Lajmi Fatma, Mhamdi Lotfi, Abdelbaki Wiem, Dhouibi Hedi, Younes Khaled
National Engineering School of Sousse, ENISO Laboratory: Networked Objects, Control, and Communication Systems (NOCCS), Sousse 4054, Tunisia.
National School of Engineering Monastir, Rue Ibn ELJazzar, Monastir 5019, Tunisia.
Sensors (Basel). 2023 Aug 3;23(15):6899. doi: 10.3390/s23156899.
The paper focuses on the importance of prompt and efficient process fault detection in contemporary manufacturing industries, where product quality and safety protocols are critical. The study compares the efficiencies of two techniques for process fault detection: Kernel Principal Component Analysis (KPCA) and the observer method. Both techniques are applied to observe water volume variation within a hydraulic system comprising three tanks. PCA is an unsupervised learning technique used for dimensionality reduction and pattern recognition. It is an extension of Principal Component Analysis (PCA) that utilizes kernel functions to transform data into higher-dimensional spaces, where it becomes easier to separate classes or identify patterns. In this paper, KPCA is applied to detect faults in the hydraulic system by analyzing the variation in water volume. The observer method originates from control theory and is utilized to estimate the internal states of a system based on its output measurements. It is commonly used in control systems to estimate the unmeasurable or hidden states of a system, which is crucial for ensuring proper control and fault detection. In this study, the observer method is applied to the hydraulic system to estimate the water volume variations within the three tanks. The paper presents a comparative study of these two techniques applied to the hydraulic system. The results show that both KPCA and the observer method perform similarly in detecting faults within the system. This similarity in performance highlights the efficacy of these techniques and their potential adaptability in various fault diagnosis scenarios within modern manufacturing processes.
本文聚焦于当代制造业中快速高效的过程故障检测的重要性，在当代制造业中，产品质量和安全协议至关重要。该研究比较了两种过程故障检测技术的效率：核主成分分析（KPCA）和观测器方法。这两种技术都被用于观察一个由三个水箱组成的液压系统中的水量变化。主成分分析（PCA）是一种用于降维和模式识别的无监督学习技术。它是主成分分析（PCA）的扩展，利用核函数将数据转换到更高维空间，在这个空间中更容易分离类别或识别模式。在本文中，KPCA通过分析水量变化来检测液压系统中的故障。观测器方法源于控制理论，用于根据系统的输出测量值估计系统的内部状态。它常用于控制系统中估计系统不可测量或隐藏的状态，这对于确保正确控制和故障检测至关重要。在本研究中，观测器方法被应用于液压系统，以估计三个水箱内的水量变化。本文对应用于液压系统的这两种技术进行了比较研究。结果表明，KPCA和观测器方法在检测系统故障方面表现相似。这种性能上的相似性突出了这些技术的有效性及其在现代制造过程中各种故障诊断场景中的潜在适用性。
