Vibration-based gearbox fault diagnosis using deep neural networks

Vibration-based analysis is the most commonly used technique to monitor the condition of gearboxes. Accurate classification of these vibration signals collected from gearbox is helpful for the gearbox fault diagnosis. In recent years, deep neural networks are becoming a promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data. In this paper, a study of deep neural networks for fault diagnosis in gearbox is presented. Four classic deep neural networks (Auto-encoders, Restricted Boltzmann Machines, Deep Boltzmann Machines and Deep Belief Networks) are employed as the classifier to classify and identify the fault conditions of gearbox. To sufficiently validate the deep neural networks diagnosis system is highly effective and reliable, herein three types of data sets based on the health condition of two rotating mechanical systems are prepared and tested. Each signal obtained includes the information of several basic gear or bearing faults. Totally 62 data sets are used to test and train the proposed gearbox diagnosis systems. Corresponding to each vibration signal, 256 features from both time and frequency domain are selected as input parameters for deep neural networks. The accuracy achieved indicates that the presented deep neural networks are highly reliable and effective in fault diagnosis of gearbox

Insolation driven biomagnetic response to the Holocene Warm Period in semi-arid East Asia

The Holocene Warm Period (HWP) provides valuable insights into the climate system and biotic responses to environmental variability and thus serves as an excellent analogue for future global climate changes. Here we document, for the first time, that warm and wet HWP conditions were highly favourable for magnetofossil proliferation in the semi-arid Asian interior. The pronounced increase of magnetofossil concentrations at ~9.8 ka and decrease at ~5.9 ka in Dali Lake coincided respectively with the onset and termination of the HWP, and are respectively linked to increased nutrient supply due to postglacial warming and poor nutrition due to drying at ~6 ka in the Asian interior. The two-stage transition at ~7.7 ka correlates well with increased organic carbon in middle HWP and suggests that improved climate conditions, leading to high quality nutrient influx, fostered magnetofossil proliferation. Our findings represent an excellent lake record in which magnetofossil abundance is, through nutrient availability, controlled by insolation driven climate changes.This research was supported by the NSFC grant 41330104 and the 973 program grant 2012CB821900. J.X. was supported by the 973 program grant 2010CB833400 and the NSFC grant 41130101. J.L. received support from the NSFC grant 41374004. C.D. acknowledges further support from the NSFC grant 40925012 and the CAS Bairen Program

Benchmarking Component Analysis of Remanent Magnetization Curves With a Synthetic Mixture Series: Insight into the Reliability of Unmixing Natural Samples

Geological samples often contain several magnetic components associated with different geological processes. Component analysis of remanent magnetization curves has been widely applied to decompose convoluted information. However, the reliability of commonly used methods is poorly assessed as independent verification is rarely available. For this purpose, we designed an experiment using a series of mixtures of two endmembers to benchmark unmixing methods for isothermal remanent magnetization (IRM) acquisition curves. First‐order reversal curves (FORC) diagrams were analyzed for comparison. It is demonstrated that the parametric method, which unmixes samples using specific probability distributions, may result in biased estimates. In contrast, an endmember‐based IRM unmixing approach yielded better quantitative results, which are comparable to the results obtained by FORC analysis based on principle component analysis (FORC‐PCA). We demonstrate that endmember‐based methods are in principle more suitable for unmixing a collection of samples with common endmembers; however, the level of decomposition will vary depending on the difference between the true endmembers that are associated with distinctive processes and the empirical endmembers used for unmixing. When it is desired to further decompose endmembers, the parametric unmixing approach remains a valuable means of inferring their underlying components. We illustrate that the results obtained by endmember‐based and parametric methods can be quantitatively combined to provide improved unmixing results at the level of parametric model distributions.The work was supported by the National Natural Science Foundation of China (41621004 and 41904070) and the Strategic Priority Research Program of Chinese Academy of Sciences (XDB18010000). This study was also supported by the National Institute of Polar Research (NIPR) through Advanced Project (KP‐7 and KP306) and JSPS KAKENHI grants (15K13581, 16H04068, 17H06321, and 18K13638). X. Z. acknowledges the Australian Research Council Discovery Projects DP200100765 and the National Natural Science Foundation of China (grant 41920104009) for financial supports

Cell transcriptomic atlas of the non-human primate Macaca fascicularis.

Studying tissue composition and function in non-human primates (NHPs) is crucial to understand the nature of our own species. Here we present a large-scale cell transcriptomic atlas that encompasses over 1 million cells from 45 tissues of the adult NHP Macaca fascicularis. This dataset provides a vast annotated resource to study a species phylogenetically close to humans. To demonstrate the utility of the atlas, we have reconstructed the cell-cell interaction networks that drive Wnt signalling across the body, mapped the distribution of receptors and co-receptors for viruses causing human infectious diseases, and intersected our data with human genetic disease orthologues to establish potential clinical associations. Our M. fascicularis cell atlas constitutes an essential reference for future studies in humans and NHPs.We thank W. Liu and L. Xu from the Huazhen Laboratory Animal Breeding Centre for helping in the collection of monkey tissues, D. Zhu and H. Li from the Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory) for technical help, G. Guo and H. Sun from Zhejiang University for providing HCL and MCA gene expression data matrices, G. Dong and C. Liu from BGI Research, and X. Zhang, P. Li and C. Qi from the Guangzhou Institutes of Biomedicine and Health for experimental advice or providing reagents. This work was supported by the Shenzhen Basic Research Project for Excellent Young Scholars (RCYX20200714114644191), Shenzhen Key Laboratory of Single-Cell Omics (ZDSYS20190902093613831), Shenzhen Bay Laboratory (SZBL2019062801012) and Guangdong Provincial Key Laboratory of Genome Read and Write (2017B030301011). In addition, L.L. was supported by the National Natural Science Foundation of China (31900466), Y. Hou was supported by the Natural Science Foundation of Guangdong Province (2018A030313379) and M.A.E. was supported by a Changbai Mountain Scholar award (419020201252), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA16030502), a Chinese Academy of Sciences–Japan Society for the Promotion of Science joint research project (GJHZ2093), the National Natural Science Foundation of China (92068106, U20A2015) and the Guangdong Basic and Applied Basic Research Foundation (2021B1515120075). M.L. was supported by the National Key Research and Development Program of China (2021YFC2600200).S

A Kernel Gabor-Based Weighted Region Covariance Matrix for Face Recognition

This paper proposes a novel image region descriptor for face recognition, named kernel Gabor-based weighted region covariance matrix (KGWRCM). As different parts are different effectual in characterizing and recognizing faces, we construct a weighting matrix by computing the similarity of each pixel within a face sample to emphasize features. We then incorporate the weighting matrices into a region covariance matrix, named weighted region covariance matrix (WRCM), to obtain the discriminative features of faces for recognition. Finally, to further preserve discriminative features in higher dimensional space, we develop the kernel Gabor-based weighted region covariance matrix (KGWRCM). Experimental results show that the KGWRCM outperforms other algorithms including the kernel Gabor-based region covariance matrix (KGCRM)