Biao Wang

Biao Wang
Beijing Jiaotong University | NJTU · State Key Laboratory of Rail Traffic Control and Safety

Doctor of Engineering

About

13
Publications
8,779
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603
Citations
Introduction
Hi, my name is Biao Wang (王彪). Now, I am a lecturer at the State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, P.R. China.
Education
September 2016 - June 2021
Xi'an Jiaotong University
Field of study
  • Mechanical Engineering
September 2014 - September 2016
Xi'an Jiaotong University
Field of study
  • Aeronautical and Astronautical Science and Technology
September 2010 - June 2014
Xi'an Jiaotong University
Field of study
  • Aeronautical and Astronautical Science and Technology

Publications

Publications (13)
Article
Remaining useful life (RUL) prediction is critical for ensuring the safe and efficient operation of machinery. Due to the existence of multiple influencing factors, the degradation of machinery is often described as dependent competing failure processes (DCFPs). Extensive studies have been conducted on the degradation modeling and RUL prediction fo...
Article
Semi-observable systems are referred to as a kind of widely used industrial equipment whose physical degradation state is only observable via shutdown inspection. To monitor the degradation process of semi-observable systems online, different types of sensors are generally employed to collect monitoring signals. Lots of studies have been conducted...
Article
To integrate the complete degradation information of machinery, deep learning-based prognostics approaches usually use monitoring data acquired by different sensors as the inputs of networks. These approaches, however, lack an explicit learning mechanism to effectively identify the distinctions of different sensor data and highlight the important d...
Article
Joint maintenance and spare parts inventory optimization has attracted increasing attention in recent years because of its capability in addressing the maintenance planning and the spare parts provisioning of industrial systems simultaneously. However, imperfect maintenance (IM) actions are either neglected or over-simplified as constant improvemen...
Conference Paper
Deep learning is gaining growing interests in the field of remaining useful life (RUL) prediction and has achieved state-of-the-art results. Current deep learning-based prognostics approaches, however, do not consider the distinctions of different sensor data during representation learning, which affects their prediction accuracy and limits their g...
Article
Full-text available
Prognostics and health management (PHM) is crucial for ensuring the safe operation of machinery, improving the productivity and increasing economic benefits. High-quality life-cycle data, as the basic resource in the field of PHM, are able to carry the key information which reflects the complete degradation processes of machinery. However, due to t...
Data
XJTU-SY bearing datasets can be downloaded by the following link: http://biaowang.tech/xjtu-sy-bearing-datasets. If you have any questions or suggestions, do not hesitate to contact: wangbiaoxjtu@outlook.com
Article
Remaining useful life (RUL) prediction of rolling element bearings plays a pivotal role in reducing costly unplanned maintenance and increasing the reliability, availability, and safety of machines. This paper proposes a hybrid prognostics approach for RUL prediction of rolling element bearings. First, degradation data of bearings are sparsely repr...
Conference Paper
Full-text available
The remaining useful life (RUL) prediction of bearings has emerged as a critical technique for providing failure warnings in advance, reducing costly unscheduled maintenance and enhancing the reliability of bearings. Recently, a fusion prognostics method combining exponential model and relevance vector machine (RVM) has been proposed and applied to...

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