کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
5518913 1544039 2016 4 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Feature selection and classification of mechanical fault of an induction motor using random forest classifier
ترجمه فارسی عنوان
انتخاب ویژگی و طبقه بندی گسل مکانیکی موتور القایی با استفاده از طبقه بندی تصادفی جنگل
موضوعات مرتبط
مهندسی و علوم پایه علوم زمین و سیارات علوم زمین و سیاره ای (عمومی)
چکیده انگلیسی

SummaryFault detection and diagnosis is the most important technology in condition-based maintenance (CBM) system for rotating machinery. This paper experimentally explores the development of a random forest (RF) classifier, a recently emerged machine learning technique, for multi-class mechanical fault diagnosis in bearing of an induction motor. Firstly, the vibration signals are collected from the bearing using accelerometer sensor. Parameters from the vibration signal are extracted in the form of statistical features and used as input feature for the classification problem. These features are classified through RF classifiers for four class problems. The prime objective of this paper is to evaluate effectiveness of random forest classifier on bearing fault diagnosis. The obtained results compared with the existing artificial intelligence techniques, neural network. The analysis of results shows the better performance and higher accuracy than the well existing techniques.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Perspectives in Science - Volume 8, September 2016, Pages 334-337
نویسندگان
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