Since minimum correlated generalized Lp/Lq deconvolution (MCG-Lp/Lq-D) cannot simultaneously have a good sensing ability to repetitive fault impulses and a strong robustness to noise when processing low SNR signals, a new deconvolution method, minimum improved correlated generalized Lp/Lq deconvolution (MICG-Lp/Lq-D), is proposed in this article. The proposed method is designed based on a novel norm, the improved correlated generalized Lp/Lq (ICG-Lp/Lq) norm. The ICG-Lp/Lq norm not only has the low-order statistics form that is similar to the correlated generalized Lp/Lq (CG-Lp/Lq) norm used in MCG-Lp/Lq-D, but also has the high shift-order signal correlation construction similar to the correlated kurtosis (CK) norm used in maximum correlated kurtosis deconvolution (MCKD). This guarantees that the proposed norm is robust enough to strong noise and can improve its sensing ability for repetitive impulses simultaneously. This feature of the ICG-Lp/Lq norm ensures that MICG-Lp/Lq-D can suppress outliers and extract weak impacts when processing low SNR signals. Simulated and experimental case studies verify the performance of the proposed method. The results indicate that the proposed method is robust enough to strong noise and very sensitive to repetitive impulses simultaneously, which indeed overcomes the aforementioned defects of MCG-Lp/Lq-D. Therefore, MICG-Lp/Lq-D can be considered a promising tool for detecting repetitive impulses.

Convolutional sparse representation based on local orthogonal matching pursuit (SR-LocOMP) can recover wheelset bearing fault impulses without being affected by random slippage, and plays an important role in wheelset bearing fault diagnosis. However, the performance of SR-LocOMP at low signal-to-noise ratio (SNR) is not satisfied. In addition, when the analyzed signals contain compound faults, the fault feature extraction ability of SR-LocOMP will be greatly affected. To further improve the performance of SR-LocOMP, this paper proposes an improved SR-LocOMP algorithm, named improved SR-LocOMP. The proposed method introduces scale space representation (SSR) for frequency band segmentation, and then performs SR-LocOMP for signals in each segmented frequency band. Reconstructed signals corresponding to each segmented frequency band are obtained separately. Envelope spectral L2/L1 norm is used to measure the fault information content of each reconstructed signal to extract more accurate and richer impulse response features caused by bearing faults. The proposed method not only has the ability to detect compound faults, but also can effectively reduce noise interference. Through simulation and experimental analysis, the superiority of the proposed method is verified.

To inference the reliability of product at normal stress conditions, accelerated life test (ALT) is usually used to obtain failure information rapidly. However, most of the researches on ALT are focused on the statistical inference with single stress, which makes it difficult to gain accurate reliability through applying ALT method in single stress case. In this article, statistical inference is considered on dual constant-stress accelerated life test (DCSALT) model for the Weibull distribution with Type-I censored data. On the assumption that the Weibull distribution has constant shape parameter, and a log-linear acceleration model between its scale parameter and the test stress level combination. Using the profile maximum likelihood estimation (PMLE) method to estimate the unknown parameters in DCSALT model for Weibull distribution, and this process is completed by Newton-Raphson algorithm. Moreover, the observed information matrix is obtained in order to construct asymptotic confidence intervals of the unknown parameters. Finally, the numerical study is presented to illustrate the effectiveness of the maximum likelihood estimators (MLEs).

Bearing fault diagnosis is essential for ensuring the reliability and operational efficiency of rotating machinery. Traditional diagnostic methods often rely on vibration acceleration signals, which require intrusive hardware for signal acquisition and struggle to detect specific fault types, such as cage faults. Because bearings are often located deep within mechanical transmission systems, their vibration characteristics do not propagate effectively to external monitoring points, making vibration acceleration sensors less sensitive to certain faults. To address these challenges, we propose CAFormer, a complex attention Transformer for bearing fault diagnosis is based on motor current signals. In traction motor control, the Clarke and Park transforms are commonly used to convert three-phase ac signals into the dq coordinate system, where D-axis and Q-axis currents are represented as orthogonal signals. In this article, we first propose a complex attention to process dual-channel orthogonal data and enhance fault feature separation. Second, to address the imbalance between fault and healthy samples, we propose a new loss function that assigns adaptive optimization gradients to each sample, thereby accelerating model convergence. We conducted extensive experiments on two datasets: the Paderborn University dataset and a bearing dataset generated using a mechanical transmission simulator. Our experiments demonstrate that CAFormer significantly reduces computational costs while achieving state-of-the-art performance, surpassing existing methods in both fault classification accuracy and adaptability to imbalanced datasets.