Joaquim Armengol

A Survey on Interval Model Simulators and their Properties Related to Fault Detection

Abstract The imprecision and the uncertainty of many systems can be expressed with interval models. The results of the simulation of these models can be represented by envelopes. These envelopes can be characterised by several properties such as completeness or soundness that lead to the concepts of overbounded and underbounded envelopes. The simulation of such interval models can be performed by several means including qualitative and semiqualitative methods as well as quantitative simulation based ones. A brief description of the different types of simulators is presented and their respective properties are outlined and compared in relation to model-based fault detection.

Fault detection and isolation of the three-tank system using the modal interval analysis

Abstract Analytical redundancy is a widely used technique for fault detection. It consists of comparing the behaviour of a real system with a reference obtained by simulation of its model. The main problem is that there are always imprecisions and uncertainties which are not represented in the model so the behaviour of the real system and the behaviour of the model are not exactly the same. One way to represent these uncertainties in the model is using interval models. The results of the simulation of these types of models may be represented by envelopes. This paper proposes an approach to generate envelopes based on interval techniques of the modal interval analysis. As an example, this approach is used to detect and isolate faults in a physical system formed by three interconnected tanks.

Minimal Structurally Overdetermined Sets for Residual Generation: A Comparison of Alternative Approaches

The issue of residual generation using structural analysis has been studied by several authors. Structural analysis does not permit to generate the analytical expressions of residuals since the model of the system is abstracted by its structure. However, it determines the set of constraints from which residuals can be generated and it provides the computation sequence to be used. This paper presents and compares four recently proposed algorithms that solve this problem.

Robust Fault Detection System for Insulin Pump Therapy Using Continuous Glucose Monitoring

Background: The popularity of continuous subcutaneous insulin infusion (CSII), or insulin pump therapy, as a way to deliver insulin more physiologically and achieve better glycemic control in diabetes patients has increased. Despite the substantiated therapeutic advantages of using CSII, its use has also been associated with an increased risk of technical malfunctioning of the device, which leads to an increased risk of acute metabolic complications, such as diabetic ketoacidosis. Current insulin pumps already incorporate systems to detect some types of faults, such as obstructions in the infusion set, but are not able to detect other types of fault such as the disconnection or leakage of the infusion set. Methods: In this article, we propose utilizing a validated robust model-based fault detection technique, based on interval analysis, for detecting disconnections of the insulin infusion set. For this purpose, a previously validated metabolic model of glucose regulation in type 1 diabetes mellitus (T1DM) and a continuous glucose monitoring device were used. As a first step to assess the performance of the presented fault detection system, a Food and Drug Administration-accepted T1DM simulator was employed. Results: Of the 100 in silico tests (10 scenarios on 10 subjects), only two false negatives and one false positive occurred. All faults were detected before plasma glucose concentration reached 300 mg/dl, with a mean plasma glucose detection value of 163 mg/dl and a mean detection time of 200 min. Conclusions: Interval model-based fault detection has been proven (in silico) to be an effective tool for detecting disconnection faults in sensor-augmented CSII systems. Proper quantification of the uncertainty associated with the employed model has been observed to be crucial for the good performance of the proposed approach.

Interval Model-Based Fault Detection Using Multiple Sliding Time Windows

Abstract Interval models may be used in many cases to express the imprecisions and uncertainties of the systems. Envelopes are a way to represent the results of the simulation of these models. One of their applications is as reference behaviour for Fault Detection (FD) based on analytical redundancy, so their properties (completeness, soundness) have important consequences on the FD results (missed and false alarms). This paper presents the Modal Interval Simulator (MIS) that approaches this problem by means of error-bounded envelopes. Sliding time windows have to be used for long simulations, depending the adequate window length on the type of the fault. MIS allows to the user to work with several window lengths simultaneously.

A review of personalized blood glucose prediction strategies for T1DM patients

This paper presents a methodological review of models for predicting blood glucose (BG) concentration, risks and BG events. The surveyed models are classified into three categories, and they are presented in summary tables containing the most relevant data regarding the experimental setup for fitting and testing each model as well as the input signals and the performance metrics. Each category exhibits trends that are presented and discussed. This document aims to be a compact guide to determine the modeling options that are currently being exploited for personalized BG prediction.

Fault Detection in a Pilot Plant Using Interval Models and Multiple Sliding Time Windows

Abstract Analytical redundancy is one of the techniques that can be used for Fault Detection. An important problem in this case is how the uncertainty associated to the systems and the measurements is taken into account. This paper proposes to consider them by means of interval models and interval measurements. The consistency between them is checked and a fault is detected when there is an inconsistency thus avoiding false alarms. The used technique is also based on Modal Interval Analysis which provides tools to compute interval extensions of real functions with the adequate semantics and saves much computational effort compared to other techniques based on global optimization algorithms. Time windows of different lengths are used in order to improve the Fault Detection results. This method is being applied to several real processes within the European project CHEM.

SQualTrack: A Tool for Robust Fault Detection

One of the techniques used to detect faults in dynamic systems is analytical redundancy. An important difficulty in applying this technique to real systems is dealing with the uncertainties associated with the system itself and with the measurements. In this paper, this uncertainty is taken into account by the use of intervals for the parameters of the model and for the measurements. The method that is proposed in this paper checks the consistency between the systems behavior, obtained from the measurements, and the models behavior; if they are inconsistent, then there is a fault. The problem of detecting faults is stated as a quantified real constraint satisfaction problem, which can be solved using the modal interval analysis (MIA). MIA is used because it provides powerful tools to extend the calculations over real functions to intervals. To improve the results of the detection of the faults, the simultaneous use of several sliding time windows is proposed. The result of implementing this method is semiqualitative tracking (SQualTrack), a fault-detection tool that is robust in the sense that it does not generate false alarms, i.e., if there are false alarms, they indicate either that the interval model does not represent the system adequately or that the interval measurements do not represent the true values of the variables adequately. SQualTrack is currently being used to detect faults in real processes. Some of these applications using real data have been developed within the European project advanced decision support system for chemical/petrochemical manufacturing processes and are also described in this paper.

Analysis of the Robustness of Predictive Controllers via Modal Intervals

This paper aims to start exploring the application of interval techniques to deal with robustness issues in the context of predictive control. The robust stability problem is transformed into that of checking the positivity of a rational function. Modal intervals are presented as a useful tool to deal with this kind of function.Modal interval analysis extends real numbers to intervals, identifying the intervals by the predicates that the real numbers fulfill, unlike classical interval analysis which identifies the intervals with the set of real numbers that they contain. Modal interval analysis not only simplifies the computation of interval functions but also allows semantic interpretations of the results. These interpretations are applied to the analysis and design of robust predictive controllers for parametric systems. Necessary, sufficient and, in some cases, necessary and sufficient conditions for robust performance are presented.Specifically, an interval procedure is proposed to compute the stability margin of a predictive control law when applied to a class of plants described by discrete time transfer functions with coefficients that depend polynomially on uncertain parameters.

Semiqualitative Simulation Using Modal Interval Analysis

Abstract Imprecision and uncertainty in systems can often be expressed with interval models. The result of the simulation of these models can be represented in the form of envelope trajectories. These envelopes can be characterised by several properties such as completeness and soundness which lead to the concept of overbounded and underbounded envelopes. Simulation of such interval models can be performed by several means including quantitative, qualitative and semiqualitative techniques. Whereas existing simulators do not provide any information about the “error” with respect to the exact envelope, a method to obtain error-known envelopes is proposed. It is based on the simultaneous computation of an underbounded and an overbounded envelope. Both envelopes are computed by means of Modal Interval Analysis. A way of controlling the error of the envelopes and adjust it to the desired value is presented.