Navigation  without Java Scripts

A PROLOG IMPLEMENTATION OF AN AUTOMATED NEURAL-NETWORK FOR DIAGNOSIS OF ROTATING MACHINERY

by Carsten Andersson and Claus Witfelt

ABSTRACT

The condition-based (vibration and process) diagnosis system presented in this paper offers a new approach to expert system design and implementation.

The diagnosis system was implemented entirely by using Prolog which has bindings to an XVT1 library. The very strong features of the Prolog language - dynamic data structures, recursiveness and the ability to compare and match elements of complex data structures has made the implementation of this program possible. The application described, is within the field of machine vibration analysis. However, the principles of the program may also be used for diagnosis systems solving other types of problems.

The paper examines the steps in setting up the system, the daily operation, the workings of the neural network implementation and for each of these steps examples of how Prolog facilitated the implementation. The paper also looks at neural networks and discusses how this important technology is used in the system to address the problems associated with traditional artificial intelligence systems.

Most simple expert systems today are limited by the number of rules in their databases. As the person using the system gains enough experience to make better judgements, the expert system is used less and less. This paper therefore discusses how a user-defined knowledge-base can be used to augment the diagnosis system with the user's own knowledge and experience, continuously improving the breadth and accuracy of the diagnoses.