As a new machine learning method, deep learning has been used gradually in the field of fault diagnosis. Stacked denoising auto-encoders (SDAE), as one of the deep learning algorithms, could acquire more robust feature representation for effective fault classification by adding "corrupted noise" to the original data, and then reconstructing the input data with the auto-encoder network. However, for a specific diagnosis problem, the number of network hidden nodes, sparse parameters and random zero proportion of input data directly affects the diagnosis results. Based on particle swarm optimization (PSO), an improved SDAE algorithm is proposed for SDAE network hyper-parameters adaptive selection. Then, the determined SDAE networks are used to obtain the feature representations of fault conditions, which could be an input to a soft-max classifier for fault classification. Bearing fault simulation and experiments were conducted under varying running conditions to verify the effectiveness of the proposed method. Experimental results demonstrate that, considering the generalization capability and classification performance, the proposed PSO-SDAE algorithm is superior to support vector machine (SVM), artificial neural network (BP), and deep belief network (DBN).
HOU Wenqing
,
YE Ming
,
LI Weihua
. Rolling Element Bearing Fault Classification Using Improved Stacked De-noising Auto-encoders[J]. Journal of Mechanical Engineering, 2018
, 54(7)
: 87
-96
.
DOI: 10.3901/JME.2018.07.087
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