A dynamic neural network  is  developed  to  detect  soft failures of
   sensors and actuators in automobile engines.   The network, currently
   implemented off-line in software, can process multi-dimensional input
   data in real time.  The network  is  trained  to  predict  one of the
   variables using others.  It learns  to  use  redundant information in
   the variables such as higher order statistics and temporal relations.
   The difference between the prediction and the measurement  is used to
   distinguish a normal engine from a faulty one.  Using the network, we
   are  able  to  detect  errors  in  the  manifold  air pressure sensor
   (Vs) and the exhaust gas recirculation valve (Va) with  a high degree
   of accuracy.

   (In: Neural Networks for Signal Processing VII, Principe J, Giles L,
    Morgan N, Wilson E, eds, IEEE, Piscataway, NJ. page 636-644. 1997)

   (Papers' Index of Dawei Dong)