FAULT CLASSIFICATION OF CROWN WHEEL AND PINION BY AN INTELLIGENT COMBINED METHOD BASED ON DATA MINING AND FUZZY INFERENCE SYSTEM

Vibration technique in a machine condition-monitoring program provides useful reliable information, bringing significant cost benefits to industry. The main purpose of this research is to explore the intelligent way to classify four common faults versus healthy state of differential. Vibration signal by FFT technique went to frequency domain. Then the features are extracted by using statistical feature parameters that reduced the data. The J48 algorithm as a decision tree generated fuzzy rules. The structure of the FIS classifier was then defined based on the crisp sets. Results showed that the total classification accuracy were about 90%. This work demonstrates that the combined J48-FIS model has the possible capacity for fault diagnosis of differential.