Biao WangBeijing Jiaotong University | NJTU · State Key Laboratory of Rail Traffic Control and Safety
Biao Wang
Doctor of Engineering
About
23
Publications
12,984
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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.
Skills and Expertise
Education
September 2016 - June 2021
September 2014 - September 2016
September 2010 - June 2014
Publications
Publications (23)
Intelligent fault diagnosis and continual learning techniques for train transmission systems arebecoming more appealing to ensure the operation safety and reliability of trains. Howeverexisting related methods have the following limitations. 1) They build separate fault diagnosisnetworks for each key component in train transmission systems, which n...
Continual learning is gaining special attention in remaining useful life (RUL) prediction of machinery recently, which enables deep prognostics networks to use incremental samples to progressively improve network performance without laborious retraining. Nonetheless, current studies exhibit several constraints: 1) An explicit mechanism is lacking i...
The significance of intelligent fault diagnosis techniques is increasing in maintaining the security and reliability of railway operations. In particular, few-shot learning shows promise since it can address the issue of limited fault samples. However, the existing approaches have the following shortcomings. First, they ignore rich fault informatio...
Continual learning is promising in intelligent fault diagnosis of three-phase motors, which allows diagnosis networks to increase diagnosable fault classes without tedious retraining during various mechanical and electrical fault sample accumulations. Existing studies, however, have the following limitations. 1) The representation learning ability...
Intelligent fault diagnosis of train bogie bearings based on edge computing is a promising technology to ensure the safety and reliability of train operation, which can give fault diagnosis systems better real-time performance and lower communication costs. Lightweight diagnosis networks are the core of this technology. However, existing lightweigh...
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...
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...
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...
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...
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...
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
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...
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...