A Fault Diagnosis Method Based on Active Example Selection
Abstract
The fault diagnosis in the real world is often complicated. It is due to the fact that not all relevant fault information is available directly. In many fault diagnosis situations, it is impossible or inconvenient to find all fault information before establishing a fault diagnosis model. To deal with this issue, a method named active example selection (AES) is proposed for the fault diagnosis. AES could actively discover unseen faults and choose useful samples to improve the fault detection accuracy. AES consists of three key components: (1) a fusion model of combining the advantage of the unsupervised and supervised fault diagnosis methods, where the unsupervised fault diagnosis methods could discover unseen faults and the supervised fault diagnosis methods could provide better fault detection accuracy on seen faults, (2) an active learning algorithm to help the supervised fault diagnosis methods actively discover unseen faults and choose useful samples to improve the fault detection accuracy, and (3) an incremental learning scheme to speed up the iterative training procedure for AES. The proposed method was evaluated on the benchmark Tennessee Eastman Process data. The proposed method performed better on both unseen and seen faults than the stand-alone unsupervised, supervised fault diagnosis methods, their joint and referenced support vector machines based on active learning.
This paper was recommended by Regional Editor Tongquan Wei.
