Enhanced Anomaly Detection in Compressor Components Using Deep Learning and an Attribute Updating Model

IF 3.8 3区 工程技术 Q2 ENGINEERING, CHEMICAL
Guotao Yang, Shaolin Hu, Longtao Wang
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引用次数: 0

Abstract

With increased focus on the safety of chemical plants, detecting anomalies in core equipment like compressors has become crucial for stable operations, preventive maintenance, and optimizing production efficiency. However, accurately setting anomaly thresholds for multidimensional data, pinpointing abnormal components, and fully considering the interdependence among various components remain challenging. Hence, this paper proposes an anomaly detection method integrating deep learning and an attribute updating model. It comprises an attribute update model, a dimensionality reduction and structural reorganization model, and an SL-RegNet detection model enhanced by SE and LKA. A set of detection methods for complex anomalies (collective anomalies) is developed in the end. Experimental results demonstrate an accuracy of 95.68%, effectively identifying abnormal states of compressor components. Simultaneously, we conduct validity experiments on the attribute updating model, ablation experiments, and comparison experiments to demonstrate the superiority of our proposed method.

Abstract Image

利用深度学习和属性更新模型加强压缩机部件的异常检测
随着人们对化工厂安全性的日益关注,检测压缩机等核心设备的异常已成为稳定运行、预防性维护和优化生产效率的关键。然而,准确设置多维数据的异常阈值、精确定位异常组件以及充分考虑各组件之间的相互依存关系仍具有挑战性。因此,本文提出了一种集成深度学习和属性更新模型的异常检测方法。它包括一个属性更新模型、一个降维和结构重组模型,以及一个由 SE 和 LKA 增强的 SL-RegNet 检测模型。最后还开发了一套复杂异常(集体异常)的检测方法。实验结果表明,准确率高达 95.68%,能有效识别压缩机组件的异常状态。同时,我们还进行了属性更新模型的有效性实验、烧蚀实验和对比实验,以证明我们提出的方法的优越性。
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来源期刊
Industrial & Engineering Chemistry Research
Industrial & Engineering Chemistry Research 工程技术-工程:化工
CiteScore
7.40
自引率
7.10%
发文量
1467
审稿时长
2.8 months
期刊介绍: ndustrial & Engineering Chemistry, with variations in title and format, has been published since 1909 by the American Chemical Society. Industrial & Engineering Chemistry Research is a weekly publication that reports industrial and academic research in the broad fields of applied chemistry and chemical engineering with special focus on fundamentals, processes, and products.
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