Implementing data-driven fault detection and diagnosis methods on process plants can be a challenge. Constraints due to the availability and the variability of the process measurements as well as constraints due to the characteristics of the industrial systems impact the reliability of fault detection and diagnosis methods. This thesis aims at increasing the reliability of data-driven fault detection and diagnosis methods on process plants to extend their use in industry.
The core idea of the thesis is to bring together the disciplines of alarm management and fault detection and diagnosis. The first part of the thesis suggests a fault detection and diagnosis approach to the problem of classification of ongoing abnormal situations based on alarm data alone. The second part of the thesis investigates the integration of alarms, alarm settings, and alarm management practices into traditional fault detection and diagnosis methods based on process measurements. Both parts emphasize the robustness of the proposed methods with regard to the variability in the input data, as well as the industrial applicability of the methods. The results are validated on an oil and gas separation plant and on a multiphase flow facility.
The last part of the thesis focuses on root cause analysis of process disturbances. Many data-driven root cause analysis methods have been proposed in process literature in the past twenty years, but their reliability depends on the properties of the industrial system and on the properties of the disturbance. This thesis provides a comparative review of data-driven root cause analysis methods clarifying the scope of application of each method. The objective is to guide practitioners during the root cause analysis and facilitate the use of data-driven root cause analysis methods in industry. The comparative review also highlights the gap of knowledge in root cause analysis of transient disturbances and suggests a new approach based on transient disturbance detection methods to fill this gap.Open Acces
