Acoustic emission data assisted process monitoring.
Acoustic emission data assisted process monitoring.
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DOI：
10.1016/s0019-0578(07)60087-1
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发表时间：
2002-07
期刊：
影响因子：
7.3
通讯作者：
G. Yen;Haiming Lu
中科院分区：
文献类型：
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作者：
G. Yen;Haiming Lu
气液两相流广泛应用于化学工业。流量参数（例如流态）的准确测量是运行效率的关键。由于两相流界面的复杂性，在线实时监测和区分流型非常困难。在本文中，我们提出了一种具有成本效益和计算效率的声发射（AE）检测系统，与人工神经网络技术相结合，可以识别空气-水垂直两相流柱中的四种主要模式。对几个关键的声发射参数进行了探索和验证，我们发现声发射事件的密度和振铃计数是流动模式识别问题的两个极好的指标。代替传统的公平映射，开发了命中计数映射，并设计了多层感知器神经网络作为决策者来描述给定两相流系统的近似传输阶段。
Gas-liquid two-phase flows are widely used in the chemical industry. Accurate measurements of flow parameters, such as flow regimes, are the key of operating efficiency. Due to the interface complexity of a two-phase flow, it is very difficult to monitor and distinguish flow regimes on-line and real time. In this paper we propose a cost-effective and computation-efficient acoustic emission (AE) detection system combined with artificial neural network technology to recognize four major patterns in an air-water vertical two-phase flow column. Several crucial AE parameters are explored and validated, and we found that the density of acoustic emission events and ring-down counts are two excellent indicators for the flow pattern recognition problems. Instead of the traditional Fair map, a hit-count map is developed and a multilayer Perceptron neural network is designed as a decision maker to describe an approximate transmission stage of a given two-phase flow system.