Rotating machinery usually operates under variable-speed conditions, and how to effectively handle nonstationary signal in fault diagnosis is a critical task. The time-frequency analysis (TFA) method is widely used in the processing of nonstationary signal. To improve the time-frequency resolution and clearly identify instantaneous frequency (IF) characteristics, the adaptive generalized S-synchroextracting transform (AGSSET), which is a novel TFA method proposed in this paper. Firstly, a new transform named adaptive generalized S-transform (AGST) is put forward by optimizing the window function of generalized S transform. In this paper, an adaptive window function optimization method based on the frequency spectrum of the vibration signal is introduced, and the energy concentration measure is used to determine the window function’s parameters in AGST. Simultaneously, the synchrony extraction idea is incorporated into the AGST, then the AGSSET is derived. To address more complex IF characteristics, the synchronous extraction operator (SEO) is reconstructed. In the simulation experiment, the GMLC signal model is selected to represent nonstationary signal and to verify the effectiveness of the proposed method. In addition, bearing fault data is also used for fault diagnosis experiments. The results of both numerical simulation and experimental analysis indicate that AGSSET performs well in identifying the time-varying IF characteristic in nonstationary signals. It can also efficiently detect faults with high accuracy and strong stability.

In intelligent fault diagnosis for rolling bearings, the variable operating conditions of rotating machinery lead to inconsistent distributions between training and testing data, inevitably decreasing fault diagnosis accuracy. To overcome this issue, we propose a bearing fault diagnosis method based on transfer learning with a dual-flow manifold residual network and improved capsule network (DMRCN). Firstly, we introduce a dual-flow manifold residual network structure consisting of continuous wide-kernel convolution, continuous narrow-kernel convolution, and manifold learning spatial information description blocks. This architecture is utilized to extract low-level features from the data. Then, we present a novel architecture of the capsule network with self-attention output. We incorporate a self-attention mechanism into the dynamic routing algorithm to transfer richer high-level features from the low-level capsule layer to the higher-level capsule layer. During training, we propose a loss function known as global-local distribution discrepancy loss to enhance the model’s generalization ability and accelerate model convergence. Finally, we validate the performance of DMRCN in cross-condition transfer fault diagnosis on both the Case Western Reserve University public dataset and a laboratory-built bearing experimental rig dataset. Experimental results demonstrate that DMRCN outperforms other commonly used deep transfer learning methods regarding fault diagnosis across different operating conditions.

To address the low accuracy of current one-dimensional signal recognition for coal-rock cutting vibration and the low efficiency of traditional static neural networks, this paper proposes an interpretable recognition method that combines Markov transition field (MTF) and channel-selective neural networks. Firstly, by decomposing and reconstructing the energy of wavelet packets and denoising the signal, it is transformed into MTF images with temporal correlation to improve signal processing efficiency. Then, a channel selective module is proposed to replace traditional convolutional layers, enhancing the model’s ability to extract data features. A spatial shift mechanism is improved to alleviate the problem of weight degradation and improve the model’s generalization capability. Experimental results on the self-built dataset show that MTF can effectively capture the dynamic characteristics of time series data compared to other signal transformation methods. ResNet + SelectConv achieved recognition accuracy of 97.375% without adding extra parameters and maintained good robustness in noise environments up to 6 dB, significantly outperforming traditional methods. This study improves the efficiency and accuracy of coal-rock cutting state recognition and provides new insights for signal processing in environments with limited underground computational resources.

Rotating machinery under variable-speed conditions suffers from amplitude modulation (AM) effects induced by speed fluctuations, complicating accurate fault detection. To address this issue, an amplitude normalization method based on support vector regression (SVR) is proposed to estimate and remove the AM effects. The method employs a correlation-based feature selection strategy to construct feature vectors strongly associated with rotational speed, thereby enabling the accurate quantification of speed-induced AM effects. The robust nonlinear fitting capability of SVR is then utilized to model and remove these effects, enhancing fault signal clarity. The proposed method is validated through two case studies and compared with advanced amplitude normalization techniques, demonstrating its superior accuracy, robustness, and reliability. Experimental results demonstrate that the proposed method accurately estimates and eliminates speed-induced AM, significantly improving fault diagnosis accuracy by up to 34.7%.

The failure of commutation will cause the abrupt change of the operating parameters of the DC system. In this paper, the mechanism and physical process of commutation failure are analyzed. In view of the changing characteristics of inverter side valve current and DC current in the case of commutation failure, two kinds of S transform entropy are introduced into the detection of commutation failure of DC system in combination with S transform entropy’s excellent signal change feature recognition ability. In addition, the detection performance of the two s-transform entropies is fully considered in the case of no commutation failure, critical commutation failure and commutation failure. The simulation results based on PSCAD/EMTDC preliminarily verify the validity and accuracy of the two kinds of S transform entropy in dc system fault detection, which provides a basis for the further application of S transform entropy in dc system fault detection field.