Mathematical Problems in Engineering

Data-Driven Fault Supervisory Control Theory and Applications

Publishing date
18 Jan 2013
Submission deadline
31 Aug 2012

1College of Information Science and Engineering, Northeastern University, Shenyang, China

2College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China

3Departamento de Control Automatico, Mexico City, DF, Mexico

Data-Driven Fault Supervisory Control Theory and Applications


Fault detection, diagnosis, and prognosis are the three major tasks of fault supervisory control systems (FSCs). The goal of FSCs is to detect potential abnormity, find the causes of abnormity, assess performance degradation trending, predict the remaining time before a likely failure, and determine failure effects across subsystems. FSCs are desirable in a wide range of systems, from vehicles and aerospace, marine systems, mechatronics, electric power systems, offshore wind technology, and chemical processes to biomedical and other industrial applications involving control.

Fault detection and diagnosis (FDD) has been the subject of intensive research for more than 40 years, and fruitful results have been reported in the literature and books. Fault prognosis (or prediction) (FP) is an emerging topic, whose foundation is not yet completely firmed, but whose understanding is getting deeper and deeper. Recent advances in the field of FDD and FP have introduced new technologies, among which, data-driven techniques have been attracting intensive attentions both in academic and industrial communities. A number of mature techniques have been given for extracting useful information from measured data. Data-driven FDD and FP techniques have shown their advantages for the complex and large-scale industrial processes and equipments.

In order to reflect the most recent advances in this field and increase the awareness at large on this effective technology, we invite authors to submit original research and review articles on new emerging trends in FDD, FP, as well as FSCs, and their practical applications. Potential topics include, but are not limited to:

  • Data-based fault feature extraction, fault reasoning, and decision-making with uncertain information
  • Data-based fault propagation modeling
  • Learning-based fault detection, diagnosis, and supervisory control
  • Hybrid model-based fault accommodation design
  • New architectures and structures for FSCs
  • Applications of FSCs to complex systems

All the accepted papers will be published in Journal of Mathematical Problems in Engineering, which are indexed by SCI. Therefore, papers are invited widely from both theoretical researches and industrial practices.

Before submission authors should carefully read over the journal's Author Guidelines, which are located at Prospective authors should submit an electronic copy of their complete manuscript through the journal Manuscript Tracking System at according to the following timetable:

Mathematical Problems in Engineering
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