A Temporal Neuro-Fuzzy Monitoring System to Manufacturing Systems
  Fault diagnosis and failure prognosis are essential techniques in improving
the safety of many manufacturing systems. Therefore, on-line fault detection
and isolation is one of the most important tasks in safety-critical and
intelligent control systems. Computational intelligence techniques are being
investigated as extension of the traditional fault diagnosis methods. This
paper discusses the Temporal Neuro-Fuzzy Systems (TNFS) fault diagnosis within
an application study of a manufacturing system. The key issues of finding a
suitable structure for detecting and isolating ten realistic actuator faults
are described. Within this framework, data-processing interactive software of
simulation baptized NEFDIAG (NEuro Fuzzy DIAGnosis) version 1.0 is developed.
  This software devoted primarily to creation, training and test of a
classification Neuro-Fuzzy system of industrial process failures. NEFDIAG can
be represented like a special type of fuzzy perceptron, with three layers used
to classify patterns and failures. The system selected is the workshop of
SCIMAT clinker, cement factory in Algeria.
