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Faults Detection and Isolation computational tool using Neural Networks and State Observers

Gloria Mousalli-Kayat* Jesús Calderón-Vielma** Francklin Rivas-Echeverría*** Addison Ríos-Bolívar *** Universidad de Los Andes *Departamento de Medición y Evaluación **Departamento de Circuitos y Medidas ***Departamento de Sistemas de Control Mérida, Venezuela 5101

Abstract:-This work presents the design of a computational tool for fault Detection and Diagnostic in industrial processes, through the integration of a virtual instrument developed in LabVIEW™ and a computer application in MATLAB® for simulating an industrial process. Additionally, two faults detecting filters were developed using MatLab®: one based on state observers and another based on a heuristic method and implemented through a neural network. This tool has been made in English and Spanish in order to be handle by both language users.

Key words:- LabVIEW™, MatLab®, Integration, Fault Detection, State Observers, Neural Networks.

1 Introduction faults detection and diagnostic using virtual system and Section 4 contains the corresponding One type of programs that have had great growing in conclusions. the last decades are the simulators, which try to support the learning, emulating reality situations [3]. 2 Faults detection These programs have wide applications in the engineering field, because they can be used for It is well-known that the operational reliability modelling and simulating different kind of processes should be conformed by: the correct operation of the using a personal computer (PC). processes, the appropriate control systems and the coordination. This whole infrastructure is held by Simulators allow to create similar atmospheres as the diverse support systems inside an integral automation found in industrial control rooms and using them, it structure, where the information and its exchanges is feasible to accumulate knowledge and experiences are considered outstanding, from the point of view of that can be used in real conditions. reliability, security and productivity. In any level of production chain, this information should be In this work the capabilities of LabVIEW™[4] are managed for maintaining high efficiency indexes and used for developing human-machine interfaces operational productivity. (HMI), combined with a processes simulation program developed using MatLab®[5] for creating a Inside a mark of reliable and safe operation, there computational tool, with the main objective of should be presented the systems that allow the events helping the user with the fault detection and recognition, which should guide the decision- diagnosis methods. The assistance is achieved makings when the behavior of the productive process through a group of tests over a process and the user is affected by the presence of any adverse will observe the behavior of the system under eventuality. Since the reliability is very near to the different fault conditions. concept of security, then, it is fundamental to provide industrial processes with demanding mechanisms of The paper is structured as follows: Section 2 presents security whose basic elements are the Supervision, a brief introduction to fault Detection. In section 3 it Diagnostic and Detection (SDD) systems; use the is described the developed computational tool for indicators and the measured variables of the processes, for maintaining a continuous and constant these techniques are in the building phase or for supervision of the evolutionary behavior in the having very accurate models. production time, in order to report any behavior that is considered abnormal. In analytic techniques, all the information coming from the processes measurement devices is used for The SDD systems are based in their capacity to obtaining a mathematical model for diagnostic. The respond under unexpected situations concerning the processes used for generating these residuals are [9, process behavior, so their main task is the Faults 10, 11]: Diagnosis and Detection, (FDD). A FDD system, as the one shown in the Figure 1, uses the 1. The direct substitution in the model equations. measurements of the process in order to produce residuals, which, by means of evaluation functions 2. The use of the model together with the real and decision logics, looks for the faults process, so that in both the same inputs are applied. identificability and separability. So, any system that allows, starting from measured variables of the 3. The use of a state observer. This is an extension of processes, to generate residuals and to evaluate this the method of the parallel model. The main idea is residuals deeply, considering decision makings for the residual generation, with precise directional faults recognition is denominated Faults Detection properties, by means of an appropriate selection of and Diagnostic Filter. the observer's gain.

4. The conception of an inverse model in order to reconstruct the faults.

2.1 Observers-based Filter design

The state observers are analytic techniques based-on the fault detection and diagnostic (FDD) filters design. The FDD filters design can be divided in two stages: the first phase is the residuals generation (detection problem). The second stage is the residuals evaluation in order to determinate the Figure 1. SDD System origin of the faults, (faults separation problem). So, the residuals are scalar or vector signals that contain From the point of view of comparison-based the information about the time and localization of the residual generation, the filters design techniques for faults. In principle, the residuals should be zero in FDD can be classified as: absence of faults and, obviously, different from zeros when some fault appear [14]. 1.- Methods based on Physical Redundancy: In this methods, it is used several Under those premises, the state observers can be physical sensors of the devices and systems under used for residuals generation. The idea is to build a study. These residuals are obtained by the complete order classic observer, for the system given comparison between the answers of the different in (1), using the output variables y(t) and the control elements. These techniques have the main variables u(t), in order to produce a vector of inconvenience of high costs involved in their estimated states. The residuals are obtained implementation and pursuit. comparing the estimated output with the measured output of the physical plant.

2.- Methods based in Models: In this x˙(t)  Ax(t)  Bx(t), x(0)  x0 methods, it is produced estimated values for the y(t)  Cy(t) (1) processes variables and are used for the residuals generation, by means of their comparison with the Then, for the system (1) a gain matrix D  nxq measured outputs. The main inconveniences of exists in such a way that the estimation xˆ(t) of the state vector x(t) will be the solution for complete None -1 -1 -1 -1 order observer's equation: Fault 1 1 -1 -1 -1 x˙ˆ(t)  Axˆ(t)  Bx(t)  D(y(t)  Cxˆ(t)), Fault 2 -1 1 -1 -1 yˆ(t)  Cyˆ(t) (2) ⋮ ⋮ ⋮ ⋮ ⋮ Fault i -1 -1 1 -1 The outputs of system (2) are the estimated outputs ⋮ ⋮ ⋮ ⋮ ⋮ and the observer gain matrix D should be selected Fault n -1 -1 -1 1 appropriately. So, defining the error signal by: Table 1. Neural Networks design for Fault detection e(t)  x(t)  xˆ(t) (3) which produces an innovation in the output defined y by  1 x e˙(t)  (A  DC)e(t)  Af f p  B f fa  DC f fs 1

η(t)  Ce(t)  C f fs (4) x2 then, the error dynamics and the corresponding output error will be given as x y η(t)  y(t)  yˆ(t) (5) n  n

If D is selected in such a way that (A-DC) is stable, that means, all their eigenvalues has negative real Figure 2. Fault detection Scheme using Neural part. In the limit, t, the estimation error will be Networks null (e(t)=0). In this case, it is said that the observer is exponential or asymptotic. Since for t

Fault Y1 Y2 Yi Yn Valve Resistance R0 0.01 R1 0.033 R2 0.04 R3 0.016

The possible faults that have been considered in this system are the blockage of any of the four valves individually or at the same time.

Figure 3. Computational Tool Main Panel The equations that represent the system of Figure 4 are: 1 1 1 1 1 The application example presented in this work h˙ (t)  (  )( )h (t)  ( )h (t)  ( )u(t) consists on a system with three interconnected 1 1 2 r0 r1 a1 r0a1 a1 tanks, as is depicted the Figure 4 [15]. ˙ 1 1 1 1 h2 (t)  ( )h1(t)  (  )( )h2 (t) r0a2 r0 r2 a2 ˙ 1 1 1 h3 (t)  ( )h1(t)  ( )h2 (t)  ( )h3 (t) r1a3 r2a3 r3a3

Under normal operational conditions, the four valves that compose the system are open and it is assumed that the supply of liquid is constant. As the time continues, the levels in the three tanks are increased, and the supply of liquid from tank 1 to tank 2 neither the supply from tanks 1 and 2 to tank 3 are interrupted. Figure 4. Three interconnected Tanks System

The system presented in Figure 4 consists of three Figure 5 illustrates the HMI designed for this tanks, where the tank 1 receive a constant flow u(t) = process using LabVIEW. The user will be able to 5000 cm3/s, and it feeds tank 2 and tank 3. It is generate any of the four possible faults and to assumed that the tanks have the following observe the behavior of the tanks levels. dimensions: Additionally, the user will have a help file that includes the system equations and the tank levels Tank Area Height Initial behaviors under different faults conditions for the Conditions system. 1 2500 cm2 100 cm 0.5 cm 2 2000 cm2 80 cm 4.5 cm 3 3000 cm2 80 cm 0.2 cm

Starting from these values and considering that the tank 1 flow is equal to 60% of the input flow u(t), and the tank 2 flow are equal to 40% of the input flow u(t); it was obtained the resistances values and flows of each one of the four valves.

5 References

[1] Aguilar J., Rivas F, Introducción a las técnicas de Computación Inteligente. Meritec, Venezuela (2001) [2] Carbonell, J.R, AI in CAI: An Artificial Intelligence Approach to Computer-Assisted-Instruction. IEEE Transactions on Man-Machine Systems, 190-202 (1970). [3] Galvis, A., Ingeniería de Software Educativo. Ediciones Uniandes, Colombia p 22-23 (1992). [4] National Instruments Corporation, LabVIEW. User Manual. USA (2000). [5] The MathWorks Inc., Matlab Language of Technical Computing. USA (1999). Figure 5. Virtual Instrument developed using [6] Petzold, C., Programación en Windows® 95, LabVIEW MacGraw-Hill Interamericana de España, S.A, España (1996). The computational tool implements the designed [7] Calderón-Vielma, J. Viloria et al, Integración de detecting filters using the two methods introduced herramientas de programación para la enseñanza previously. One of the filters is based on analytic de procesos. XVIII Interamerican Congress of redundancy methods that calculate the residuals Chemical Engineering, Puerto Rico (1998). starting from a state observer. The other filter is [8] Calderón-Vielma, J, Laboratorio Virtual para la based on a heuristic method and implemented enseñanza de Automatización e Instrumentación through a neural network; this neural network was Industrial. Coloquio de Automatización y Control, Venezuela (1999). trained using different operational conditions. Both [9] Rios, A., Sur la Synthèse de Filtres dde Détection de filters are simulated in MatLab® and when those Défaillances. Ph.D Thesis, Université Paul filters detect a fault condition it sends a signal to Sabatier, Toulouse. France. (2001). LabVIEW™ and immediately it turns on a light [10] Szigeti F., J. Bokor, Edelmayer A. And Tarantino signal indicating the fault that has happened. Using R., Fault detection Filter Design for Linear Time this system, the users can evaluate the filters Varying Systems: Algebraic-geometric. 4th operation and efficiency based on the obtained European Control Conference, Belgium (1997). results. [11] Keviczky L., Bokor J., Edelmeyer A. and Szigeti F, Detection Filter Design in System Perturbation Application to Robust Change Detection and 4 Conclusions Identification, Proc. 12th IFAC World Congress Vol 7 517-520, Australia (1992). The designed computational tool for fault detection [12] Massoumnia M. A., A geometric approach to the and diagnostic can be used for personal training in synthesis of failure detection filters. IEEE Trans. this area, allowing on-line interaction with Aut. Control. AC-31.9. 839-846 (1986). MatLab®. [13] Rios, A., Mousalli G. and Rivas F., Invertibility and Neural Networks based FDI Filter. IASTED-ISC, Japan (2002). The use of a computer-based tool, allows an [14] Mousalli G., Calderón J., Ríos A., Rivas F., interactive way for studying fault detection and Learning Faults Detection and Diagnostic using diagnostic methods based on state observers and Virtual System. Accepted for Publication on IJEE artificial neural networks. Special Issue on Virtual Instrumentation. USA (2004) This type of computational tools helps the users to [15] Novoa D., Pérez A., Rivas F., Fault detection interact with industrial processes, to modify its scheme using Neo-fuzzy neurons. Proceedings of operational conditions and to verify the obtained IASTED International Conference on Intelligent faults and its consequences. This helps to obtain an Systems and Control. Hawaii USA (2000). inspection-based learning of real systems operational conditions.

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