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

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 task 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.