中文摘要：

针对球轴承的剩余寿命预测问题，基于自组织映射（Self organizing map，SOM）和反向传播（Back propagation，BP）两种神经网络，提出一套新的预测球轴承剩余寿命的方法体系。深入对比分析几种不同轴承衰退指标的优缺点，利用三套时间域衰退指标和三套频率域衰退指标，包括一套新设计的指标，训练自组织映射神经网络。将源自于SOM的最小量化误差（Minimum quantization error,MQE）作为新的衰退指标，建立一套轴承性能数据库。针对球轴承衰退期，训练一套BP神经网络，根据权值计算失效时间技术，成功开发一套剩余寿命预测模型。结果表明，该方案远优于业界常用的L10寿命估计。

英文摘要：

A new scheme for prediction of ball beating＇s remaining useful life is dealt with based on self-organizing map and back propagation neural networks. One of the key issues in bearing life prediction is to set up an appropriate degradation indicator from its incipient defect stage to final failure. Different from degradation features ever used, it uses the minimum quantization error （MQE） indicator deriving from SOM, which is trained by six vibrations features including a new designed degradation index for performance degradation assessment. Then using this indicator, back propagation neural networks focusing on the degradation periods are trained. Based on weight application to failure times （WAFT） technology, a remaining useful life prediction model of ball bearing is developed successfully. The validation results show that the proposed methods are greatly superior to the currently used L10 bearing life prediction.