration signal dataset is preprocessed. then the one-dimensional vibration signal is encoded into a two-dimensional feature map by MTF, which preserves the correlation between data and time. Second, the encoded 2D feature image dataset was input into the Swin Transformer model with a moving window and hierarchical design structure for training to realize the recognition of different faults in the gearbox. Finally, the power transmission fault diagnosis test-bed (DDS) dataset was used for experimental verification, and the fault diagnosis accuracy rate was 99.69%. Experimental results show that the proposed method has higher computational efficiency, better generalization performance, and higher fault identification accuracy than the intelligent diagnosis model based on a time-frequency diagram, CWT diagram, GAF diagram, convolutional neural network (CNN), and Vision Transformer (Vi T), which can provide a reference for fault diagnosis of gearboxes in practical industries.

Considering the serious wear of disk cutters in tunnel project and the low prediction accuracy of existing methods, a new method for wear prediction of disk cutters from an energy perspective was proposed. Based on contact mechanics, the rock breaking process of cutters was analyzed, and the combined action of sliding and rolling friction was considered. Furthermore, the wear prediction model of disk cutters was established, which realized the predicted wear of disk cutters under different geological conditions throughout the construction line. In addition, considering the influence of penetration on the wear prediction model, the predicted wear values under different penetrations were obtained, and their average values were taken as the predicted wear values of disk cutters considering penetration. Finally, the predicted wear values were compared with the actual wear value of disk cutters. The results showed that the average relative error between the predicted wear value of disk cutters considering sliding and rolling friction and the actual wear data was 12.8%. The average relative error between the predicted value of wear considering only sliding friction and the actual value of disk cutters is 18.3%. The average relative error between the predicted value of disk cutter wear considering penetration and the actual wear data was 12.2%, which proved the feasibility and accuracy of the prediction model. Meanwhile, it is more accurate than the model only considering sliding friction. The research results provided a scientific theoretical basis for the design of a tool change scheme in tunnel construction.