An effective zero-shot learning approach for intelligent fault detection using 1D CNN
An effective zero-shot learning approach for intelligent fault detection using 1D CNN
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DOI：
10.1007/s10489-022-04342-1
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发表时间：
2022-12
影响因子：
5.3
通讯作者：
Siyu Zhang;Hua‐Liang Wei;Jinliang Ding
中科院分区：
文献类型：
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作者：
Siyu Zhang;Hua‐Liang Wei;Jinliang Ding
近年来，数据驱动的故障检测技术在工程、工业等领域引起了广泛的关注。在许多实际应用中，经常会出现以下情况：某些类型的故障(看不见的故障)的数据无法用于训练用于故障检测的模型。当数据收集变得非常耗时或具有破坏性时，可能会发生这种情况。针对这一具有挑战性的问题，本文提出了一种基于零镜头学习的故障检测方法，该方法包括特征提取、标签嵌入和特征嵌入三个阶段。该方法首先应用一维卷积神经网络(1DCNN)从原始信号中提取特征，然后建立语义描述(人类定义的)作为已知故障和不可见故障之间共享的故障属性，最后使用双线性相容函数找到排序最高的故障类型。提出的基于语义空间的一维CNN零镜头学习称为SSB-ZSL-1DCNN。利用余弦距离度量特征嵌入与故障属性之间的相似度。SSB-ZSL-1DCNN的一个重要特征是，该模型只使用已见故障的样本进行训练，可以用于检测未见故障。为了评估所提出的方法，设计了两个基于两个著名基准的案例研究(分别是田纳西-伊士曼化学控制过程和凯斯西储大学的滚动轴承实验)。结果表明，该方法在检测不可见故障方面表现出了显著的性能。
Data-driven fault detection techniques have attracted extensive attention in engineering, industry and many other areas in recent years. In many real applications, the following situation often occurs: data for certain types of faults (unseen faults) are not available to train models that are used for fault detection. Such a scenario can occur when data collection becomes highly time-consuming or destructive. To address this challenging problem, a novel fault detection method using zero-shot learning (ZSL) is proposed in this paper, which contains three phases: feature extraction, label embedding, and feature embedding. The method first extracts features from raw signals by applying a one-dimensional convolutional neural network (1D CNN), then builds semantic descriptions (human-defined) as fault attributes shared between seen faults and unseen faults, and finally uses a bi-linear compatibility function to find the highest-ranking fault type. The proposed semantic space based zero-shot learning with 1D CNN is called SSB-ZSL-1DCNN. The cosine distance is used to measure the similarity between feature embeddings and fault attributes. An important characteristic of SSB-ZSL-1DCNN is that the model, trained using only samples of seen faults, can be used to detect unseen defects. To evaluate the proposed method, two case studies are designed based on two well-known benchmarks (the Tennessee-Eastman chemical control process and the rolling bearing experiments at the Case Western Reserve University, respectively). The results demonstrate that the proposed method shows remarkable performance in detecting unseen faults.