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The unlabeled data set (unknown fault class) has unknown classification acoustic data and the labeled data set (known fault class) is from the known classification data.
Typically, such methods first hypothesize a fault class and then generate tests.
Final results give the fault classes set and suitable remedies or a reliability rate of the possible identified fault class.
Thus, a difference with the proposed method is that only one fault class is associated to a given cell.
Then, more the output which is a distance is close to c and more this output will be far to be identified as fault class.
Contrarily, more the output is close to 0 and more this output will be close to be the identified fault class.
Similar(51)
Current fault classification generally depends on feature pattern difference of different fault classes.
However, there is almost no research that justifies fault classes proposed previously.
We allow for unobservable transitions that cannot be labeled as well as for multiple fault classes.
The majority voting accuracy of their technique was less than 93% for all fault classes.
Open image in new window Fig. 1 Global diagnosis synopsis of the same fault classes set.
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