Mutual stacked autoencoder for unsupervised fault detection under complex multi-residual correlations

IF 8 1区 工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
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引用次数: 0

Abstract

Due to the increasing complexity of variable relationships, fault detection has garnered significant attention, as it is crucial for ensuring industrial safety and engineering reliability. Traditional detection methods can be classified as twofold: global-based and local-based strategies, which respectively focus on mining macro- and micro-level information. However, our theoretical derivation and experiment results reveal that some spurious assumptions, such as local groups and their provided information are mutually independent are implicitly adhered to but are hardly satisfied in unsupervised fault detection under real industrial scenarios. Hence, this study introduces a novel mutual stacked autoencoder (M-SAE) which can be divided into three sub-networks: L-Net, R-Net, and M-Net. L-Net enriches local information learning through multiple local backbones by incorporating the unsupervised clustering algorithm. R-Net, employing a multi-scale attention mechanism, leverages complete local information for residual strength calculation and utilizes local features to capture residual information within the latent feature space. M-Net fuses the multi-scale local feature information to perform a reconstruction for each local. A multitask entropy-aided loss function is introduced to enrich local details, the global structure, and the residual associations. Finally, results on eleven datasets validate the high-performance of the proposed M-SAE and the ablation experiments demonstrate the efficacy of each component in M-SAE, confirming that this research effectively and accurately addresses multivariable industrial fault detection tasks, thereby enabling timely interventions that are crucial for maintaining operational safety in real-world scenarios.
用于复杂多残差相关性下无监督故障检测的互叠自动编码器
由于变量关系日益复杂,故障检测已引起人们的极大关注,因为它对确保工业安全和工程可靠性至关重要。传统的检测方法可分为两类:基于全局的策略和基于局部的策略,它们分别侧重于挖掘宏观和微观层面的信息。然而,我们的理论推导和实验结果表明,在实际工业场景下的无监督故障检测中,一些虚假的假设,如局部群组及其提供的信息相互独立的假设被隐含地遵守,但却难以满足。因此,本研究引入了一种新型互叠自动编码器(M-SAE),它可分为三个子网络:它可分为三个子网络:L-网络、R-网络和 M-网络。L-Net 结合了无监督聚类算法,通过多个本地骨干网丰富了本地信息的学习。R-Net 采用多尺度关注机制,利用完整的本地信息计算残差强度,并利用本地特征捕捉潜在特征空间中的残差信息。M-Net 融合多尺度局部特征信息,对每个局部进行重构。M-Net 引入了多任务熵辅助损失函数,以丰富局部细节、全局结构和残差关联。最后,11 个数据集的结果验证了所提出的 M-SAE 的高性能,而烧蚀实验则证明了 M-SAE 中每个组件的功效,证实这项研究能有效、准确地解决多变量工业故障检测任务,从而实现及时干预,这对维护现实世界场景中的运行安全至关重要。
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来源期刊
Advanced Engineering Informatics
Advanced Engineering Informatics 工程技术-工程:综合
CiteScore
12.40
自引率
18.20%
发文量
292
审稿时长
45 days
期刊介绍: Advanced Engineering Informatics is an international Journal that solicits research papers with an emphasis on 'knowledge' and 'engineering applications'. The Journal seeks original papers that report progress in applying methods of engineering informatics. These papers should have engineering relevance and help provide a scientific base for more reliable, spontaneous, and creative engineering decision-making. Additionally, papers should demonstrate the science of supporting knowledge-intensive engineering tasks and validate the generality, power, and scalability of new methods through rigorous evaluation, preferably both qualitatively and quantitatively. Abstracting and indexing for Advanced Engineering Informatics include Science Citation Index Expanded, Scopus and INSPEC.
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