Fault_Detection.zip

所属分类:人工智能/神经网络/深度学习
开发工具:Python
文件大小:8231KB
下载次数:1
上传日期:2020-03-12 11:31:24
上 传 者去年夏天1013
说明:  svm python 实现对故障信号的分类处理
(svm python Realize the classified processing of fault signals)

文件列表:
Faults_Detection.pptx (1099826, 2019-08-18)
LOG10-20171018_m.csv (1323880, 2019-08-18)
grubbs (0, 2019-08-18)
grubbs\bin (0, 2019-08-18)
grubbs\bin\deploy-gh-pages.sh (362, 2019-08-18)
grubbs\grubbs.pdf (109133, 2019-08-18)
grubbs\lib (0, 2019-08-18)
grubbs\lib\criticalValueTable.js (1439, 2019-08-18)
grubbs\lib\index.js (3545, 2019-08-18)
grubbs\node_modules (0, 2019-08-18)
grubbs\node_modules\acorn-jsx (0, 2019-08-18)
grubbs\node_modules\acorn-jsx\LICENSE (1068, 2019-08-18)
grubbs\node_modules\acorn-jsx\index.js (71, 2019-08-18)
grubbs\node_modules\acorn-jsx\inject.js (13538, 2019-08-18)
grubbs\node_modules\acorn-jsx\node_modules (0, 2019-08-18)
grubbs\node_modules\acorn-jsx\node_modules\acorn (0, 2019-08-18)
grubbs\node_modules\acorn-jsx\node_modules\acorn\AUTHORS (851, 2019-08-18)
grubbs\node_modules\acorn-jsx\node_modules\acorn\CHANGELOG.md (3444, 2019-08-18)
grubbs\node_modules\acorn-jsx\node_modules\acorn\LICENSE (1086, 2019-08-18)
grubbs\node_modules\acorn-jsx\node_modules\acorn\bin (0, 2019-08-18)
grubbs\node_modules\acorn-jsx\node_modules\acorn\bin\acorn (2192, 2019-08-18)
grubbs\node_modules\acorn-jsx\node_modules\acorn\bin\generate-identifier-regex.js (1737, 2019-08-18)
grubbs\node_modules\acorn-jsx\node_modules\acorn\bin\update_authors.sh (307, 2019-08-18)
grubbs\node_modules\acorn-jsx\node_modules\acorn\dist (0, 2019-08-18)
grubbs\node_modules\acorn-jsx\node_modules\acorn\dist\acorn.es.js (112893, 2019-08-18)
grubbs\node_modules\acorn-jsx\node_modules\acorn\dist\acorn.js (119072, 2019-08-18)
grubbs\node_modules\acorn-jsx\node_modules\acorn\dist\acorn_loose.es.js (41067, 2019-08-18)
grubbs\node_modules\acorn-jsx\node_modules\acorn\dist\acorn_loose.js (44948, 2019-08-18)
grubbs\node_modules\acorn-jsx\node_modules\acorn\dist\walk.es.js (11950, 2019-08-18)
grubbs\node_modules\acorn-jsx\node_modules\acorn\dist\walk.js (13179, 2019-08-18)
grubbs\node_modules\acorn-jsx\node_modules\acorn\package.json (4567, 2019-08-18)
grubbs\node_modules\acorn-jsx\node_modules\acorn\rollup (0, 2019-08-18)
grubbs\node_modules\acorn-jsx\node_modules\acorn\rollup\config.bin.js (274, 2019-08-18)
grubbs\node_modules\acorn-jsx\node_modules\acorn\rollup\config.loose.js (374, 2019-08-18)
grubbs\node_modules\acorn-jsx\node_modules\acorn\rollup\config.main.js (235, 2019-08-18)
grubbs\node_modules\acorn-jsx\node_modules\acorn\rollup\config.walk.js (243, 2019-08-18)
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# Fault Detection & Diagnostics Of ElectroMechanical Devices Fault Detection & Diagnostics of ElectroMechanical Devices is a project based on Sinfonia conveyor belts used in airports. Outliers detection, classication & prediction were the main tasks involved. Various statistical tests like Grubbs test, Modified Thompson Tau test were used to find the outliers in the given datasets using Python and JavaScript programming languages. Finding outliers separately in the different features of the dataset itself is fault detection and classification. Further a prediction was made using algorithms like SVM, Decision trees to know the next series of data whether faulty or not. If faulty over a run was found then suitable diagnosis was applied to make the machine run smooth.

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