基于核极限学习机自编码器的转盘轴承寿命状态识别

A life condition recognition method based on multi-layer kernel extreme learning machine based autoencoder optimized by moth-flame optimization (MFO-MLKELM-AE) was proposed to solve the problem of low-speed heavy-load slewing bearing, such as poor working condition and weak fault feature. Firstly, multiple feature vectors were extracted from time domain and time-frequency domain of vibration signal to make a high dimension feature set which can characterize the operation condition of slewing bearing. Secondly, multi-layer kernel extreme learning machine based auto-encoder (MLKELM-AE) was utilized to extract the vectors which best reflect the slewing bearing life condition information from the high dimension feature set. Thirdly, the vectors were inputted into the kernel extreme learning machine (KELM) for the life condition recognition. Finally, a new moth-flame optimization (MFO) was proposed to optimize the penalty coefficient and kernel parameter for the improvement of MLKELM-AE feature recognition ability in the training process. The accelerated life test of slewing bearing shows that the recognition accuracy of MLKELM-AE is better than multi-layer extreme learning machine based autoencoder (MLELM-AE), single layer extreme learning machine (ELM) and KELM. The multi-sensor and multi-domain features can reflect the life condition of slewing bearing more comprehensively.