Knowledge-based techniques for fault detection in digital microwave radio communication equipment

The application of two distinct approaches to the diagnosis of faults in digital communication equipment is examined. The selected artificial-intelligence approaches use either rule-based or machine-learning techniques. Faults up to 8-dB TWT (traveling-wave tube) overdrive, 10% spacing error in the signal constellation, and 5° nonorthogonality in the modulating carriers are introduced on an 11-GHz radio. Each approach shown is capable of diagnosing both the type and magnitude of the introduced faults, subject to certain constraints for each system. Results indicate that the machine learning system is more appropriate than the rule-based system for providing optimal adjustment of the radio, where the underlying mechanisms are too complex to allow simple rules of thumb to be applied. However, the rule-based technique has been shown to be suitable for areas with large nonlinearities. A combination of the two techniques would solve some of the problems caused by the suitability of each method to a specific problem type, the rule-based approach covering the areas with large nonlinearities and the machine learning system the more linear regions. This would help in simplifying the implementation as it would minimize the number of rules required for each different radio type