Prognostics and health management technology (PHM), a measure to ensure the reliability and safety of the operation of industrial machinery, has attracted attention and application adequately. However, how to use the monitored information to evaluate the degradation of rolling bearings is a significant issue for its predictive maintenance and autonomic logistics. This work presents a reliable health prognosis approach to estimate the health indicator (HI) and remaining useful life (RUL) of rolling bearings. Firstly, to accurately capture the degradation process, a novel health index (HI) is constructed based on correlation kurtosis for different iteration periods and a Gaussian process latency variable model (GPLVM). Then, a multiple convolutional long short-term memory (MCLSTM) network is proposed to predict HI values and RUL values. Finally, we perform experimental datasets of rolling bearings, demonstrating that the presented method surpasses other state-of-the-art prognosis approaches. The results also confirm the feasibility of the presented method in industrial machinery.

中文翻译：

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一种使用MCLSTM评估滚动轴承性能的可靠预测方法。

预防和健康管理技术（PHM）是确保工业机械运行的可靠性和安全性的一种措施，已经引起了足够的关注和应用。但是，如何使用监视的信息评估滚动轴承的性能下降是其预测性维护和自主物流的重要问题。这项工作提出了一种可靠的健康预测方法，可以估算滚动轴承的健康指标（HI）和剩余使用寿命（RUL）。首先，为了准确地捕获退化过程，基于不同迭代周期的相关峰度和高斯过程等待时间变量模型（GPLVM），构造了一个新的健康指数（HI）。然后，提出了一种多卷积长短期记忆（MCLSTM）网络来预测HI值和RUL值。最后，我们执行滚动轴承的实验数据集，证明所提出的方法超越了其他最新的预测方法。结果也证实了该方法在工业机械中的可行性。