1. NORPIE 2004 Trondheim, 14 June
Automatic bearing fault classification combining statistical classification and fuzzy logic
Tuomo Lindh
Jero Ahola
Petr Spatenka
Anna-Lena Rautiainen

2. Introduction to fault classification
Neural networks, unsupervised Kohonen’s maps, supervised BP
Statistical classification, Mahalanobis distance, Support vector Machines
Hidden Markov chains,
Fuzzy logic
Adaptive, model based methods and many others

3. Why probabilistic and deterministic ?
Statistical pattern classification
search patterns of features
compare with known prototypes of faults
Deterministic calculation, rules
calculates the magnitudes
estimates the risk of fault classification
calculates the quality of features
avoid known pit falls (classification method, behaviour of machines etc.)

4. Appearance of cyclic bearing faults in motor frame vibration
Wang (1998), Lindh (2002)

5. Statistical classification
Minimum distance classifier

6. Fuzzy logic, the fault size estimation

7. Fuzzy logic, the probability of the fault
uses the results of the
statistical classification as input

8. Fault classifier

9. Results

10. Results
Classification results using 16- dimensional feature space covering all four fault types.
The bold cases are selected with the minimum distance classifier.

11. Results
Classification results using four dimensional feature space. The distance between test vector and any fault prototypes are calculated separately.
The bold cases are selected with the minimum distance classifier.

12. Results
Classification results using simple fuzzy logic for the determination of fault degree

13. Results
Classification results using fuzzy logic that estimates the fault degree as well as the probability of the faults. Action can be formed with fuzzy logic or multiplication of the fields of table of previous slide with the probabilities of this table. The actions are selected with trigger levels.

14. Conclusions
The method where the Mahalanobis distance based statistical classification results were used as one input in fuzzy logic was introduced.
This input and other qualitative features of the spectrum were taken into account in the fuzzy logic which evaluated the probability of a certain fault. The other fuzzy logic evaluated the degree of certain fault and by combining the outputs of these logics, the final suggestion of the state and required action were given.

15. What?