As one of the most important components of machinery, once the bearing has a failure, serious catastrophe may happen. Hence, for avoiding the catastrophe, it is valuable to predict the remaining useful life (RUL) of bearing. Health indicators (HIs) construction plays a greatly important role in the data-driven RUL prediction. Unfortunately, most of the existing HIs construction methods need prior knowledge and few of them construct HIs from raw vibration signals. For dealing with the above issues, a novel quadratic function-based deep convolutional auto-encoder is developed in this work. The raw bearing vibration signals are first preprocessed by low-pass filtering. Then the cleaned vibration signals are input into the quadratic function-based DCAE neural networks for constructing HIs of bearings. Compared with AE, DNN, KPCA, ISOMAP, PCA and VAE, it is revealed that the proposed methodology can construct a better HI from the raw bearing vibration signal in terms of comprehensive performance. Several comparative experiments have been implemented, and the results indicate that the HI constructed by quadratic function-based DCAE neural network has stronger predictive power than the traditional data-driven HIs.

作为机械最重要的部件之一，轴承一旦出现故障，可能会发生严重的灾难。因此，为了避免灾难，预测轴承的剩余使用寿命（RUL）是很有价值的。健康指标（HI）构建在数据驱动的 RUL 预测中起着非常重要的作用。不幸的是，大多数现有的 HI 构建方法都需要先验知识，并且很少有从原始振动信号构建 HI 的方法。为了解决上述问题，本文开发了一种新型的基于二次函数的深度卷积自动编码器。原始轴承振动信号首先通过低通滤波进行预处理。然后将清洗后的振动信号输入到基于二次函数的 DCAE 神经网络中，用于构建轴承的 HI。与AE、DNN、KPCA、ISOMAP、PCA和VAE相比，结果表明，就综合性能而言，所提出的方法可以从原始轴承振动信号构建更好的 HI。已经进行了多次对比实验，结果表明基于二次函数的 DCAE 神经网络构建的 HI 比传统的数据驱动的 HI 具有更强的预测能力。