Bearing fault feature extraction method based on complete ensemble empirical mode decomposition with adaptive noise

As an important part of rotating machinery, bearings play an important role in large-scale mechanical equipment. Abnormal bearing conditions may cause the machine to malfunction, or even evolve into a serious accident. Therefore, the accurate and timely fault diagnosis of the bearing is of great significance. Based on EMD, this paper introduces the working principles and characteristics of EEMD and CEEMDAN, respectively. Then the signal was decomposed by EEMD and CEEMDAN respectively. The simulation results show that CEEMDAN has better effect on signal decomposition. Then, comparing the effect of CEEMDAN and EEMD on bearing fault feature frequency extraction, the experiment proves that CEEMDAN has a better ability to preserve original signal and eliminate noise than EEMD method, and can extract bearing fault feature more accurately and timely

Single-Atom Catalyst Aggregates: Size-Matching is Critical to Electrocatalytic Performance in Sulfur Cathodes

Electrocatalysis is critical to the performance displayed by sulfur cathodes. However, the constituent electrocatalysts and the sulfur reactants have vastly different molecular sizes, which ultimately restrict electrocatalysis efficiency and hamper device performance. Herein, the authors report that aggregates of cobalt single-atom catalysts (SACs) attached to graphene via porphyrins can overcome the challenges associated with the catalyst/reactant size mismatch. Atomic-resolution transmission electron microscopy and X-ray absorption spectroscopy measurements show that the Co atoms present in the SAC aggregates exist as single atoms with spatially resolved dimensions that are commensurate the sulfur species found in sulfur cathodes and thus fully accessible to enable 100% atomic utilization efficiency in electrocatalysis. Density functional theory calculations demonstrate that the Co SAC aggregates can interact with the sulfur species in a synergistic manner that enhances the electrocatalytic effect and promote the performance of sulfur cathodes. For example, Li-S cells prepared from the Co SAC aggregates exhibit outstanding capacity retention (i.e., 505 mA h g(-1) at 0.5 C after 600 cycles) and excellent rate capability (i.e., 648 mA h g(-1) at 6 C). An ultrahigh area specific capacity of 12.52 mA h cm(-2) is achieved at a high sulfur loading of 11.8 mg cm(-2)

Remaining Useful Life Prediction and Fault Diagnosis of Rolling Bearings Based on Short-Time Fourier Transform and Convolutional Neural Network

Rolling bearings play a pivotal role in rotating machinery. The remaining useful life prediction and fault diagnosis of bearings are crucial to condition-based maintenance. However, traditional data-driven methods usually require manual extraction of features, which needs rich signal processing theory as support and is difficult to control the efficiency. In this study, a bearing remaining life prediction and fault diagnosis method based on short-time Fourier transform (STFT) and convolutional neural network (CNN) has been proposed. First, the STFT was adopted to construct time-frequency maps of the unprocessed original vibration signals that can ensure the true and effective recovery of the fault characteristics in vibration signals. Then, the training time-frequency maps were used as an input of the CNN to train the network model. Finally, the time-frequency maps of testing signals were inputted into the network model to complete the life prediction or fault identification of rolling bearings. The rolling bearing life-cycle datasets from the Intelligent Management System were applied to verify the proposed life prediction method, showing that its accuracy reaches 99.45%, and the prediction effect is good. Multiple sets of validation experiments were conducted to verify the proposed fault diagnosis method with the open datasets from Case Western Reserve University. Results show that the proposed method can effectively identify the fault classification and the accuracy can reach 95.83%. The comparison with the fault diagnosis classification effects of backpropagation (BP) neural network, particle swarm optimization-BP, and genetic algorithm-BP further proves its superiority. The proposed method in this paper is proved to have strong ability of adaptive feature extraction, life prediction, and fault identification