Fault Diagnosis Based on Vibration Data Using Machine Learning Models
Kitapta bölümler > Araştırma Kitabı · 2023
Yazarlar: Fatma DEMİRCAN KESKİN, Ural Gökay ÇİÇEKLİ
Machine Learning Applications in Operations Management, Araştırma Kitabı, Kriter Yayınevi, A, Uluslararası, 2023, ss 1-25, Türkiye, İstanbul
Özet
<p>This chapter addresses the fault diagnosis problem based on vibration data. Various models have been proposed for the problem based on Decision Trees (DT), Random Forest (RF), Support Vector Machines (SVM), and Multilayer Perceptron (MLP) machine learning methods. In the models, various time-based statistical features extracted from the vibration data have been used directly and after applying various dimension reduction approaches to these features. Both linear and non-linear dimension reduction techniques have been used to reduce the dimension of the data set. The linear dimension reduction technique, one of</p><p>the most widely applied techniques, Principal Component Analysis (PCA), has been applied. On the non-linear side, Kernel Principal Component Analysis (kPCA), Isometric Feature Mapping (Isomap), and Local Linear Embedding (LLE) have been implemented. Before implementing all the developed model variations to the real-life problem, the model variations were tested on the widely used benchmark bearing test data of Case Western Reserve University bearing data. The model variations with successful prediction performances on test data have been used in the data of the real-life problem, and their performances have</p><p>been evaluated.</p>