首页|Duzce University Researcher Describes Research in Machine Learning (An Efficient Approach for Automatic Fault Classification Based on Data Balance and One-Dimen sional Deep Learning)
Duzce University Researcher Describes Research in Machine Learning (An Efficient Approach for Automatic Fault Classification Based on Data Balance and One-Dimen sional Deep Learning)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in artific ial intelligence. According to news reporting from Duzce, Turkey, by NewsRx jour nalists, research stated, "Predictive maintenance (PdM) is implemented to effici ently manage maintenance schedules of machinery and equipment in manufacturing b y predicting potential faults with advanced technologies such as sensors, data a nalysis, and machine learning algorithms. This paper introduces a study of diffe rent methodologies for automatically classifying the failures in PdM data." The news journalists obtained a quote from the research from Duzce University: " We first present the performance evaluation of fault classification performed by shallow machine learning (SML) methods such as Decision Trees, Support Vector M achines, k-Nearest Neighbors, and one-dimensional deep learning (DL) techniques like 1D-LeNet, 1D-AlexNet, and 1D-VGG16. Then, we apply normalization, which is a scaling technique in which features are shifted and rescaled in the dataset. W e reapply classification algorithms to the normalized dataset and present the pe rformance tables in comparison with the first results we obtained. Moreover, in contrast to existing studies in the literature, we generate balanced dataset gro ups by randomly selecting normal data and all faulty data for all fault types fr om the original dataset. The dataset groups are generated with 100 different rep etitions, recording performance scores for each one and presenting the maximum s cores. All methods utilized in the study are similarly employed on these groups. "
NETL
NSTL
