Fault Detection of Anti-friction Bearing using Ensemble Machine Learning Methods
Author(s):
Article Type:
Research/Original Article (دارای رتبه معتبر)
Abstract:
Anti-Friction Bearing (AFB) is a very important machine component and its unscheduled failure leads to cause of malfunction in wide range of rotating machinery which results in unexpected downtime and economic loss. In this paper, ensemble machine learning techniques are demonstrated for the detection of different AFB faults. Initially, statistical features were extracted from temporal vibration signals and are collected using experimental test rig for different input parameters like load, speed and bearing conditions. These features are ranked using two techniques, namely Decision Tree (DT) and Randomized Lasso (R Lasso), which are further used to form training and testing input feature sets to machine learning techniques. It uses three ensemble machine learning techniques for AFB fault classification namely Random Forest (RF), Gradient Boosting Classifier (GBC) and Extra Tree Classifier (ETC). The impact of number of ranked features and estimators have been studied for ensemble techniques. The result showed that the classification efficiency is significantly influenced by the number of features but the effect of number of estimators is minor. The demonstrated ensemble techniques give more accuracy in classification as compared to tuned SVM with same experimental input data. The highest AFB fault classification accuracy 98% is obtained with ETC and DT feature ranking.
Keywords:
Language:
English
Published:
International Journal of Engineering, Volume:31 Issue: 11, Nov 2018
Pages:
1972 to 1981
magiran.com/p1907439
دانلود و مطالعه متن این مقاله با یکی از روشهای زیر امکان پذیر است:
اشتراک شخصی
با عضویت و پرداخت آنلاین حق اشتراک یکساله به مبلغ 1,390,000ريال میتوانید 70 عنوان مطلب دانلود کنید!
اشتراک سازمانی
به کتابخانه دانشگاه یا محل کار خود پیشنهاد کنید تا اشتراک سازمانی این پایگاه را برای دسترسی نامحدود همه کاربران به متن مطالب تهیه نمایند!
توجه!
- حق عضویت دریافتی صرف حمایت از نشریات عضو و نگهداری، تکمیل و توسعه مگیران میشود.
- پرداخت حق اشتراک و دانلود مقالات اجازه بازنشر آن در سایر رسانههای چاپی و دیجیتال را به کاربر نمیدهد.
In order to view content subscription is required
Personal subscription
Subscribe magiran.com for 70 € euros via PayPal and download 70 articles during a year.
Organization subscription
Please contact us to subscribe your university or library for unlimited access!