Dissolved gas-in-oil analysis (DGA) is a sensitive and reliable technique for the detection of incipient fault condition within oil-immersed transformers. The presence of certain key gases is monitored and quantified. There are a number of methods developed for analyzing these gases and interpreting their significance: Key Gas, Rogers Ratio, Doernenburg, Logarithmic Nomograph, IEC Ratio and Duval Triangle. This paper investigates the accuracy and consistency of these methods in interpreting the transformer condition. The evaluation is carried out on DGA data obtained from the local power utilities and from published papers. The data consists of 92 different cases. The key gases considered are hydrogen, methane, ethane, ethylene and acetylene. A MATLAB program was developed to automate the evaluation of the methods.
Background
In this study, we aimed to perform a comprehensive analysis on the metagenomic next-generation sequencing for the etiological diagnosis of septic patients, and further to establish optimal read values for detecting common pathogens.
Methods
In this single-center retrospective study, septic patients who underwent pathogen detection by both microbial culture and metagenomic next-generation sequencing in the intensive care unit of the Second People’s Hospital of Shenzhen from June 24, 2015, to October 20, 2019, were included.
Results
A total of 193 patients with 305 detected specimens were included in the final analysis. The results of metagenomic next-generation sequencing showed significantly higher positive rates in samples from disparate loci, including blood, bronchoalveolar lavage fluid, and cerebrospinal fluid, as well as in the determination of various pathogens. The optimal diagnostic reads were 2893, 1825.5, and 892.5 for Acinetobacter baumannii, Pseudomonas aeruginosa, and Klebsiella pneumoniae, respectively.
Conclusions
The metagenomic next-generation sequencing is capable of identifying multiple pathogens in specimens from septic patients, and shows significantly higher positive rates than culture-based diagnostics. The optimal diagnostic reads for frequently detected microbes might be useful for the clinical application of metagenomic next-generation sequencing in terms of timely and accurately determining etiological pathogens for suspected and confirmed cases of sepsis due to well-performed data interpretation.
Continuous on-line monitoring of partial discharge (PD) activities involves recording large amounts of data and a challenging task is to extract useful information from them. Thus, data mining plays a very important role. In this paper, data of PD in power cables are obtained from on-site monitoring and from laboratory test. The PD patterns in terms of the univariate phase-resolved distributions are analysed. An alternative method to that using statistical moments for characterizing the patterns is proposed. Principal component analysis (PCA) is used for feature extraction as well as dimensionality reduction in the proposed method. The results obtained are further processed using self-organizing mapping (SOM) to enable better visualisation of the trend of the PD activities.
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