Root cause isolation of propagated oscillations in process plants

Persistent whole-plant disturbances can have an especially large impact on product quality and running costs. There is thus a motivation for the automated detection of a plant-wide disturbance and for the isolation of its sources. Oscillations increase variability and can prevent a plant from operating close to optimal constraints. They can also camouflage other behaviour that may need attention such as upsets due to external disturbances. A large petrochemical plant may have a 1000 or more control loops and indicators, so a key requirement of an industrial control engineer is for an automated means to detect and isolate the root cause of these oscillations so that maintenance effort can be directed efficiently. The propagation model that is proposed is represented by a log-ratio plot, which is shown to be ‘bell’ shaped in most industrial situations. Theoretical and practical issues are addressed to derive guidelines for determining the cut-off frequencies of the ‘bell’ from data sets requiring little knowledge of the plant schematic and controller settings. The alternative method for isolation is based on the bispectrum and makes explicit use of this model representation. A comparison is then made with other techniques. These techniques include nonlinear time series analysis tools like Correlation dimension and maximal Lyapunov Exponent and a new interpretation of the Spectral ICA method, which is proposed to accommodate our revised understanding of harmonic propagation. Both simulated and real plant data are used to test the proposed approaches. Results demonstrate and compare their ability to detect and isolate the root cause of whole plant oscillations. Being based on higher order statistics (HOS), the bispectrum also provides a means to detect nonlinearity when oscillatory measurement records exist in process systems. Its comparison with previous HOS based nonlinearity detection method is made and the bispectrum-based is preferred

Plant-Wide Diagnosis: Cause-and-Effect Analysis Using Process Connectivity and Directionality Information

Production plants used in modern process industry must produce products that meet stringent environmental, quality and profitability constraints. In such integrated plants, non-linearity and strong process dynamic interactions among process units complicate root-cause diagnosis of plant-wide disturbances because disturbances may propagate to units at some distance away from the primary source of the upset. Similarly, implemented advanced process control strategies, backup and recovery systems, use of recycle streams and heat integration may hamper detection and diagnostic efforts. It is important to track down the root-cause of a plant-wide disturbance because once corrective action is taken at the source, secondary propagated effects can be quickly eliminated with minimum effort and reduced down time with the resultant positive impact on process efficiency, productivity and profitability. In order to diagnose the root-cause of disturbances that manifest plant-wide, it is crucial to incorporate and utilize knowledge about the overall process topology or interrelated physical structure of the plant, such as is contained in Piping and Instrumentation Diagrams (P&IDs). Traditionally, process control engineers have intuitively referred to the physical structure of the plant by visual inspection and manual tracing of fault propagation paths within the process structures, such as the process drawings on printed P&IDs, in order to make logical conclusions based on the results from data-driven analysis. This manual approach, however, is prone to various sources of errors and can quickly become complicated in real processes. The aim of this thesis, therefore, is to establish innovative techniques for the electronic capture and manipulation of process schematic information from large plants such as refineries in order to provide an automated means of diagnosing plant-wide performance problems. This report also describes the design and implementation of a computer application program that integrates: (i) process connectivity and directionality information from intelligent P&IDs (ii) results from data-driven cause-and-effect analysis of process measurements and (iii) process know-how to aid process control engineers and plant operators gain process insight. This work explored process intelligent P&IDs, created with AVEVA® P&ID, a Computer Aided Design (CAD) tool, and exported as an ISO 15926 compliant platform and vendor independent text-based XML description of the plant. The XML output was processed by a software tool developed in Microsoft® .NET environment in this research project to computationally generate connectivity matrix that shows plant items and their connections. The connectivity matrix produced can be exported to Excel® spreadsheet application as a basis for other application and has served as precursor to other research work. The final version of the developed software tool links statistical results of cause-and-effect analysis of process data with the connectivity matrix to simplify and gain insights into the cause and effect analysis using the connectivity information. Process knowhow and understanding is incorporated to generate logical conclusions. The thesis presents a case study in an atmospheric crude heating unit as an illustrative example to drive home key concepts and also describes an industrial case study involving refinery operations. In the industrial case study, in addition to confirming the root-cause candidate, the developed software tool was set the task to determine the physical sequence of fault propagation path within the plant. This was then compared with the hypothesis about disturbance propagation sequence generated by pure data-driven method. The results show a high degree of overlap which helps to validate statistical data-driven technique and easily identify any spurious results from the data-driven multivariable analysis. This significantly increase control engineers confidence in data-driven method being used for root-cause diagnosis. The thesis concludes with a discussion of the approach and presents ideas for further development of the methods

A wide area system for power transmission security enhancement using a process systems approach

Imperial Users onl

Control loop performance monitoring in an industrial setting

The wide range of applications for single input single output controllers have encouraged interest in monitoring their performance. Over the past two decades researchers in the area have found many performance enhancement opportunities by applying these techniques. These are most evident in large operational plants with hundreds of controllers being monitored at the same time. Early performance measures were based on minimum variance control as a benchmark for controller performance. Many other procedures have since emerged that have improved the level of accuracy in these performance measures. In addition, these improvements made it easier to implement control loop performance monitoring in large industrial settings. This thesis looks at the performance measures in use for single input single output controllers. The work here looks at incorporating these different measures for a specific manufacturing plant. Ways of identifying the goals and objectives of controllers in a system are presented. Furthermore, measures are proposed that most accurately indicate if these goals and objectives are being met. The concept is demonstrated on a distillation system in a gas plant. It is shown how using these objective driven techniques can provide the user with sound results. These results do not require much user analysis to identify sources of problems and areas of improvement

Control loop measurement based isolation of faults and disturbances in process plants

This thesis focuses on the development of data-driven automated techniques to enhance performance assessment methods. These techniques include process control loop status monitoring, fault localisation in a number of interacting control loops and the detection and isolation of multiple oscillations in a multi-loop situation. Not only do they make use of controlled variables, but they also make use of controller outputs, indicator readings, set-points and controller settings. The idea behind loop status is that knowledge of the current behaviour of a loop is important when assessing MVC-based performance, because of the assumptions that are made in the assessment. Current behaviour is defined in terms of the kind of deterministic trend that is present in the loop at the time of assessment. When the status is other than steady, MVC-based approaches are inappropriate. Either the assessment must be delayed until steady conditions are attained or other methods must be applied. When the status is other than steady, knowledge of current behaviour can help identify the possible cause. One way of doing this is to derive another statistic, the overall loop performance index (OLPI), from loop status. The thesis describes a novel fault localisation technique, which analyses this statistic to find the source of a plant-wide disturbance, when a number of interacting control loops are perturbed by a single dominant disturbance/fault. Although the technique can isolate a single dominant oscillation, it is not able to isolate the sources of multiple, dominant oscillations. To do this, a novel technique is proposed that is based on the application of spectral independent component analysis (ICA). Spectral independent component analysis (spectral ICA) is based on the analysis of spectra derived via a discrete Fourier transform from time domain process data. The analysis is able to extract dominant spectrum-like independent components each of which has a narrow-bank peak that captures the behaviour of one of the oscillation sources. It is shown that the extraction of independent components with single spectral peaks can be guaranteed by an ICA algorithm that maximises the kurtosis of the independent components (ICs). This is a significant advantage over spectral principle component analysis (PCA), because multiple spectral peaks could be present in the extracted principle components (PCs), and the interpretation of detection and isolation of oscillation disturbances based on spectral PCs is not straightforward. The novel spectral ICA method is applied to a simulated data set and to real plant data obtained from an industrial chemical plant. Results demonstrate its ability to detect and isolate multiple dominant oscillations in different frequency ranges