Root Cause Of Oscillations Research Articles

Investigation of Abnormal Level Control Oscillations in a BWR Feedwater System

In the long-term operation of nuclear power plants, the aging of systems, structures, and components can lead to maintenance issues that must be dealt with to maintain cost-effective plant operations. One common issue affecting the currently operated boiling water reactors is the onset of unexpected level oscillations in feedwater heaters. This phenomenon can cause excessive cycling of drain valves and lead to premature failures. In this work, we develop a dynamic model of a set of feedwater heaters to determine the root cause of oscillations observed in an operating plant. Simulation results of various transient scenarios were used to investigate the effects of the controller parameters, boundary conditions, and possible valve and instrument issues. The analysis led to the conclusion that the most likely causes of the observed self-sustained oscillations in the system are the nonlinear behaviors of the drain valve and the level transmitter induced by degraded equipment condition. A partial plug of the pressure line used for level sensing in the system can account for a significant deadtime in the level transmitter, a nonlinear effect shown to induce self-sustained oscillatory behaviors.

Root cause diagnosis of plant-wide oscillations based on fuzzy kernel multivariate Granger causality

BackgroundPlant-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. MethodIn 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 findingsOur 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.

Application of Alarm Correlations in Root Cause Diagnosis of Plant-wide Oscillations*

Plant-wide oscillations are common types of disturbances in industrial processes, whose effects propagate to multiple process units through material and/or information flow paths. Such oscillations cause poor controller performance and thus can impact product quality and overall plant performance. Early detection of oscillations and root cause diagnosis is essential. Constructing an adjacency matrix is an efficient method to locate the root cause of oscillations and uses control loop digraphs to capture controller interactions from the process flowsheet. However, constructing an adjacency matrix is time-consuming and resource-intensive, especially when dealing with complex industrial facilities. This paper proposes an alternative data-driven method to construct an adjacency matrix using alarm correlations extracted from historical Alarm & Event (A&E) data. A component-level adjacency matrix is constructed by consolidating process unit information from alarm configuration attributes. Potential root causes of oscillations are shortlisted for further investigations using a process knowledge-based method. This preliminary diagnosis tool can reduce the computational complexity of the subsequent knowledge-driven stage. The applicability and effectiveness of the proposed method are demonstrated via a case study using a benchmark industrial dataset.

Root cause analysis and diagnosis of solid oxide fuel cell system oscillations based on data and topology-based model

Solid oxide fuel cell system is a energy conversion device with the advantages of low emissions, high efficiency and long life. However, the occurrence and propagation of oscillations are common in a system. When certain variables oscillate, the lifetime of system is significantly reduced and the output electrical characteristics are affected. Therefore, it is important to analyze and diagnose the root cause of solid oxide fuel cell system oscillations to prevent the propagation of the oscillations. An independent solid oxide fuel cell system consists of multiple subsystems, and each subsystem consists of multiple process variables. It is not easy to locate the root cause of the oscillations accurately. A combination of data-driven causality and topology-based model is adopted in this paper, which provides a complete procedure for diagnosing system oscillations. First, the method of combining principal component analysis and oscillation significance index is chosen to select feature variables. Then the data-driven Granger causality analysis is applied to provide reliable diagnosis of oscillation source. Finally, the diagnosis result is further enhanced by topology-based model which takes process connectivity and knowledge into account. Through analysis and system experiment, the source of oscillations Feed CH4 PV is successfully found. The result shows that the method based on the combination of data and topology model can accurately locate the root cause of oscillations.

Plant-wide oscillation detection using multivariate empirical mode decomposition

Plant-wide oscillation detection is an important task in the maintenance of large-scale industrial control systems, owing to the fact that in an interactive multi-loop environment oscillation generated in one loop may propagate to the different parts of the plant. In such a scenario, it is required that different loops oscillating due to a common cause and hence similar frequency may be grouped together. In this paper an adaptive method for plant-wide oscillation detection based on multivariate empirical mode decomposition (MEMD) along with a grouping algorithm is proposed. The method can identify multiple oscillation groups among different variables as well as variables with random noise only. The proposed method is also applicable to both non-linear and non-stationary time series where the techniques based on the conventional Fourier analysis are prone to errors. Within each group that oscillate due to a common cause, the method can also indicate the location of the probable root cause of oscillations. The efficacy of the proposed method is established with the help of both simulation and industrial case studies.