Exact(36)
In this study, we propose a new intelligent diagnosis method of bearing, which can learn features automatically.
As it is well known, deep learning methods are able to learn features and perform classification automatically.
The effort involved in feature engineering is the main reason to seek algorithms that can learn features by themselves.
Through that data, we want to learn features that will be useful for other vision tasks".
The results demonstrate that the proposed method is able to learn features adaptively from frequency data and achieve higher diagnosis accuracy than other comparative methods.
The approach utilizes sparse auto-encoder (SAE) to learn features, which belongs to unsupervised feature learning that only requires unlabeled measurement data.
Similar(24)
Deep learning can automatically learn feature representation from big data, including millions of parameters.
After that, we learn feature from the labeled region and train two-level classifiers.
Gong et al. [14] proposed a kernel-based method to learn feature representations.
Experimental results demonstrate that our method can learn feature representation which is time-invariant and viewpoint-invariant from depth sequences.
"How'd You Learn" (featuring Lydia Loveless).
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