Bearing running state recognition based on non-extensive wavelet feature scale entropy and support vector machine

• Use the non-extensive wavelet feature scale entropy for feature extraction.
• Use the locality preserving projection method to reduce the features dimension.
• Though the Morlet wavelet kernel support vector machine to construct the model.
• Achieve the bearing running state recognition.

In order to effectively recognize the bearing running state, a new method based on non-extensive wavelet feature scale entropy and the Morlet wavelet kernel support vector machine (MWSVM) was proposed. Firstly, the gathered vibration signals were decomposed by the wavelet to obtain the corresponding wavelet coefficients. Then, based on the integration of non-extensive entropy and the coefficients, the features were extracted by the wavelet feature scale entropy. However, the extracted features remained high-dimensional and excessive redundant information still existed. Therefore, the manifold learning algorithm locality preserving projection (LPP) was introduced to extract the characteristic features and to reduce the dimension. The extracted characteristic features were inputted into the MWSVM to train and construct the running state identification model; the bearing running state identification was thereby realized. Cases of test and actual fault were analyzed. The results validate the effectiveness of the proposed algorithm.

The flowchart of the proposed method.Figure optionsDownload as PowerPoint slide