۱٫ Introduction

۲٫ Introduction of Decision Tree Arithmetic

۳٫ C4.5 Algorithmic

۴٫ The Improved C4.5 Algorithmic Based on Fayyad Borderline Principle

۵٫ Fault Detection and Diagnosis Experiment Scheme Based on Improved C4.5

۶٫ Conclusion

References

Aiming at the shortage of greatness of test data and the traditional decision-making arithmetic of decision tree, the paper put forward a way to diagnose faults based on vehicle test data mining. Attribute list was built by the way of training data set in tests, fault decision tree fabricated based on better-deciphered arithmetic –C4.5. To overcome the time-consuming shortage of C4.5 in the discretization process of continuous valves, the paper studied Fayyad theorem, prospered improved arithmetic by calculating via dividing line, experiments proved the fault detection and diagnose strategy effectively and exactitude.

Introduction

Tests go with the process of vehicle development, production and flight experimentation process at all times. Vehicles test data is largeness, multi-dimensionality, complex relation, relevant and specialized. It is useful to analyze data availability and compute capacity for the develop management personnel to judge the performance of aircraft. And the test data provided many kinds of hereunder in effect to run and manage vehicles. As a new knowledge acquisition technology, data mining has a wide application in fault detection and diagnosis, production optimization, repository enrichment and decision-making[1-2]. It is important for test data assistant to improve flight fault detection diagnosis and decision-making, and it is momentous to improve the quality of fault detection and diagnosis and ensure the safety in flight.