دانلود مقاله ISI انگلیسی شماره 46573
ترجمه فارسی عنوان مقاله

طبقه بندی شرایط ماشین آلات با استفاده از نمونه نفت و رگرسیون لجستیک باینری

عنوان انگلیسی
Classifying machinery condition using oil samples and binary logistic regression
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
46573 2015 10 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Mechanical Systems and Signal Processing, Volumes 60–61, August 2015, Pages 316–325

ترجمه کلمات کلیدی
رگرسیون لجستیک - تقسیم بندی - تجزیه و تحلیل نفت - کامیون معدن - بهداشت و ماشین آلات - شبکه های عصبی - پشتیبانی از ماشین بردار - منحنی مشخصه گیرنده عملیاتی
کلمات کلیدی انگلیسی
Logistic regression; Classification; Oil analysis; Mining trucks; Machine health; Neural networks; Support vector machine; Receiver operating characteristic curve
پیش نمایش مقاله
پیش نمایش مقاله  طبقه بندی شرایط ماشین آلات با استفاده از نمونه نفت و رگرسیون لجستیک باینری

چکیده انگلیسی

The era of big data has resulted in an explosion of condition monitoring information. The result is an increasing motivation to automate the costly and time consuming human elements involved in the classification of machine health. When working with industry it is important to build an understanding and hence some trust in the classification scheme for those who use the analysis to initiate maintenance tasks. Typically “black box” approaches such as artificial neural networks (ANN) and support vector machines (SVM) can be difficult to provide ease of interpretability. In contrast, this paper argues that logistic regression offers easy interpretability to industry experts, providing insight to the drivers of the human classification process and to the ramifications of potential misclassification. Of course, accuracy is of foremost importance in any automated classification scheme, so we also provide a comparative study based on predictive performance of logistic regression, ANN and SVM. A real world oil analysis data set from engines on mining trucks is presented and using cross-validation we demonstrate that logistic regression out-performs the ANN and SVM approaches in terms of prediction for healthy/not healthy engines.