On-line fault detection using integrated neural networks

A practical neural networks paradigm is described for material handling. The learning algorithm is a modification of the cerebella model articulation controller (CMAC) developed by Albus. A table look-up approach detects faults by monitoring the output patterns from sensors and actuators. By analyzing the timing sequence, abnormal conditions can be detected. CMAC offers an alternative to conventional back-propagated, multilayered networks, with the advantage of rapid convergence. The approach appears to be more efficient for the on-line and real-time applications required in automated systems.