Xiaoqiang Yang

This paper presents a precise time-frequency positioning method that assists in the fault diagnosis of each piston in piston pumps. Firstly, in response to the short-time broadband characteristics of the piston pump vibration pulse signal, the Local Maximum Transient Extraction Transform (LTET) is proposed to provide a time-frequency representation with more concentrated energy. Secondly, the parameter setting of LTET is discussed, and the improved LTET method (ILTET) is proposed to achieve the precise positioning of the 2D GD of piston pump vibration pulses. Finally, by combining ILTET with the time-frequency envelope and time-frequency mask method, a Periodic Pulse Screening method based on ILTET (ILTET-PPS) for piston pumps is proposed, which can effectively screen the piston vibration pulse, filter out random pulses and noise interference. Experimental validation using vibration data from six piston pump operating conditions demonstrates that the Rényi entropy value of the TFR obtained by ILTET is the smallest, and the non-zero values at the center frequency slice can accurately indicate the piston pulse components while disregarding noise components. In contrast, the TFR center frequency slices of the STET, TET, TMSST, and STFT methods exhibit a significant amount of noise components. This proves that ILTET exhibits superior energy concentration and noise robustness compared to other methods. The PPS method can more accurately extract high-quality piston pulses based on the TFR provided by ILTET, while the TFR from other methods can lead to the extraction of noise components in the PPS process. This demonstrates the effectiveness and superiority of ILTET-PPS.

This article proposes a time–frequency localization-assisted method for extracting fault features from piston pumps. This method can optimize the sample setting of intelligent fault diagnosis models, enhance diagnostic effectiveness, and reduce model complexity. First, ILTET-PPSE method is proposed, which integrates the improved local maximum transient extraction transform (ILTET) and periodic pulse screening and extraction (PPSE) methods. This approach enables precise localization, segmentation, extraction, and combination of vibration signals from a single piston. Subsequently, considering the nonlinear and nonstationary characteristics of the vibration signals, a novel fault feature extraction algorithm termed fractional-order bubble entropy (FBE) is proposed. Furthermore, a fault feature extraction method, PPSE-FBE, assisted by ILTET-PPSE, is introduced. PPSE-FBE demonstrates significant advantages in terms of parameter setting, noise robustness, and fault feature extraction sufficiency. The fault diagnosis model trained with the features extracted by PPSE-FBE can achieve higher recognition accuracy.

Traditional fault diagnosis methods often require extracting features from raw vibration signals based on prior knowledge, which are then input into intelligent classifiers for pattern recognition. This process is prone to information loss and can be inaccurate when relying on human experience for fault identification. To address this issue, this paper proposes an intelligent fault classification and diagnosis model for rolling bearings based on Fast Fourier Transform (FFT) combined with a time convolutional network (SE-TCN) incorporating an attention mechanism, with a Support Vector Machine (SVM) used as the classifier. First, the FFT is applied to transform the collected raw time-domain data of bearing faults into the frequency domain, obtaining the sequence information in the frequency domain. Second, the frequency–domain sequence data are fed into the SE-TCN model, which uses multiple convolutional layers and a channel attention mechanism to extract deep fault features. Finally, the extracted feature vectors are input into the SVM classifier, and the Particle Swarm Optimization (PSO) algorithm is used to optimize the SVM parameters. The optimal separating hyperplane is obtained through training to classify the fault types of the rolling bearings. To verify the effectiveness and diagnostic performance of the proposed method, experiments are conducted using bearing fault datasets from Case Western Reserve University (CWRU) and a laboratory self-built fault diagnosis experimental platform. The experimental results show that the classification accuracy of the proposed method exceeds 99% on the CWRU test dataset, and it also demonstrates advantages in handling small sample data, with an accuracy of over 90%. Additionally, it exhibits good diagnostic performance on the bearing fault data collected from the laboratory self-built platform. The results validate the effectiveness of the proposed classification model in bearing a fault diagnosis.