Weak line water vapor spectrum in the 13 200–15 000 cm−1 region

New Fourier transform spectra of water vapor are presented in the range 6500–16 400 cm−1 obtained using pathlengths of up to 800 m and long integration times. These spectra have a significantly higher signal-to-noise than previous measurements in this wavenumber range. Wavenumbers, absolute intensities and self-broadening coefficients, all with associated uncertainties, are presented for 3604 lines in the region 13 200–15 000 cm−1. Analysis of these lines using variational linelists, along with other unassigned lines from previous studies, has been conducted. This leads to 952 new line assignments to transitions involving 35 different vibrational states of H216O. A smaller number of lines are assigned to H218O and H217O

Deep convolutional neural networks for Bearings failure predictionand temperature correlation

Rolling elements bearings (REBs) is one of the most sensitive components and the common failure unit in mechanical equipment. Bearings failure prognostics, which aims to achieve an effective way to handle the increasing requirements for higher reliability and in the same time reduce unnecessary costs, has been an area of extensive research. The accurate prediction of bearings Remaining Useful Life (RUL) is indispensable for safe and lifetime-optimized operations. To monitor this vital component and planning repair work, a new intelligent method based on Wavelet Packet Decomposition (WPD) and deep learning networks is proposed in this paper. Firstly, features extraction from WPD used as input data. Secondly, these selected features are fed into deep Convolutional Neural Networks (CNNs) to construct the Health Indicator (HI). This study focuses on analysing the relationships such as correlations between the HI and temperature. We develop a solution for the Connectiomics contest dataset of bearings under different operating conditions and severity of defects. The performance of the proposed method is verified by four bearing data sets collected from experimental setup called “PRONOSTIA”. The results show that the health indicator obtains fairly high monotonicity and correlation values and it is beneficial to bearing life prediction. In addition, it is experimentally demonstrated that the proposed method is able to achieve better performance than a traditional neural network based method

Quantitative structure-activity relationship studies of TIBO derivatives using support vector machines

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