学习更好地看见未见：用于增量零样本故障诊断的宽深混合防遗忘框架
Learning to better see the unseen: Broad-Deep Mixed Anti-Forgetting Framework for Incremental Zero-Shot Fault Diagnosis
AI总结：
针对工业场景下零样本故障诊断的持续学习需求，提出增量零样本故障诊断范式及宽深混合防遗忘框架，通过深度生成模型与宽学习系统建立双记忆机制，无需存储历史样本即可学习新故障类别与属性并防止遗忘。
摘要：
零样本故障诊断（ZSFD）能够通过预测人类专家标注的故障属性来识别未见故障。我们首先认识到ZSFD需要应对工业过程中的持续变化，即模型在适应新故障类别和属性的同时避免遗忘先前已学到的诊断能力。为了克服现有ZSFD范式无法在工业场景中从不断演进的训练数据流中学习的问题，首次提出了增量ZSFD（IZSFD）范式，该范式在传统ZSFD和广义ZSFD范式中均结合了类别增量和属性增量。为实现IZSFD，我们提出了一种宽深混合防遗忘框架（BDMAFF），旨在从新故障类别和属性中学习。为解决遗忘问题，BDMAFF从特征和属性原型两个角度有效积累先前获取的知识。特征记忆通过采用防遗忘训练策略的深度生成模型建立，确保历史类别的生成质量受到监督和维护。诊断模型借助生成模型生成的样本看见未见故障。属性原型记忆通过受宽学习系统启发的诊断模型建立。与传统增量学习算法不同，BDMAFF为诊断模型引入了记忆驱动的迭代更新策略，使模型无需存储所有历史训练样本即可学习新故障和属性。通过真实液压系统和Tennessee-Eastman基准过程验证了所提方法的有效性。
英文摘要：
Zero-shot fault diagnosis (ZSFD) is capable of identifying unseen faults via predicting fault attributes labeled by human experts. We first recognize the demand of ZSFD to deal with continuous changes in industrial processes, i.e., the model's ability to adapt to new fault categories and attributes while avoiding forgetting the diagnosis ability learned previously. To overcome the issue that the existing ZSFD paradigm cannot learn from evolving streams of training data in industrial scenarios, the incremental ZSFD (IZSFD) paradigm is proposed for the first time, which incorporates category increment and attribute increment for both traditional ZSFD and generalized ZSFD paradigms. To achieve IZSFD, we present a broad-deep mixed anti-forgetting framework (BDMAFF) that aims to learn from new fault categories and attributes. To tackle the issue of forgetting, BDMAFF effectively accumulates previously acquired knowledge from two perspectives: features and attribute prototypes. The feature memory is established through a deep generative model that employs anti-forgetting training strategies, ensuring the generation quality of historical categories is supervised and maintained. The diagnosis model SEEs the UNSEEN faults with the help of generated samples from the generative model. The attribute prototype memory is established through a diagnosis model inspired by the broad learning system. Unlike traditional incremental learning algorithms, BDMAFF introduces a memory-driven iterative update strategy for the diagnosis model, which allows the model to learn new faults and attributes without requiring the storage of all historical training samples. The effectiveness of the proposed method is verified by a real hydraulic system and the Tennessee-Eastman benchmark process.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
