Research on Health Big Data Preprocessing Technology Based on AHSMM and Bacterial Foraging Algorithm

Zhen Liu, Xu Liang, Ming Huang
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Abstract

With the development of big data technology and the popularity of Internet of Things technology, the demand for management is becoming more and more serious. In order to solve the problem of health big data preprocessing technology, the method of Adaptive Hidden Semi-Markov Model (AHSMM) based on bacterial foraging algorithm is proposed. First of all, the bacterial foraging algorithm referring health big data is used to apply the equipment health diagnosis and prediction methods to the actual case of the United States Caterpillar company’s standard health prediction experiments. Next, the proposed method is applied to the actual case of hydraulic pump of Caterpillar company. The results verify the effectiveness and stability of the proposed algorithm.
基于AHSMM和细菌觅食算法的健康大数据预处理技术研究
随着大数据技术的发展和物联网技术的普及,对管理的需求越来越严峻。为了解决健康大数据预处理技术中的问题,提出了一种基于细菌觅食算法的自适应隐半马尔可夫模型(AHSMM)方法。首先,采用参照健康大数据的细菌觅食算法,将设备健康诊断预测方法应用到美国卡特彼勒公司标准健康预测实验的实际案例中。然后,将所提出的方法应用于卡特彼勒公司液压泵的实际案例。实验结果验证了该算法的有效性和稳定性。
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