Sarah B. Watstein, Pascal V. Calarco and James S. Ghaphery
This article identifies and defines some of the more commonly used terms and phrases which have been used to describe the evolving online/electronic/virtual/digital library…
Abstract
This article identifies and defines some of the more commonly used terms and phrases which have been used to describe the evolving online/electronic/virtual/digital library phenomenon. Includes examination of findings from the authors’ examination of selected keywords increasingly found in the literature of both library and information science as well as in the literature of computer science, within the context of a (1970‐1997) bibliographic search for these terms in library literature, INSPEC, and EI COMPENDEX.
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Lynne U. Turman, Phyllis C. Self and Pascal V. Calarco
Librarians at Virginia Commonwealth University teach a course in Health Informatics as part of a distance learning Doctoral program for allied health professionals. This paper…
Abstract
Librarians at Virginia Commonwealth University teach a course in Health Informatics as part of a distance learning Doctoral program for allied health professionals. This paper discusses the experience of developing and delivering a Web‐based course for the curriculum. Lessons learned fall into the categories of communication, technology, and resources.
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Fanshu Zhao, Jin Cui, Mei Yuan and Juanru Zhao
The purpose of this paper is to present a weakly supervised learning method to perform health evaluation and predict the remaining useful life (RUL) of rolling bearings.
Abstract
Purpose
The purpose of this paper is to present a weakly supervised learning method to perform health evaluation and predict the remaining useful life (RUL) of rolling bearings.
Design/methodology/approach
Based on the principle that bearing health degrades with the increase of service time, a weak label qualitative pairing comparison dataset for bearing health is extracted from the original time series monitoring data of bearing. A bearing health indicator (HI) quantitative evaluation model is obtained by training the delicately designed neural network structure with bearing qualitative comparison data between different health statuses. The remaining useful life is then predicted using the bearing health evaluation model and the degradation tolerance threshold. To validate the feasibility, efficiency and superiority of the proposed method, comparison experiments are designed and carried out on a widely used bearing dataset.
Findings
The method achieves the transformation of bearing health from qualitative comparison to quantitative evaluation via a learning algorithm, which is promising in industrial equipment health evaluation and prediction.
Originality/value
The method achieves the transformation of bearing health from qualitative comparison to quantitative evaluation via a learning algorithm, which is promising in industrial equipment health evaluation and prediction.