50 IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, VOL. 5, NO. 1, FEBRUARY 2009
Intelligent Automatic Fault Detectionfor Actuator Failures in Aircraft
C. H. Lo, Member, IEEE, Eric H. K. Fung, and Y. K. Wong
Abstract—This paper applies an intelligent technique based onfuzzy-genetic algorithm for automatically detecting failures in air-craft. The fuzzy-genetic algorithm constructs the automatic faultdetection system for monitoring aircraft behaviors. Fuzzy-basedclassifier is employed to estimates the time of occurrence and typesof actuator failure. Genetic algorithms are used to generate an op-timal fuzzy rule set for the classifier. The optimization capability ofgenetic algorithms provides an efficient and effective way to gen-erate optimal fuzzy rules. Different types of actuator failure canbe detected online by the fuzzy-genetic algorithm based automaticfault detection system. Simulations with different actuator failuresof the nonlinear F-16 aircraft model are reported and discussed.
Index Terms—Actuators failure, fault diagnosis, fuzzy system,genetic algorithm.
I. INTRODUCTION
T HERE are various possible failures, like, actuator, sensor,or structural, which may occur on a sophisticated modern
aircraft. Early detection of failures can assist pilot to take properactions during a catastrophic event in order to save an aircraft[1], [2]. The traditional engineering approach to cope with fail-ures on modern aircraft is to use hardware (or physical) redun-dancy. For example, the F-16 aircraft uses an analog fly-by-wirecontrol system with quadruplex sensor comparison and quadru-plex actuator redundancy [3]. However, hardware redundancyrequires additional cost, space and increase complexity whenincorporated into the aircraft. As a result, analytical redundancy(model-based approach) in [4]–[6] is developed which requiresan explicit use of a mathematical model of the system to gen-erate residual . Residual is then evaluated to determine thestate of a system as faulty or not. A fault alarm is then trig-gered when residual surpasses a predefined limit (threshold).Hence, the success and sensitivity of the analytical redundancyapproach depends heavily on the magnitude of the predefineddetection threshold.
Since today’s systems are complicated and nonlinear, itis difficult to achieve an accurate and reliable mathematicalmodel. Hence, false alarms are generated readily. Constantthreshold evaluation strategy even makes the fault detection
Manuscript received June 27, 2008; revised September 10, 2008. First pub-lished February 03, 2009; current version published March 06, 2009. This workwas supported by the Hong Kong Polytechnic University under Project A-PG68.Paper no. TII-08-06-0083.R1.
C. H. Lo is with Hong Kong Community College, The Hong Kong Poly-technic University, Hung Hom, Kowloon, Hong Kong (e-mail: [email protected]).
E. H. K. Fung is with the Department of Mechanical Engineering, TheHong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong (e-mail:[email protected]).
Y. K. Wong is with the Department of Electrical Engineering, The HongKong Polytechnic University, Hung Hom, Kowloon, Hong Kong (e-mail: [email protected]).
Digital Object Identifier 10.1109/TII.2008.2008642
process unreliable and inflexible [7], [8]. Adaptive thresholdevaluation strategy is then proposed to improve the constantthreshold counterpart [9], [10]. However, it only overcomesthe problem to a certain extent and with great effort. Thesedisadvantages led to the development and increasing use ofalternative approaches: neural network, and knowledge-basedapproach.
Neural networks approach for fault diagnosis has been usedfor more than two decades since Venkatasubramanian andChan [11] proposed the use of neural network methodology forprocess fault diagnosis. Many revised approaches have beenproposed for different applications in the literature [12]–[14].Knowledge-based approach for fault diagnosis evaluates onlinemonitored data according to a set of rules which is learntfrom the experience of human expert. This approach reducesthe burdens on exact numeric information and automateshuman intelligence for process supervision [15]. As comparedto analytical redundancy (a model-based approach), knowl-edge-based approach is particular suitable for large, complex,and nonlinear systems since these systems are difficult to modelexactly and linear approximation may introduce large errors. Inaddition, knowledge-based approach is more robust, flexible,and intelligent than convention ones [16]–[18].
As to overcome the drawbacks of model-based approach,this paper focuses on combining the knowledge-based faultdiagnosis method with real-time residuals monitoring toimprove the efficiency and reliability of detecting differentactuator failures (e.g., evaluator failure, rudder failure, etc.)of an aircraft. The capability to inference results from inexactinformation makes fuzzy logic a suitable method for faultdiagnosis. Genetic algorithms (GA) are used to generate anoptimal fuzzy rule table since GA performs large search spacesof complex systems without having an exhaustive search. Thisfuzzy-genetic algorithm based automatic fault detection systemhas the advantages as other knowledge-based approaches.
The rest of the paper is organized as follows. Section II de-scribes the structure of the fuzzy-genetic algorithm based auto-matic fault detection system. An outline of GA as search engineto generate an optimal fuzzy rule table is given in Section III.Comparison of the proposed automatic fault detection systemwith linear classifier for detecting actuator failures of the non-linear F-16 model by simulation are reported and discussed inSection IV. Finally, Section V concludes the paper.
II. FUZZY-GENETIC ALGORITHM-BASED AUTOMATIC
FAULT DETECTION SYSTEM
The fuzzy-genetic algorithm has been widely used in manyother applications, such as synthesis of fuzzy control rules [19]and fuzzy classification rules [20], but has limited use in auto-matic fault detection. With the strengths of fuzzy reasoning andglobal optimization of genetic algorithms, the fuzzy-genetic al-gorithm based automated fault detection system is proposed to
LO et al.: INTELLIGENT AUTOMATIC FAULT DETECTION FOR ACTUATOR FAILURES IN AIRCRAFT 51
Fig. 1. Configuration of the proposed automatic fault detection system.
monitor continuously the dynamic behaviors of an aircraft. Itis capable of distinguishing four types of failures as no fault(normal), elevator failure, aileron failure and rudder failure. Theblock diagram illustrated in Fig. 1 shows the configuration ofthe proposed automatic fault detection system. Residual is com-puted as the difference between observable and nominal outputvariables. Fuzzy evaluation system is then used to evaluate theresidual and provides fault information to the pilot.
The fuzzy evaluation system is a logic decision-makingprocess that transforms quantitative knowledge into qualitativeconclusions (e.g., normal or elevator failure, etc.). It can also beinterpreted as a classifier to distinguish different failure statesfrom residuals. Fuzzy evaluation system provides a flexibleand accurate way to carry fault detection without any priorknowledge about the causes of faults. Furthermore, the rate offalse detection (or alarm) is lower than crisp threshold detectionsystem as the effects of modeling uncertainty and measurementnoise to the detection system is alleviated by fuzzy evaluationsystem.
The rationale of fuzzy evaluation system is first to fuzzifyresiduals and then evaluate them by an inference mechanismusing fuzzy if-then rules. Let fuzzy sets and
, represent each residual of observable outputs 1 , 2, and fault information, respectively. Then, the
th fuzzy rule in a fuzzy rule table (with fuzzy rules)can be written in the form
(1)
Finally, the fuzzy output of fault information from fuzzy evalua-tion system has to be converted into crisp sets (normal, elevatorfailure, aileron failure, or rudder aileron). Center-of-average de-fuzzification technique (2) is adopted to defuzzify for the crispfault information. This technique is the most commonly used asit is computationally simple and intuitively plausible
(2)
where is the number of rules being fired; is the fault infor-mation from the rule table, and is the degree of membership of
. The output in (2) is rounded off to an integer. Trapezoidal andsingleton fuzzy-membership functions for residuals and fault in-formation are adopted, respectively.
The set of fuzzy if-then rules is often obtained by an expert’sknowledge and tuned manually, which result in an inaccurateand non-optimal rule set. Since the quality of the fuzzy-rule
table greatly affects the accuracy and performance of the clas-sification system, an effective approach has to be employed tooptimize the rule table. The ability to search for a solution glob-ally in a parallel fashion and the reduced probability of beingtrapped in local optimum make genetic algorithms a good op-tion to the task of fuzzy-rule optimization. In this paper, geneticalgorithm is used only to fuzzy-rule optimization.
III. GENERATION OF FUZZY RULE TABLE
BY GENETIC ALGORITHMS
GAs are search algorithm based on the mechanism of naturalselection and genetic reproduction [21]. GA is a global searcherthat performs large search spaces of complex systems withouthaving an exhaustive search. The construction of a GA to copewith fuzzy rules optimization consists of four major modules:initialization and encoding method, evaluation of fitness func-tion, reproduction, and generation selection.
A. Initialization and Encoding Method
The fuzzy rule table of the fuzzy evaluation system is codedinto a chromosome and integer number encoding method isadopted for easier understanding and manipulation. In thispaper, the fuzzy variable for fault information consists of fourfuzzy sets, (normal or no fault), (elevator failure),(aileron failure), (rudder failure) , that is coded as 0, 1, 2,and 3, respectively. Also, let each residual consists of five fuzzysets as NL (negative large), (negative small), ZE (zero),PS (positive small), and PB (positive big) . Assume thereare three residuals as input to the fuzzy evaluationsystem, the coding of the fuzzy rule table into a chromosome isillustrated in Fig. 2.
The length of a chromosome is equal to the size of the fuzzyrule table and each gene of a chromosome represents an outputof a fuzzy rule. Integer encoding method helps to reduce thelength of a chromosome, as the size of the rule table is goinglarge. Population size is problem specific and depends on thesize of the fuzzy rule table. It is chosen to be large enough topreserve diversity while small enough to reduce computationaltime (fast convergence). The initial population is generated ran-domly.
B. Evaluation of Fitness Function
Each chromosome is decoded into fuzzy rule table for thefuzzy evaluation system and is then given a fitness value, whichis a measure of its optimality with respect to an objective func-tion. A higher fitness value is awarded to a more optimal solu-tion. The choice of fitness function depends on the nature of thesearch and is problem specific. In our problem, distinguishingdifferent fault states correctly is the objective. The following fit-ness function is then formulated to determine the fitness valueof chromosomes:
(3)
where is the reference fault information from simulating dif-ferent fault situations with the nonlinear F-16 aircraft model,is the fault information computed by fuzzy evaluation system,
is the fuzzy rule table to be optimized, and is the size ofthe training data set. Once the fitness value of a chromosome is
52 IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, VOL. 5, NO. 1, FEBRUARY 2009
Fig. 2. Coding method applied in the optimization of fuzzy rule table.
equal to one or a predefined maximum number of generationsis reached, the searching of GA will be terminated.
C. Reproduction
Roulette wheel selection method is used to select chromo-somes for reproduction through randomly performed singlepoint crossover or mutation to generate offspring. Duringmutation, a single gene is selected randomly to either increaseor decrease by 1. Equation (4) is used as the parent selectionprobability function with population size, . In eachparent selection process, two different chromosomes are beingselected to generate their offspring according to the crossoverprobability and the mutation probability . The popu-lation sizes of both parent and offspring are kept constant
(4)
D. Generation Selection
Chromosomes with highest fitness values are retained in thenext generation, while those with the lowest fitness values arediscarded. Steady-state-without-duplicates (SSWOD) [21] isemployed to discard those identical offspring in the populationin order to ensure a maximum usage of the population.
TABLE ISETTINGS FOR THE PROPOSED AUTOMATIC FAULT DETECTION SYSTEM
IV. SIMULATION RESULTS
The performance of the proposed fuzzy-genetic algorithmbased automatic fault detection system is demonstrated throughthree simulation studies with different types of faults and mea-surement noise applied to the nonlinear F-16 aircraft model [22],[23]. Assume the aircraft is flying at 10 000 ft at a speed of 500ft/s. The roll rate , pitch rate , and yaw rate are theobservable outputs for the generation of residuals. A step func-tion is applied to the actuator control surface in order to simulateactuator failure. A linear classifier and neural network (ANN)are used to compare with our proposed system. The linear clas-sifier has the form, , where are con-stant coefficients which are obtained by the least square fit. Thearchitecture of the ANN is a three-layer, feedforward backprop-agation network which is trained by steepest descent and usesthe standard sigmoid activation function. The settings for theproposed automatic fault detection system are shown in Table I.
A. Elevator Failure
Failure of the elevator actuator is applied at 15 s. Fig. 3 showsthe residuals of , and , and the performance of the pro-posed fault detection system. After 0.2 s, the occurrence andtype of fault is detected and identified.
B. Rudder Failure
Consider failure of rudder actuator occurs at 15 s. Residualsof , and , and the performance of the proposed fault de-tection system is shown in Fig. 4. The fault is detected at 15.4 s.
C. Aileron Failure
Failure of the aileron actuator is applied at 15 s. Fig. 5 showsthe residuals of , and , and the performance of the pro-posed fault detection system. After 0.9 s, the occurrence andtype of fault is detected and identified.
D. Elevator Failure With Measurement Noise
Failure of the elevator actuator is applied at 15 s. Measure-ment noise (which is a white noise) is also incorporated to theobservable outputs, , and . Fig. 6 shows the residuals of
, and , and the performance of the proposed fault detec-tion system, respectively. The proposed fault detection system is
LO et al.: INTELLIGENT AUTOMATIC FAULT DETECTION FOR ACTUATOR FAILURES IN AIRCRAFT 53
Fig. 3. Proposed automatic fault detection system versus linear classifier and ANN: case of elevator failure.
Fig. 4. Proposed automatic fault detection system versus linear classifier and ANN: case of rudder failure.
capable to detect and distinguish the corresponding failure withthe presence of measurement noise at 15.4 s.
E. Discussion
Simulation results show that the proposed fuzzy-genetic al-gorithm based automatic fault detection system can detect andclassify different types of actuator failures accurately and ef-fectively. The resulted fuzzy decision surface of the proposedsystem showing the relation between residuals and fault type areshown in Fig. 7. From the simulation results, the linear classi-fier is unable to detect and distinguish different actuator failures.ANN shows comparable results with the proposed system, ex-cept a false alarm was detected for rudder failure (Fig. 4). Fluc-tuations between fault levels for ANN is observed during thedetection of elevator failure with measurement noise which in-
dicates the ANN may require further training or adaptation tothe new input data.
Unlike the fixed-threshold method using performance in-dexes [7] (e.g., IE, ISE, etc.), the proposed fault detectionsystem provides a flexible and “soft” classification of fail-ures. Thus, the difficulty in choosing appropriate performanceindexes and their limits for a dynamic system in order todistinguish clearly normal and faulty states is avoided. This canhelp in reduction the chance of false/missed alarm.
The characteristic of faults and measurement noise are twoimportant sources that may lengthen the time for detection andsometimes even affect the detectability of the automatic fault de-tection system. The “soft” classification of failures provided bythe fuzzy evaluation system minimizes the effect of measure-ment noise. At the same time, with different universe of dis-course used for residuals and (Table I), the coupling effect
54 IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, VOL. 5, NO. 1, FEBRUARY 2009
Fig. 5. Proposed automatic fault detection system versus linear classifier and ANN: case of aileron failure.
Fig. 6. Proposed automatic fault detection system versus linear classifier: case of elevator failure with measurement noise.
of aileron and rudder failures towards and can be allevi-ated. As for incipient faults, the gradual deterioration towards adynamic system may be masked by the controller and only bedetected when the faults have accumulated to a certain extend.The design of our proposed automatic fault detection system isfocused on detecting abrupt faults.
With the fuzzy-genetic algorithm approach, it is possibleto update the knowledge embedded in the fuzzy rule table ofthe detection system. This provides a means for the proposedsystem to be more “intelligent” by learning new knowledgefrom system data as time passes. Online generation of optimalfuzzy rule table can be achieved by the segmentation of fuzzyrule table. According to the training data, a segment of rule
table can be identified by the fired fuzzy rules at single timestep. Only this segment is coded and trained instead of the entirerule table, which in turn, reduce the search space and hencethe computational time. Extending the proposed automaticfault detection system to detect and distinguish multiple faultsrequires further investigations.
V. CONCLUSION
An automatic fault detection system based on fuzzy systemand genetic algorithms is proposed in this paper. With thestrengths of fuzzy reasoning and global optimization from GA,the fuzzy-genetic algorithm based automatic fault detectionsystem is a promising technique to perform automatic fault
LO et al.: INTELLIGENT AUTOMATIC FAULT DETECTION FOR ACTUATOR FAILURES IN AIRCRAFT 55
Fig. 7. Fuzzy decision surface showing the relation between inputs and output.
detection. Simulation studies have shown that the proposedsystem is capable of detecting the occurrence and distinguishingdifferent types of actuator failures.
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C. H. Lo (M’01) received the B.Eng. and Ph.D. de-grees in electrical engineering from the Hong KongPolytechnic University, Hong Kong.
He is currently a Lecturer in Hong Kong Com-munity College at The Hong Kong Polytechnic Uni-versity. His research interests include model-basedfault diagnosis, bond-graph theory, soft-computingmethods, automatic modeling of real-time systems,and qualitative reasoning.
Eric H. K. Fung received the B.Sc. and Ph.D. de-grees from the University of Hong Kong, Hong Kong,both in mechanical engineering.
He is currently an Associate Professor in theDepartment of Mechanical Engineering, The HongKong Polytechnic University. His research interestsinclude control system dynamics, automation, androbotics.
Y. K. Wong received the B.Sc. and M.Sc. degreesfrom the University of London, London, U.K., andthe Ph.D. degree from the Heriot-Watt University,Edinburgh, U.K.
He joined the Hong Kong Polytechnic University,Hong Kong, in 1980. His current research interestsinclude modeling, simulation, intelligent control, andaircraft control systems.
