Intelligent Fault Diagnosis of Rolling Bearings Based on Markov Transition Field and Mixed Attention Residual Network

To address the problems of existing methods that struggle to effectively extract fault features and unstable model training using unbalanced data, this paper proposes a new fault diagnosis method for rolling bearings based on a Markov Transition Field (MTF) and Mixed Attention Residual Network (MARN). The acquired vibration signals are transformed into two-dimensional MTF feature images as network inputs to avoid the loss of the original signal information, while retaining the temporal correlation; then, the mixed attention mechanism is inserted into the residual structure to enhance the feature extraction capability, and finally, the network is trained and outputs diagnostic results. In order to validate the feasibility of the MARN, other popular deep learning (DL) methods are compared on balanced and unbalanced datasets divided by a CWRU fault bearing dataset, and the proposed method results in superior performance. Ultimately, the proposed method achieves an average recognition accuracy of 99.5% and 99.2% under the two categories of divided datasets, respectively

Ultra-long-haul L-band WDM transmission over a standard single-mode fiber loop using DCF+CFBG hybrid dispersion compensation

We demonstrate a 10.7Gb/s-line-rate L-band WDM loop transmission over 1890km standard single-mode fiber (SSMF) with 100km amplifier spacing as well as non-return-to-zero (NRZ) format. For the first time, dispersion compensating fiber (DCF) plus chirped fiber Bragg grating (CFBG) is employed for hybrid inline dispersion compensation. The power penalty of each channel is less than 3dB after three loop transmission. The experimental results show that high-performance-CFBGs can be successfully used in ultra-long haul (>1000km) WDM systems. We also point out that all-CFBG compensation scheme is not suitable for re-circulating loop transmissions.EI