NAFIPS 2005 - 2005 Annual Meeting of the North American Fuzzy Information Processing Society

FUMSTM Artificial Intelligence TechnologiesIncluding Fuzzy Logic For Automatic Decision

MakingN. H. Wakefield

Research and Technology DevelopmentSmiths Aerospace, Electronic Systems - Southampton

Eastleigh, S053 4YG, UKNigel.Wakefield@smiths-aerospace.com

K. P. J. BryantResearch and Technology Development

Smiths Aerospace, Electronic Systems - SouthamptonEastleigh, S053 4YG, UK

Ken.Bryant@smiths-aerospace.com

Abstract - Advances in sensing technologies and aircraft dataacquisition systems have resulted in generating huge aircraft datasets, which can potentially offer significant improvements inaircraft Management, Affordability, Availability, Airworthinessand Performance (MAAAP). In order to realise these potentialbenefits, there is a growing need for automaticallytrending/mining these data and fusing the data into informationand decisions that can lead to MAAAP improvements. Smithshas worked closely with the UK Ministry of Defence (MOD) toevolve Flight and Usage Management Software (FUMSTm) toaddress this need. FUMST provides a single fusion and decisionsupport platform for helicopters, aeroplanes and engines.FUMST' tools have operated on existing aircraft data to providean affordable framework for developing and verifying diagnostic,prognostic and life management approaches. Whilst FUMSTMprovides automatic analysis and trend capabilities, it fuses theCondition Indicators (CIs) generated by aircraft Health andUsage Monitoring Systems (HUMS) into decisions that canincrease fault detection rates and reduce false alarm rates. Thispaper reports on a number of decision-making processesincluding logic, Bayesian belief networks and fuzzy logic. Theinvestigation presented in this paper has indicated that decision-making based on logic and fuzzy logic can offer verifiabletechniques. The paper also shows how Smiths has successfullyapplied Fuzzy Logic to the Chinook HUMS CIs. Fuzzy logic hasalso been applied to detect sensor problems causing long-termdata corruptions.

I. INTRODUCTION

The Smiths FUMSTM tools have operated on existingaircraft data to provide an affordable framework fordeveloping and verifying diagnostic, prognostic and lifemanagement approaches. In the context of MOD objectives,FUMST provides a ground-based Intelligent Management(IM) framework operating on existing MOD databases andaircraft data. It also provides a procurement risk controlframework to assist with verifying emerging PrognosticHealth Management (PHM) approaches using real data and to

P. R. KnightResearch and Technology Development

Smiths Aerospace, Electronic Systems - SouthamptonEastleigh, S053 4YG, UK

Peter.R.Knight(smiths-aerospace.com

H. AzzamResearch and Technology Development Director

Smiths Aerospace, Electronic Systems - SouthamptonEastleigh, S053 4YG, UK

Hesham.Azzam@smiths-aerospace.com

collect verification evidence for qualification and maintenancecredit purposes. The Smiths/MOD FUMSTm activities havebeen targeted at evolving an intelligent platform that willaddress a number of military needs:

A Needfor Advanced Diagnostics, Prognostics and LifeManagement: Diagnostics provides indications of faults frommeasured symptoms but, for almost all applications, does notindicate when the detected faults will lead to loss of aircraftfunctionality; a faulty component may still perform itsdesignated function, whilst a failed component will not.Prognostics anticipate component faults, and wheneverpossible detect them from measured symptoms, long beforefailure occurs. At the same time, prognostics predictremaining component lives and times to failure againstintended usage.

A Needfor Concise Prognostic Information (e.g. UsageIndices and Usage Spectra): Since proactive life and fleetmanagement systems should not only evaluate Low CycleFatigue (LCF) but also indicate how aircraft/enginecomponents have been used, Smiths demonstrated thefeasibility of generating usage spectra and Usage Indices (UIs)from recorded flight/engine data. The concept of UIs wasproposed and implemented to provide concise summaries ofrecorded flight data and, at the same time, indicate the impactof usage on component condition and life. It wasdemonstrated that the fatigue of engine and structuralcomponents could be accurately computed from UIs. The UIscould also summarise sensor data, strain data, vibration dataand any data derived from measured flight data, and thusprovide further prognostic information that could be used toevaluate the condition/life of additional aircraft subsystemsincluding electronic equipment.

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A Need for a Fusion, Mining and Automatic TrendingPlatform: Whilst the MOD maintenance/logistic systemscontain a wealth of data and information, there is a growinginterest in analysing and automatically trending/mining thedata/information within these systems to develop advanceddiagnostics and prognostics, extract new knowledge, andestablish enhanced maintenance guidelines and procedures.There is a need to efficiently review and extract the data andinformation, fuse them and combine them with experienceswithin the MOD centres of excellence. Advanced model-based tools, statistical analysis, powerful user-friendlyinterfaces and intelligent data management tools are requiredto address this need. Implementing such tools into each of theMOD logistic systems would require high upgrading costs andwould not address a need for a fusion platform external tothese individual ground-based systems. A cost-effectiveapproach is to develop FUMST software applications that canaccess the large volumes of data within the MOD databasesand, hence, aid the development of advanceddiagnostics/prognostics to exploit the wide range ofHUMS/FUMST capabilities.

A Need for an Expandable Verification Platform Openfor 3rd Party Tools: To avoid the risk of a long PHMevolutionary route, there is a need for a capability thatfacilitates the integration of technologies developed by variouscompanies and harmonises their information with theoperational in-frastructure. There is a need for a system thatcan allow not only Smiths but also Design Authorities (DAs)and other PHM developers to plug diagnostic/prognosticmodels into the system and make them available for militaryuse and for verification exercises.

A Needfor a Platform Providing Diverse Applications toa Wide Range of Users: Military personnel are supported by awide range of software systems. Whilst these systems usesimilar data types, the transfer of information/experiencesbetween them is often difficult and can be costly. For such alarge number of software systems, tasks such as obsolescencemanagement, mid-life upgrades and redesign to meetemerging requirements would not be straightforward. It istherefore desirable to develop a single platform that providesmultiple applications for different users and quick informationexchange between applications and users. FUMST has beendesigned to provide users having different needs withinformation consistent with their roles and experiences byoperating under a number of distinct user modes coveringapplications for 1 ' to 4th lines. Each application wouldaddress the needs of a group of users and could be easilytailored to specific requirements.

A Need for a Flexible Platform Providing MilitaryBenefits During Evolution: Whilst FUMST could be used todefine requirements for future systems and verify theirfunctions, FUMST applications could be prototyped andexploited to deliver near term military benefits. FUMSTMwould not offer all the promises of prognostics at the same

time; the improvements in aircraft MAAAP would bedelivered in a stepped manner. At each step, the MOD userswould benefit from some of the FUMSTm capabilities. EachFUMSTm capability would be evaluated, tested andsubstantiated with the user. A capability that wouldsuccessfully address a user need would be introduced in-service whilst maturing other capabilities. The approachfollows the guidelines of the MOD Smart Acquisitionprocurement process, which encourages use of best practiceand team working over the whole life cycle of a project withthe acquisition consisting of the following phases: concept,assessment, demonstration, manufacture, in-service anddisposal.

This paper concentrates on the fusion aspects of FUMSTand reports on a number of fusion and decision support toolsapplied to MOD aircraft data. The FUMST fusionapplications are composed of a range of conventional andadvanced signal processing tools. The tools include a suite ofstatistical analysis tools, cycle counting tools, Singular ValueDecomposition (SVD), Principal Component Analysis (PCA)and Artificial Neural Network (ANN) for non-linear multi-variant analysis. The fusion tools also include a suite ofArtificial Intelligence (Al) tools such as neural networks,cluster/novelty detection algorithms, Bayesian beliefnetworks, fuzzy logic, genetic algorithms and mathematicalnetworks. They include model-based damage algorithms basedon both safe-life and damage tolerant approaches. Whilst theverification aspects of various PHM tools were discussed in[1], this paper concentrates on fusion applications using fuzzylogic.

II. Fuzzy LOGIC FOR AUTOMATIC IDENTIFICATION OF GAUGEFAILURES

A. BackgroundThe strain gauge data corruptions observed in legacy

aircraft data could have occurred for a variety of reasons.Short terms failures in power supplies can cause sudden dropsin recorded strains to values close to zero. The strain voltagelevels are usually very small (millivolts), and, therefore, thestrain signals are amplified hundreds of times. The signalamplifiers can suffer failures leading to intermittent gainchanges and signal corruption. Whilst the strain system cablesare screened to prevent electromagnetic interference, operatingin high radio fields can cause signal corruption, especially foraging strain gauge systems. Dry joints and soldering that failto achieve perfect contacts can cause erratic strain behaviourcharacterised by the signals being stuck at wrong strain levelsfor periods of time. Changes in resistance due to ingress ofmoisture can cause corruption. Failures in temperaturecompensation mechanisms can lead to sensitivity to localtemperature changes and signal drifts. Strain gauge sensitivitycan be influenced by changes in bonding characteristics.Errors can also occur during strain signals multiplexing,synchronisation and recording. The corruption patterns of astrain gauge system are influenced by the system maintenancestatus; they can vary with time [2].

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Data corruption is not confined to strain data, all recordeddata including, for example, accelerometer data, controlsurface positions and airspeed, can be exposed to corruptionduring the lifetime of the aircraft.

Fig. 1 shows an example of long period corruption of astrain gauge fitted on a wing component, together with acoarse Smiths Mathematical Network (MN) prediction. Thestrain gauge suffered loss of sensitivity during the first 20minutes of the sortie. Later within the sortie, the strain gaugeand recording system started to operate normally. Fig. 2 showslong period corruption of a strain gauge located on the fin of alegacy aircraft. The central part of the strain time trace showscorruntion that has caused an offset in the recorded data.

Fig. 3 A schematic ofthe Smiths Mathematical Network

FM e. 1 -xam,le ofwinEnninnnent lnongeriM crrmntin.

The strain prediction and the recorded strain wereanalysed. Statistics such as regression analysis statistics andRoot Mean Square (RMS) errors indicated the fidelity of theprediction. A number of computed statistics were fuzzifiedinto "Expected", "Anomalous" and "Erroneous" sets; see Fig.4 for example.

Fig.2 Example of fin component long period conuption.

The examples of corruption above were identified usinggraphical comparisons with coarse Smiths MN predictions.The predictions were generated from other parameters. Thismethod would be prohibitively slow when applied tothousands of flights and more than 40 structural components.For this reason, an automated process was refined, whichemployed fuzzy logic to identify strain gauge corruption.

B. Automatic Identification ofGauge FailuresThe use of a strain gauge prediction is integral to the

process described in this section. The prediction is foundusing a Smiths MN. As shown in Fig. 3, flight parameterswere used as inputs to the MN. The data from a number offlights were used to calibrate a coarse network and obtain thenetwork coefficients.

The fidelity of the predicted component strains dependson the accuracy of the recorded flight parameters. Therefore,differences in the predicted and recorded strain can indicatecorruption in either the recorded strain or the flight parametersused in the prediction.

Fig. 4 Two regression metric fuzzification functions.

The fuzzification functions were defined using anarbitrary continuous function (X). The definition of eachfunction required a location (XEXP) and a width (SD), whichwere derived from engineering judgment and consideration offlights known to be free ofcorruption (1).

Expected= 1- A24) xFw-X l-))_1)4 (1)2xSD 9

The degrees of membership to the fuzzy sets of theconsidered statistics were combined using fuzzy rules such asthe one below:

EXPECTFDREGREss = EXPECTEDREoREssoCRREL AND EXPECTEDREGREss-RMSERROR AND EXPECTED.GREssnsLopE AND EXPECTEDECRuss-oFFsET

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Further rules were used to indicate whether one of the flightparameter sensors was faulty or the recorded strain wascorrupt.

C. ResultsThe fuzzy method described above allowed rapid

automatic analysis of selected aircraft structural componentstrains over thousands of flights. Fig. 5 and Fig. 6 showcorrectly identified periods of strain gauge corruption.

indicated that the discrepancy was caused by accelerometerfailure during this Deriod.

Fig. 7 Corrupt recorded accelerometer data.

Fig. 5 Corrupt recorded wing component stramin.

D. Bayesian NetworkAs an alternative decision-making process, a FUMS17

Bayesian network was configured to identify flights withsensor problems, Fig. 8. The nodes of the network changecolours from white to red for degraded sensors. For example,the central red node of Fig. 8 indicates that the similaritybetween predicted and recorded sensor data has degraded.The inputs to this node are found from regression analysis ofpredicted and recorded data. The lower red node indicates thatthe statistics of the recorded data are dissimilar to expectedvalues. In contrast, the lower pink node indicates that thestatistics of the predicted data are close to those expected.This information provides a strong indication that the recordeddata are corrupt, which is confirmed by the graphs shown in.U_h -4l I-A_o-A_tcirU.ofWiQ

Fig. 6 Corrupt recorded fin component strain.

The fuzzy method also identified periods of corruptionwithin flight parameter sensors. For example, Fig. 7 showsthe time traces of both the recorded and predicted wingcomponent strains and highlights a discrepancy between thestrains during a mid flight period. The fuzzy logic correctly

Fig. 8 FUMSM sensor health management using a Bayesian network.

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III. FUZZY LOGIC FOR INTELLIGENT MANAGEMENT OFHUMSCONDITION INDICATORS

A. BackgroundHelicopter HUMS generate large amounts of data that are

downloaded to ground-based systems. The data areautomatically examined on download for damage indications,which provides the immediate go/no-go response required bythe aircraft operations management. This level of reactivefault detection and diagnosis is reasonably well understoodand has been demonstrated to improve aircraft availability andairworthiness. In order to achieve further benefit andmaintenance cost savings from HUMS, another level ofanalysis is required, leading to prognostics and predictivemaintenance through IM of accumulated HUMS data. Incollaboration with the Civil Aviation Authority (CAA),Smiths demonstrated a suite of IM methods and successfullyapplied them to gearbox seeded-fault data [3]. Workingclosely with the UK MOD, Smiths have enhanced theirmethods and applied them to Chinook HUMS data. The resultis a high degree of early anomaly detection and a clear view ofthe deterioration to failure. The objective of the Smiths/MODprogramme has been to apply IM tools to the large volume ofHUMS data and, thereby, enabling improved analysiscapability, increased levels of automation and more intelligentuse of resources.

B. The Fuzzy RulesA variety of FUMST tools were configured to

intelligently manage Chinook HUMS drive train ConditionIndicators (CIs). The tools included: auditing to removeerroneous data; automatically generating thresholds andexceedences for both the whole fleet and individual aircraft;trending the data leading up to an exceedence and; clusteringto automatically identify normal and abnormal groups of CIs.The tools generated Health Judgments (HJs) using expertlogic. The HJs were fuzzified and fused using fuzzy logic tosupport maintenance decisions regarding the cause andseverity of HUMS wamings. For example, the HJs included:the percentage exceedence over the fleet and aircraftthresholds (0/oFleet_Ex and %AC_Ex respectively) and thenumber of successive exceedences over both the fleet andaircraft thresholds (NEXROWFT and NEXROWACrespectively). Fig. 9 shows the fuzzy sets defined for two HJsand the functions used to determine the degree of membershipof each set. The HJs fuzzy sets were fused together usingfuzzy rules to indicate the degree of membership tocomponent condition sets. For example, the following fuzzyrule was used to fuse the HJs and evaluate the degree ofmembership to a 'Component Failure Alert' set:

Component Alert = (PersistentAEmzowrr OR Persistentm!YoowAc) AND(Normalw,,, E. OR Normal s4c E ) AND PositiveGiWDsTAND PositiveGRADLT

AND (Abnormaic ,DwAEm OR Extreemecia,_DAEiA)To perform this rule in fuzzy logic, the OR function

becomes the maximum of the values (MAX) and the ANDfunction becomes the minimum ofthe values (MIN):

Component Alert = AiN(MAX(PersistentAEuowFr, PersistentAMowAC),M4X(NormajEs, Normalmc i,). PositiveGRADsT, PositiveGRADLT,

MX(Abnormalcnt,tDAEAm, ExtreemecI7.,,DAEMB))

A defuzzification rule was then used to give a percentageconfidence as to whether the alert is related to a componentfailure.

Fig. 9 Examples of fuzzy sets for HUMS Cls IM.

Further fuzzy rules were used to identify CIs that indicatepotential sensor failures or other data anomalies.

The decisions made using fuzzy logic allow maintenancepersonnel to identify an appropriate course of action.FUMSm users are presented with an overview ofthe warningsgenerated and have the ability to drilldown to see more detailsas required. Whilst Smiths configured FUMSm to provide anIM capability, it is worth mentioning that FUMSm can allowthe Smiths/MOD expert users to update/enhance the fuzzysets/rules in sympathy with emerging knowledge, and hence,achieve improved results.

C. Case Study: SNZA 709 Left Combiner BearingThe IM process was applied to a large number ofHUMS

CIs. This section presents one example of an alert generatedby this process. Fig. 10 shows the results ofthe automaticallygenerated thresholds, the threshold exceedences for both thewhole fleet and individual aircraft, the short and long termtrends of the data leading up to an exceedence, and the resultsof the clustering algorithm which automatically identifiednormal and abnormal CIs for the Left Combiner Bearing onaircraft SNZA709.

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Fig. 10 Case study - SNZA709 combiner bearing.

Table I shows a number of HJS and fuizzy set membershipvalues generated for a CI of an enveloped signal recorded on22/01/2003.

TABLE IEXAMPLE OF HJs AND Fuzzy SET MEMBERSHIP VALUES

Variable FHJ Value et Set MembershipNEXROWAC 4 Persistent 1.000

N_____ot-Persistent o0.000O/oAC_Ex 143.159 NoExceedence 0.000

Exceedence L.000ghtErro edence 0.0001E_ or0.000

The HJs membership values were then fused using thefuzzy rule that evaluated the degree of membership to the'Component Failure Alert' set giving a 100% componentfailure alert confidence. The MOD confirmed that thecombiner bearing on this aircraft failed on 23/01/2003 and wasreplaced [4].

CONCLUSIONS

The paper described some of the fusion aspects ofFUMST, which is evolving in a stepped, affordable manner asa ground-based PHM system that would deliver improvementsin aircraft MAAAP. At each evolution step, a number ofFUMST capabilities would be evaluated, tested anddemonstrated; a capability that would successfully address amilitary user need would be introduced in-service. In thisway, the user would benefit from introducing the fusioncapability whilst maturing other capabilities, and would beable to effectively manage the risks associated with PHM.

The paper concentrated on FUMSTm fusion techniquesusing fuzzy logic. The techniques were illustrated throughtwo applications: detection of strain gauge long periodcorruptions and supporting maintenance decisions usingHUMS CI data.

ACKNOWLEDGEMENTThis work would not have been realised without the

support of the Ministry of Defence. Acknowledgements areexpressed to AD Sys PSG Health Mr Stuart Driver who hassupported FUMST developments, and to DPA HUMS IPTCdr Mark Deaney, Mr Colin Wood and Mr Paul Harding.Acknowledgements are expressed to the teams of AD AIMand AD Sys PSG for their interest in this program and theirproactive support, valuable feedback and comments. Theauthors would also like to thank BAE SYSTEMS, Rolls-Royce, Westland Helicopters, Civil Aviation Authority andBristow Helicopters for their support at the various stages ofthe programme.

REFERENCES[1] Hesham Azzam, Jonathan Cook and Stuart Driver, "FUMSTm

Technologies for Verifiable Affordable Prognostics Health Management(PHM)," 2004 IEEE Aerospace Conference Proceedings, April 2004.

[2] G J Venn , "Information Received through Private Communication,".Westland Helicopters Limited, December 2003.

[3] Hesham Azzam and Neil Harrison, "A Demonstration of the Feasibilityand Performance of an Intelligent Management System Operating onHUMS In-Service Data," CAA Paper 99006, 1999.

[4] Dr N. Symonds PhD BEng(Hons), "Combiner Bearing Failure", ADAircraft Integrity Monitoring, Ministry of Defence, Fleetlands, Gosport,2003.

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