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首页> 外文期刊>Reliability Engineering & System Safety >Early detection of gradual concept drifts by text categorization and Support Vector Machine techniques: The TRIO algorithm
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Early detection of gradual concept drifts by text categorization and Support Vector Machine techniques: The TRIO algorithm

机译：通过文本分类和支持向量机技术对渐变概念漂移进行早期检测：TRIO算法

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During the normal operation of complex and nsky industrial plants such as the nuclear or the aerospace ones, the safety heavily rests upon the capability of the diagnostic systems of detecting concept drifts which might imply incipient failures. In this paper we propound the TRIO algorithm for the online detection of signal drifts: the underlying idea is that a real signal may be categorized as correct or drifting by comparison with added sets of artificial signals known to be correct or drifted. More specifically, the TRIO algorithm is based on three performers, namely (ⅰ) a training set of artificial signals, (ⅱ) the Text Categorization (TC) technique and (ⅲ) the Support Vector Machine (SVM) technique. Initially, we construct an artificial training set constituted by one "correct" set of signals, embraced by two "suspect" sets of signals, the suspect-up and the suspect-down drifting signals. These signals are transformed in points within the signal space by the TC technique; then the SVM technique is applied for isolating the regions occupied by the suspect-up and by the suspect-down points. At this point the "artificial context" has been established and the real measurements come in. By resorting to the sliding window technique, at each epoch the actually measured data segment is analogously transformed into a point within the signal space and then declared correct or suspect (drifted) according to the region where it falls. In the latter case suitable actions must be taken by the plant operators. Numerical case-studies and a comparison with literature results are presented.
机译：在诸如核或航空航天工业之类的复杂工业工厂的正常运行过程中，安全性很大程度上取决于诊断系统检测概念漂移的能力，这些概念漂移可能意味着初期的故障。在本文中，我们提出了用于信号漂移在线检测的TRIO算法：其基本思想是，通过与已知为正确或漂移的附加人工信号进行比较，可以将真实信号归为正确或漂移。更具体地说，TRIO算法基于三个执行者，即（ⅰ）人工信号训练集，（ⅱ）文本分类（TC）技术和（ⅲ）支持向量机（SVM）技术。最初，我们构建了一个人工训练集，该训练集由一个“正确”的信号集，两个“可疑”信号集（可疑向上漂移和可疑向下漂移信号）组成。这些信号通过TC技术转换为信号空间内的点。然后将SVM技术应用于隔离可疑点和可疑点所占据的区域。至此，已经建立了“人工环境”，并进行了实际测量。通过使用滑动窗口技术，在每个时期将实际测量的数据段类似地转换为信号空间内的一个点，然后声明为正确或可疑（漂移）根据其下落的区域。在后一种情况下，工厂运营商必须采取适当的措施。进行了数值案例研究并与文献结果进行了比较。

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