Zekun Xu , Xiaoyong Gao , Jun Fu , Qiang Li , Chaodong Tan
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In the first stage, the Model-Agnostic Meta-Learning framework is adopted to extract gain valuable model parameters from the available faulty data, yielding in the optimization of the initial weight and bias of the network model to obtain the reconstructed ELM. This enhancement significantly bolsters the ELM's parameters optimization capability, especially in scenarios with limited fault samples. Consequently, in the second stage, the reconstructed ELM model is deployed for effective fault diagnosis.</p></div><div><h3>Significant Findings</h3><p>The proposed method has proven successful in the real-time diagnosis of electric submersible pump, specifically addressing the challenging issue of ineffective fault diagnosis due to the limited fault samples in condition monitoring. 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引用次数: 0
摘要
背景传统的故障诊断方法通常依赖于足够数量的故障样本。然而,在工业流程中,故障样本的可用性往往有限,而且样本收集成本高昂。为了解决样本有限的问题,我们提出了一种融合了极限学习机(ELM)和元学习(Meta-learning)的新型故障诊断方法。该方法包括两个阶段:元学习优化和顶级模型分类。在第一阶段,采用模型诊断元学习框架,从可用的故障数据中提取有价值的模型参数,优化网络模型的初始权重和偏差,从而获得重建的 ELM。这一改进极大地增强了 ELM 的参数优化能力,尤其是在故障样本有限的情况下。因此,在第二阶段,重构后的 ELM 模型可用于有效的故障诊断。 重要发现该方法已被证明可成功用于电潜泵的实时诊断,特别是解决了状态监测中由于故障样本有限而导致故障诊断无效的难题。结果表明,与 ELM 相比,分类效果提高了 30%,与 PSO-ELM、FOS-ELM、LE-ELM 和 Siamese Nets 相比,分类效果提高了 8%。
A novel fault diagnosis method under limited samples based on an extreme learning machine and meta-learning
Background
Traditional fault diagnosis methods typically rely on an adequate number of fault samples. Yet, in industrial processes, the availability of faulty samples is often limited, accompanied by high sample collection cost. In this case, traditional methods prove ineffective for accurate diagnosis.
Methods
To solve this issue with limited samples, a novel fault diagnosis method, incorporating Extreme Learning Machine (ELM) and Meta-learning, is proposed. This method comprises two stages: Meta-learning optimization and top-model classification. In the first stage, the Model-Agnostic Meta-Learning framework is adopted to extract gain valuable model parameters from the available faulty data, yielding in the optimization of the initial weight and bias of the network model to obtain the reconstructed ELM. This enhancement significantly bolsters the ELM's parameters optimization capability, especially in scenarios with limited fault samples. Consequently, in the second stage, the reconstructed ELM model is deployed for effective fault diagnosis.
Significant Findings
The proposed method has proven successful in the real-time diagnosis of electric submersible pump, specifically addressing the challenging issue of ineffective fault diagnosis due to the limited fault samples in condition monitoring. The results showcase a 30 % classification improvement compared to ELM and an 8 % enhancement over PSO-ELM, FOS-ELM, LE-ELM, and Siamese Nets.
期刊介绍:
Journal of the Taiwan Institute of Chemical Engineers (formerly known as Journal of the Chinese Institute of Chemical Engineers) publishes original works, from fundamental principles to practical applications, in the broad field of chemical engineering with special focus on three aspects: Chemical and Biomolecular Science and Technology, Energy and Environmental Science and Technology, and Materials Science and Technology. Authors should choose for their manuscript an appropriate aspect section and a few related classifications when submitting to the journal online.