Constraint Guided AutoEncoders for Joint Optimization of Condition Indicator Estimation and Anomaly Detection in Machine Condition Monitoring

Maarten Meire, Quinten Van Baelen, Ted Ooijevaar, Peter Karsmakers
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引用次数: 0

Abstract

The main goal of machine condition monitoring is, as the name implies, to monitor the condition of industrial applications. The objective of this monitoring can be mainly split into two problems. A diagnostic problem, where normal data should be distinguished from anomalous data, otherwise called Anomaly Detection (AD), or a prognostic problem, where the aim is to predict the evolution of a Condition Indicator (CI) that reflects the condition of an asset throughout its life time. When considering machine condition monitoring, it is expected that this CI shows a monotonic behavior, as the condition of a machine gradually degrades over time. This work proposes an extension to Constraint Guided AutoEncoders (CGAE), which is a robust AD method, that enables building a single model that can be used for both AD and CI estimation. For the purpose of improved CI estimation the extension incorporates a constraint that enforces the model to have monotonically increasing CI predictions over time. Experimental results indicate that the proposed algorithm performs similar, or slightly better, than CGAE, with regards to AD, while improving the monotonic behavior of the CI.
用于联合优化机器状态监测中的状态指标估计和异常检测的约束引导自动编码器
顾名思义,机器状态监测的主要目的是监测工业应用的状态。这种监控的目标主要可分为两个问题。一个是诊断问题,需要将正常数据与异常数据区分开来,也称为异常检测 (AD);另一个是预测问题,目的是预测状态指标 (CI) 的变化,该指标反映了资产在整个生命周期内的状态。在考虑机器状态监控时,随着时间的推移,机器的状态会逐渐恶化,因此预计该 CI 会表现出单调的行为。为了改进 CI 估算,该扩展包含了一个约束条件,强制模型具有随时间单调递增的 CI 预测。实验结果表明,所提出的算法在 AD 方面的表现与 CGAE 相似或略胜一筹,同时改进了 CI 的单调行为。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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