Accurately predicting the remaining useful life (RUL) of a key component, such as gear, is significant for guaranteeing the safe operation of mechanical equipment and making a proper maintenance plan. The health indicator (HI) plays an essential role in the data-driven RUL prediction technique. HI can be constructed from the perspective of the data distribution discrepancy. However, some existing methods cannot utilize different types of distributions to estimate the distribution discrepancy in various domains. In addition, the constructed HI may not comprehensively describe the tendency of performance degradation by using a type of distribution to obtain the distribution discrepancy in a domain. To overcome these challenging problems, a novel lognormal-normal mixture model (LNMM) that utilizes lognormal and normal distributions is constructed to estimate data distributions from two data domains, including the raw data domain and exponentially transformed data domain. Then, the distribution contact ratio metric (DCRM) is applied to calculate the discrepancies between benchmark distribution of healthy data and distributions of whole life-cycle data in two domains. The gear HI is generated without supervision by combining the DCRMs of two domains. The developed unsupervised HI is employed to estimate gear’s RUL via an improved multi-hierarchical long-term memory augmented network. Finally, the experimental results indicate the feasibility and merit of the developed LNMM in gear HI construction. The LNMM-based HI has a better predictive efficacy than the conventional and state-of-the-art unsupervised HIs.

中文翻译：

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用于无监督健康指标构建的对数正态-正态混合模型及其在齿轮剩余使用寿命预测中的应用


准确预测齿轮等关键部件的剩余使用寿命（RUL）对于保证机械设备的安全运行和制定适当的维护计划具有重要意义。健康指标 (HI) 在数据驱动的 RUL 预测技术中起着至关重要的作用。 HI可以从数据分布差异的角度来构建。然而，一些现有方法无法利用不同类型的分布来估计各个领域的分布差异。另外，通过使用一种分布来获取域内的分布差异，所构建的HI可能无法全面描述性能下降的趋势。为了克服这些具有挑战性的问题，构建了一种利用对数正态分布和正态分布的新型对数正态-正态混合模型（LNMM）来估计两个数据域（包括原始数据域和指数变换数据域）的数据分布。然后，应用分布接触比度量（DCRM）来计算两个域中健康数据的基准分布与全生命周期数据的分布之间的差异。齿轮HI是通过组合两个域的DCRM而在无监督的情况下生成的。开发的无监督 HI 用于通过改进的多层次长期记忆增强网络来估计齿轮的 RUL。最后，实验结果表明了所开发的LNMM在齿轮HI构造中的可行性和优点。基于 LNMM 的 HI 比传统和最先进的无监督 HI 具有更好的预测效果。