RESUMO
A turnout switch machine is key equipment in a railway, and its fault condition has an enormous impact on the safety of train operation. Electrohydraulic switch machines are increasingly used in high-speed railways, and how to extract effective fault features from their working condition monitoring signal is a difficult problem. This paper focuses on the sectionalized feature extraction method of the oil pressure signal of the electrohydraulic switch machine and realizes the fault detection of the switch machine based on this method. First, the oil pressure signal is divided into three stages according to the working principle and action process of the switch machine, and multiple features of each stage are extracted. Then the max-relevance and min-redundancy (mRMR) algorithm is applied to select the effective features. Finally, the mini batch k-means method is used to achieve unsupervised fault diagnosis. Through experimental verification, this method can not only derive the best sectionalization mode and feature types of the oil pressure signal, but also achieve the fault diagnosis and the prediction of the status of the electrohydraulic switch machine.
RESUMO
Planetary gearbox is a critical component for many mechanical systems. It is essential to monitor the planetary gearbox health and performance in order to maintain the whole machine works well. The methodology of mechanical fault diagnosis is increasingly intelligent with the extensive application of deep learning. However, the cross-domain issue caused by varying working conditions becomes an enormous encumbrance to fault diagnosis based on deep learning. In this paper, in order to fully excavate potentialities of deep neural network architectures, a novel generative adversarial learning method was introduced for a completely new fault diagnosis based on a deep convolution neural network. In addition, the intelligent fault diagnostic scheme for planetary gearbox under varying speed conditions was developed. After that, some experiments on measured vibration signals of planetary gearbox were conducted to verify the validity and efficiency of the fault diagnostic scheme. The results showed that the proposed method enhanced the capability of the intelligent diagnosis for planetary gear faults under varying speed conditions.
RESUMO
A method for the simultaneous determination of TM208 and its major metabolite (sulfine 4-methyl-piperazine-1-carbodithioc acid 3-cyano-3,3-diphenylpropyl ester, TM208-SO) was developed and validated for the first time. The analytes were extracted from plasma samples by liquid-liquid extraction and analyzed using high-performance liquid chromatography. Flunarizine hydrochloride was used as the internal standard. Chromatographic separations were performed on a Diamonsil C(18) analytical column. The mobile phases consisted of 20 mM ammonium acetate adjusted to pH 4.20 with acetic acid (solvent A) and acetonitrile (solvent B). The analytes were detected at 254 nm after linear gradient elution. The flow rate was 0.8 mL/min. Linearity was obtained over the concentration range of 0.104-5.20 microg/mL for TM208 and 0.145-5.80 microg/mL for TM208-SO in rat plasma. The limit of quantification was 0.104 microg/mL for TM208 and 0.145 microg/mL for TM208-SO, respectively. The inter- and intra-day precision was less than 12.8% for TM208 and 14.1% for TM208-SO. And the accuracy was 96.2-111.1% for TM208 and 95.5-108.6% for TM208-SO. This analytic procedure was applied to a pharmacokinetic study of TM208 and TM208-SO in rats, and the pharmacokinetic parameters were calculated.
