Computers in Industry ( IF 10.0 ) Pub Date : 2021-03-10 , DOI: 10.1016/j.compind.2021.103434 Fernando Hidalgo-Mompeán , Juan Francisco Gómez Fernández , Gonzalo Cerruela-García , Adolfo Crespo Márquez
The correct selection of the subset of features for the design of a CBM (Condition Based Maintenance) strategy may result in models working faster and producing more accurate predictions. This must be done avoiding a phenomenon known as the curse of dimensionality, that appears in Machine Learning when algorithms must learn from an ample feature volume with abundant values within each one.
This paper deals precisely with feature selection problem when dealing with compressors failure modes detection, using machine learning (ML) models. To that end, several feature selection ranking (FSR) methods are considered. These methods are basically algorithms which include wrappers and filters and they are able to provide a ranking about all the analysed features. A very important issue of these methods, is to realise the feature selection unconstrained of the Machine Learning algorithm to be later applied, and that will be tested in this paper. Stability and scalability of these methods will be also defined and discussed in the paper.
The paper case study evaluates the possibility of detecting and therefore diagnosing the rod drop failure mode appearance in Liquid Natural Gas (LNG) cryogenic reciprocating compressors by using artificial intelligence analysis techniques. This failure mode implies unavailability of this equipment, which are critic in the LNG industry due to the cost of flaring or, less common and desirable, venting of the boil-off gas (BOG) recovered by its compression in order to send it out or use as fuel.
More than 90.000 running hours and thirteen representative features are evaluated as well as thirteen FSR methods. Three most-used classifiers have been employed in order to assess the feature rankers’ effect over the models development to diagnose the rod drop failure.
Conclusions are about the possibility, not only to diagnose the appearance of a failure mode like rod drop, but also to do it with considering a reduced number of features.
中文翻译:
机器学习故障检测模型中的维度分析。LNG压缩机的案例研究
为CBM(基于条件的维护)策略的设计正确选择特征子集可能会导致模型工作更快并产生更准确的预测。这样做必须避免被称为维数诅咒的现象,这种现象出现在机器学习中,这是因为算法必须从每个特征中具有丰富值的充足特征量中进行学习。
本文使用机器学习(ML)模型,在处理压缩机故障模式检测时,精确地处理了特征选择问题。为此,考虑了几种特征选择分级(FSR)方法。这些方法基本上是包括包装器和过滤器的算法,它们能够提供有关所有分析特征的排名。这些方法的一个非常重要的问题是实现不受机器学习算法约束的特征选择,以供以后应用,本文将对此进行测试。这些方法的稳定性和可扩展性也将在本文中定义和讨论。
本文的案例研究评估了使用人工智能分析技术检测并诊断液化天然气(LNG)低温往复式压缩机中的活塞杆掉落故障模式的可能性。这种故障模式意味着该设备不可用,这在液化天然气行业中受到批评,原因是燃烧的成本高昂,或者由于压缩而回收的蒸发气体(BOG)排空了,以便将其排出或排放出去。用作燃料。
超过90.000个小时的运行时间和13种代表性功能以及13种FSR方法得到了评估。为了评估特征等级对模型开发过程的影响,使用了三个最常用的分类器来诊断杆落故障。
结论是关于可能性的,不仅是要诊断像杆掉落这样的故障模式的出现,而且还要考虑减少特征数量来做到这一点。