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首页> 外文期刊>Mechanical systems and signal processing >Dempster-shafer Regression For Multi-step-ahead Time-series Prediction Towards Data-driven Machinery Prognosis
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Dempster-shafer Regression For Multi-step-ahead Time-series Prediction Towards Data-driven Machinery Prognosis

机译：基于多普勒shafer回归的多步提前时间序列预测，以数据驱动的机械预测

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Predicting a sequence of future values of a time series using the descriptors observed in the past can be regarded as the stand-stone of data-driven machinery prognosis. The purpose of this paper is to develop a novel data-driven machinery prognosis strategy for industry application. First, the collected time-series degradation features are reconstructed based on the theorem of Takens, among which the reconstruction parameters, delay time and embedding dimension are selected by the C-C method and the false nearest neighbor method, respectively. Next, the Dempster-Shafer regression technique is developed to perform the task of time-series prediction. Moreover, the strategy of iterated multi-step-ahead prediction is discussed to keep track with the rapid variation of time-series signals during the data monitoring process in an industrial plant. The proposed scheme is validated using condition monitoring data of a methane compressor to predict the degradation trend. Experimental results show that the proposed methods have a low error rate; hence, it can be regarded as an effective tool for data-driven machinery prognosis applications.
机译：使用过去观察到的描述符来预测时间序列的未来值序列，可以视为数据驱动的机械预后的基础。本文的目的是为工业应用开发一种新颖的数据驱动的机械预后策略。首先，根据Takes定理对收集的时间序列退化特征进行重构，其中重构参数，延迟时间和嵌入维数分别通过C-C方法和伪最近邻方法进行选择。接下来，开发了Dempster-Shafer回归技术来执行时间序列预测的任务。此外，讨论了迭代多步提前预测的策略，以跟踪工厂设备中数据监视过程中时序信号的快速变化。利用甲烷压缩机的状态监测数据对所提出的方案进行了验证，以预测其退化趋势。实验结果表明，该方法具有较低的误码率。因此，它可以被视为用于数据驱动的机械预测应用程序的有效工具。

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