Bearing Condition Recognition and Degradation Assessment under Varying Running Conditions Using NPE and SOM

Manifold learning methods have been widely used in machine condition monitoring and fault diagnosis. However, the results reported in these studies focus on the machine faults under stable loading and rotational speeds, which cannot interpret the practical machine running. Rotating machine is always running under variable speeds and loading, which makes the vibration signal more complicated. To address such concern, the NPE (neighborhood preserving embedding) is applied for bearing fault classification. Compared with other algorithms (PCA, LPP, LDA, and ISOP), the NPE performs well in feature extraction. Since the traditional time domain signal denoising is time consuming and memory consuming, we denoise the signal features directly in feature space. Furthermore, NPE and SOM (self-organizing map) are combined to assess the bearing degradation performance. Simulation and experiment results validate the effectiveness of the proposed method.