KPIs for Asset Management: A Pump Case Study

The integration of multiple data sources and the convergence of process control systems and business intelligence layer such as the enterprise resource planning (ERP) are paving the way for important progress in plant operation optimization. Numerous companies offer “Analytics Services” to leverage this newly available mine of data but applications still appear to be limited to certain specific types of large plants. Key Performance Indicators (KPIs) is arguably the most used approach to make sense of large and complex systems in a wide variety of fields and its applicability to industrial operations is more and more common, to the extent that standardization of KPIs has become a major topic for the International Organization for Standardization (ISO). While the KPI standard ISO 22400 focuses on KPIs for manufacturing operations management at the plant level, the scope of this thesis is to bring it to the first layer of the control system: the equipment. In addition to being part of the quest for operational excellence and energy efficiency, bringing KPIs to the asset level is an important step towards integration of the different layers of the automation pyramid, integrating in particular control and scheduling. Developed within the frame of the new generation of Operations Management Software for the process industries, this work presents a case study on the most widely used assets in the field – pumps, based on operational data of different plants in the Oil & Gas and Chemicals industries

Industrial fault detection and diagnosis using alarms and process measurements

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

KPIs as the interface between scheduling and control

The integration of scheduling and control has been discussed in the past. While constructing an integrated plant model that may still seem out of reach, scheduling and control systems are increasingly more intertwined. We argue that they are in fact already integrated and give the example of two key performance indicators (KPIs) that are defined in the recent international standard ISO 22400. The focus of this study is on KPIs that consider both planned times and actual times. An amino acid production plant is used in the study, and the production is described from both the scheduling and the control perspective. To illustrate the integration, a schedule is computed containing the planned production times. Resulting measurements from the control system are analyzed for their actual production times using a proposed procedure that detects the start and end time of batches. Using KPIs as the interface between scheduling and control can be used as a strategy for maximizing the plant performance. The study focuses on the process industry