Multifeatures Fusion and Nonlinear Dimension Reduction for Intelligent Bearing Condition Monitoring

Abstract EN

Condition-based maintenance is critical to reduce the costs of maintenance and improve the production efficiency.

Data-driven method based on neural network (NN) is one of the most used models for mechanical components condition recognition.

In this paper, we introduce a new bearing condition recognition method based on multifeatures extraction and deep neural network (DNN).

First, the method calculates time domain, frequency domain, and time-frequency domain features to represent characteristic of vibration signals.

Then the nonlinear dimension reduction algorithm based on deep learning is proposed to reduce the redundancy information.

Finally, the top-layer classifier of deep neural network outputs the bearing condition.

The proposed method is validated using experiment test-bed bearing vibration data.

Meanwhile some comparative studies are performed; the results show the advantage of the proposed method in adaptive features selection and superior accuracy in bearing condition recognition.