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Fault Diagnosis of Complex Processes Using Sparse Kernel Local Fisher Discriminant Analysis

机译:使用稀疏内核本地Fisher判别分析的复杂过程故障诊断

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摘要

As an outstanding discriminant analysis technique, Fisher discriminant analysis (FDA) gained extensive attention in supervised dimensionality reduction and fault diagnosis fields. However, it typically ignores the multimodality within the measured data, which may cause infeasibility in practice. In addition, it generally incorporates all process variables without emphasizing the key faulty ones when modeling the complex process, thus leading to degraded fault classification capability and poor model interpretability. To ease the above two drawbacks of conventional FDA, this brief presents an advantageously sparse local FDA (SLFDA) model, it first preserves the within-class multimodality by introducing local weighting factors into scatter matrix. Then, the responsible faulty variables are identified automatically through the elastic net algorithm, and the current optimization problem is subsequently settled through the feasible gradient direction method. Since then, the local data structure characteristics are exploited from both the sample dimension and variable dimension so that the fault diagnosis performance and model interpretability are significantly enhanced. In addition, we naturally extend SLFDA model to nonlinear variant (i.e., sparse kernel local FDA) by the kernel trick, which is substantially more resistant to strong nonlinearity. The simulation studies on Tennessee Eastman (TE) benchmark process and real-world diesel engine working process both validate that the novel diagnosis strategy is more accurate and reliable than the existing state-of-the-art methods.
机译:作为一个突出的判别分析技术,Fisher判别分析(FDA)在监督减少和故障诊断领域获得了广泛的关注。然而,它通常忽略测量数据内的多模,这可能会导致实践中的不可行度。此外,它通常包含所有处理变量,而无需强调在建模复杂过程时强调关键故障,从而导致具有降级的故障分类能力和模型解释性差。为了缓解以上传统FDA的缺点,本发明简述呈现了一个有利的局部FDA(SLFDA)模型,首先通过将局部加权因子引入散射矩阵来保留课堂内部的多模。然后,通过弹性网络算法自动识别负责任的故障变量,随后通过可行的梯度方向方法沉降电流优化问题。从那时起,局部数据结构特性从样品尺寸和可变尺寸都利用,从而显着提高了故障诊断性能和模型解释性。此外,我们通过内核诀窍自然将SLFDA模型扩展到非线性变体(即,稀疏的内核本地FDA),这对强度的非线性显着耐受。田纳西州伊斯特曼(TE)基准工艺和现实世界柴油发动机工作过程的仿真研究既验证了新型诊断策略比现有最先进的方法更准确可靠。

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