摘要
Background: Plant-wide oscillations are commonly observed in industrial processes and can have significant impacts on product quality and energy consumption. Accurately diagnosing the root causes of these oscillations is essential to mitigate their effects. However, when these oscillations occur, the resulting data often exhibits strong nonlinearity, making root cause diagnosis a challenging task. Method: In this study, we propose the use of kernel multivariate Granger causality (KMGC) test as an analytical tool for root cause diagnosis. The kernel function allows for low-dimensional data to be mapped to a higher-dimensional space, making it easier to process data with strong nonlinearity. To address the computational complexity associated with complex kernel functions, we have combined the KMGC with a fuzzification function to create the fuzzy kernel multivariate Granger causality (FKMGC) analysis method. This approach improves computational efficiency, particularly in industrial scenarios where Gaussian kernel functions are commonly used. Significant findings: Our simulation and plant-wide oscillation case study demonstrate that FKMGC accurately identifies the root cause of oscillations. Furthermore, FKMGC significantly reduces analysis time compared to Gaussian kernel-based algorithms, with a 95% reduction in running time observed in our experiments. These findings suggest that FKMGC is an effective and efficient tool for diagnosing the root causes of plant-wide oscillations in industrial settings.