Neural-networks-aided fault diagnosis in supervisory control of advanced manufacturing systems

Nong Ye, Baijun Zhao, Gavriel Salvendy

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


Fault diagnosis presents considerable difficulty to human operators in supervisory control of advanced manufacturing systems. A neural network which employs a symptomatic search strategy by pattern recognition has been developed to aid human operators in supervisory control of a simulated advanced manufacturing system. The abilities of learning-by-example, knowledge generalisation, and parallel processing of neural networks were examined, resulting in the superior performance of neural networks in comparison to expert systems and humans in terms of training, knowledge generalisation, knowledge updating, and taks performance. A more complex system of neural-networks-aided fault diagnosis, employing symptomatic search by hypothesis and test has been designed for the economy of collecting and using symptomatic information. Finally, it is suggested that an integrated supervisory control system with computer-aided fault detection and neural-networks-aided fault diagnosis will make advanced manufacturing systems more attractive in terms of increased productivity as well as improved human jobs.

Original languageEnglish (US)
Pages (from-to)200-209
Number of pages10
JournalThe International Journal of Advanced Manufacturing Technology
Issue number4
StatePublished - Jul 1 1993
Externally publishedYes


  • Advanced manufacturing systems
  • Fault diagnosis
  • Knowledge generalisation
  • Learning by example
  • Neural networks
  • Supervisory control

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Mechanical Engineering
  • Computer Science Applications
  • Industrial and Manufacturing Engineering


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