Detecting rotating machinery faults under different working conditions with cross-domain negative correlated ensemble algorithm

Abstract When detecting faults of rotating machinery, variation in working conditions leads to the distribution mismatch between training and test data. Thus, the diagnosis accuracy of existing deep learning models deteriorates greatly. To address that, we propose a novel ensemble algorithm. The ensemble algorithm employs deep convolutional extreme learning machine (DCELM) as base learners to learn representative features from time-frequency images. Then, a selection scheme is designed to select diverse base learners which are negatively correlated. Meanwhile, by calculating the correlation term using both the source and the target domain data, the attained ensemble diversity is also valid in target domains. The proposed ensemble algorithm is applied to detect faults of rotating machinery components in three cases. Results show that it attains the best performance on all cross-condition tasks. On average, its accuracy is 1.9% higher than other state-of-the-art ensemble methods and achieves 3.6% enhancement compared with its base learner.