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A multivariate statistical approach for anomaly detection and condition based maintenance in complex systems

机译：基于异常检测和复杂系统维护的多变量统计方法

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This paper will describe recent developments in the practical application of multivariate Statistical Process Control (SPC) techniques to improve and automate anomaly detection in complex systems as part of a comprehensive Condition Based Maintenance (CBM) system. The CBM implementation process will be broken down into phases from planning to operation. The operational CBM system includes data acquisition, preprocessing, detection, and diagnosis. The multivariate statistical methods for establishing baseline operation and analyzing operational data are covered. A comprehensive discussion of the technical need, complexities of implementation, and some best practices based upon novel applications of the technology are included. This technology is applicable to complex mechanical systems exhibiting degradation or wear prior to system failure and where failure leads to significant financial losses or loss of mission capability. Examples of the implementation of the methodology will be drawn from jet engine trending, facility infrastructure, utility-scale wind turbines, and projects where the techniques have been successfully employed in real world applications. CBM has been shown to be an effective approach to reducing the Life Cycle Cost of a system and improving system availability. Univariate SPC techniques are widely used in industry but are sometimes insufficient for monitoring complex systems which function over a wide range of operating conditions and environments. Multivariate SPC techniques provide high sensitivity and low false alarm rate monitoring opportunities. Simplifying the system health assessment of a complex system into a single control chart reduces the burden of interpreting complex interactions and reduces the training and education level of the maintenance personnel. Multivariate SPC techniques can also be implemented to improve anomaly detection in environments where it is impossible or impractical for cost or performance reasons to employ additional sensors for fault detection. Significant cost savings and cost avoidance can be realized by utilizing the full life of equipment but also having early detection of impending problems when there is still time for failure mitigation or scheduling of preventative maintenance. The statistical techniques are also useful for forensic analysis of failed systems where multivariate SPC have not been implemented. The authors introduced this technique at the 2016 International Symposium on Systems Engineering in Edinburgh, Scotland [1]. This paper will expand and build upon the previous work by adding the results of recent applications and elaborating on the approach as it pertains to the current state-of-the-art based on these results.
机译：本文将描述多元统计过程控制（SPC）技术在复杂系统中改善和自动化异常检测的实际应用中的最新发展，作为基于综合条件的维护（CBM）系统的一部分。 CBM实现过程将分解为从计划运行的阶段。操作CBM系统包括数据采集，预处理，检测和诊断。涵盖了用于建立基线操作和分析操作数据的多变量统计方法。包括对技术需求，实施复杂性以及基于技术的新应用的一些最佳实践的全面讨论。该技术适用于在系统故障之前表现出降解或磨损的复杂机械系统，并且故障导致严重的财务损失或使命能力丧失。方法的实施例将从Jet Engine趋势，设施基础设施，公用事业级风力涡轮机以及在现实世界应用中成功使用的项目中汲取的。 CBM已被证明是降低系统生命周期成本和提高系统可用性的有效方法。单变量SPC技术广泛用于工业，但有时不足以监控复杂的系统，这些系统在广泛的操作条件和环境中起作用。多变量SPC技术提供高灵敏度和低误报率监测机会。简化复杂系统的系统健康评估到单个控制图中，减少了解释复杂互动的负担，并降低了维护人员的培训和教育水平。也可以实现多元SPC技术，以改善在环境中不可能或不切实际的环境中的异常检测，因为成本或性能原因采用额外的传感器进行故障检测。通过利用设备的全部寿命，还可以实现显着的成本节约和成本避免，而且在仍然有可能的失败减缓或预防性维护调度时，还可以早期发现即将发生的问题。统计技术也可用于对尚未实施多元SPC的失败系统的法医分析。作者在苏格兰爱丁堡的2016年系统工程研讨会上介绍了这项技术[1]。本文将通过添加最近应用程序的结果并阐述基于这些结果的方法来扩展和构建以通过添加到当前最先进的方法的方法来构建。

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