Cost-Benefit Analysis and Specification of Component-level … · 2014. 9. 26. · Cost-Benefit...

Cost-Benefit Analysis and Specification of Component-level PHMSystems in Aircrafts

Alexander Kahlert1, Sebastian Giljohann2, and Uwe Klingauf3

1,3 Institute of Flight Systems and Automatic Control, Technische Universitat Darmstadt, Darmstadt, Hessen, 64287, [email protected]@fsr.tu-darmstadt.de

2 Lufthansa Technik, Frankfurt/M, Hessen, 60546, [email protected]

ABSTRACT

Unplanned aircraft groundtimes caused by component fail-ures create costs for the operator through delays and reducedaircraft availability. Unscheduled maintenance on the otherhand also creates costs for Maintenance, Repair and Over-haul (MRO) companies. The use of PHM is considered toimprove the planning of component-specific maintenance andthus reduces consequential costs of unscheduled events onboth sides.

This study assesses the component-specific costs and charac-teristics of today’s maintenance approach. A discrete eventsimulation represents all relevant aircraft maintenance pro-cesses and dependencies. For this purpose the Event-drivenProcess Chain (EPC) method and Matlab/SimEvents are used.The data input (process information, empirical data) is pro-vided by a particular MRO company.

Whereas recent approaches deal with stochastically processeddata only, e.g. failure probabilities, the proposed methodmainly uses deterministic data. Empirical data, representingparticular dependencies, describes all relevant stages in thecomponent lifecycle. This includes operation, line and com-ponent maintenance, troubleshooting, planning and logistics.

By simulating different scenarios, various maintenance futurestates can be evaluated by analysing effects on costs. The ob-tained economical and technical constraints allow to specifycomponent-level PHM design parameters, as minimum prog-nostic horizon or accuracy. Detailed process-specific infor-mation is provided as well, e.g. costs of non-productive MROactivities or no-fault-found (NFF) characteristics.

Alexander Kahlert et al. This is an open-access article distributed under theterms of the Creative Commons Attribution 3.0 United States License, whichpermits unrestricted use, distribution, and reproduction in any medium, pro-vided the original author and source are credited.

1. INTRODUCTION

Since the development in research fields as e.g. fuel efficiencyhas reached a point, where the savings potential is expected toadvance incrementally only, the concept of PHM offers newopportunities to improve competitiveness, see (Sun, ShengkuiZeng, Kang, & Pecht, 2010) and (Feldman, Jazouli, & Sand-born, 2009). By converting unplanned aircraft groundtimesinto planned maintenance tasks, it is considered to supportthe general objectives of aircraft maintenance. According to(Fromm, 2009) and (Knotts, 1999) these are:

• maximising aircraft availability and dispatch reliability• minimising consequential costs of technical delays• minimising direct maintenance costs (DMC)

The dispatch reliability (DR) describes the ratio of revenuedepartures without delays or cancellations compared to allflights. A higher DR results in a higher aircraft availabil-ity and thereby implies a reduction of delay compensationcosts (e.g. rescheduling costs, payoffs) as well as lower op-portunity costs through more revenue flights, see (Rodrigues,Balestrassi, Paiva, Garcia-Diaz, & Pontes, 2012) and (Sisk,1993). According to (Eurocontrol, 2010) average costs ofaircraft delays reach $113 per operating minute. Other re-sults are given in (Rodrigues et al., 2012), (Cook, Tanner,& Anderson, 2004) and (Fritzsche & Lasch, 2012). In 2008European airlines experienced 85 million delay minutes, cre-ating estimated overall costs of $9.7 billion, see (Eurocontrol,2011). According to (Eurocontrol, 2012) technically induceddelays account for a large portion of all delays. Since PHMallows to perform maintenance tasks within planned main-tenance events prior to a component failure, technical delaycosts are one measured variable in this study. The schedulingis based on remaining useful life (RUL) prediction, e.g. see(Holzel, Schilling, Neuheuser, Gollnick, & Lufthansa Tech-nik AG, 2012). Within a prognostic horizon (PH) the future

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system state can be predicted with sufficient accuracy.

Reducing the DMC, being part of the direct operating costs(DOC), is another key factor in competition. According to(Fromm, 2009) DMC account for approximately 7% of theDOC of an airline. (Fritzsche & Lasch, 2012) state that PHMenables a MRO company to optimise the maintenance pro-gram’s scheduling as well as structuring. This allows more ef-ficient processes by converting unplanned into planned activ-ities and preventing non-productive events. Therefore avoid-able maintenance costs are another measured variable in thisstudy.

Since a PHM system is not ideal, it is characterised by uncer-tainties and involves various risks:

• The PH is too short and allows no useful forecast.• A low accuracy causes misinterpretation (NFF or unde-

tected events).• The PHM system itself fails (e.g. sensor failure).

In order to facilitate the planning of maintenance events, thePH has to allow forecasts for a certain number of flight cy-cles (FC) or flight hours (FH). For instance, if a componentmalfunction is indicated by a RUL prediction 5 minutes priorto failure, it may not be early enough in order to prevent agroundtime at the next station. On the other hand 5 minutesmight be enough to significantly improve operation in somecases (Sun et al., 2010). If the PHM system’s accuracy isnot sufficient, false conclusions are possible. Non-productiveNFF events may be generated by false alarms, or unscheduledevents by undetected failures, see (Knotts, 1999) and (Holzelet al., 2012). Furthermore, a PHM system involves require-ments concerning its own maintainability.

In summary, the goals of this study can be defined as follows:

1. Evaluate the financial potential of a component-specificPHM system.

2. Specify component-based PHM parameters.

The required component-specific PHM performance param-eters, as PH and accuracy, can be specified by the evaluatedeconomic constraints. These are gained from the calculationof a component-specific PHM system’s effect on

• delay compensation costs and• direct maintenance costs

with respect to the different PHM design parameters. Studiesanalysing similar goals often use simulation models. (Holzelet al., 2012) employ a model to carry out a cost-benefit anal-ysis by using failure probabilities as input data and evaluat-ing savings potentials of different PHM systems. An alterna-tive to the data input approach will be discussed in sections

2.2.1 and 2.2.2. Another similar procedure is presented in(Feldman et al., 2009). Key of this study is the Return onInvestment (ROI) calculation. Component failures are gener-ated probabilistically as well in this case.

Compared to the other studies, this paper presents a PHMevaluation using mostly deterministic data to simulate main-tenance events as close to reality as possible. This is enabledby available, adequate MRO data. The methodology, includ-ing assumptions and limitations, is discussed in the next chap-ter.

2. METHODOLOGY

This chapter provides an overview of the applied approach,illustrated in Figure 1. The major steps described in the nextsections are indicated by the labeled arrows: Data prepro-cessing, modelling and simulation. The boxes represent thein- and outputs, further explained in the particular sections.

Databases

Simulation

Model

Event-based

Data

Maintenance

Process Logic

Data Pre-

ProcessingModelling

Results

Simulation

Figure 1. Description of the approach.

2.1. Component-Level Approach

The component-level approach used in this study is explainedin the following.

2.1.1. Level of Detail

The introduced approach aims to evaluate the effects of aPHM system on component or line-replacable unit (LRU)level. LRUs are designed to be replaced quickly during turnaround times between two flights. Hence, faulty LRUs areresponsible for technical delays in many cases, because thereplacement requires a prior diagnosis as well as spare partsand often takes place during flight operation time.

An evaluation on a more detailed level is not conducted, dueto the fact that most available MRO data provides LRU spe-cific data only. In most cases LRUs can be found within anATA-6-digit chapter, specified as a subsystem. For further in-formation on the ATA-numbering system see (Air TransportAssociation of America, 2012). If an LRU is supplied by dif-ferent manufacturers or modified by minor updates, differentpartnumbers (PN) are applied.

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2.1.2. Sample Component Selection

The assumptions made in this study require the evaluatedLRUs to fulfill the following requirements:

• Standard LRU maintenance applies.

• LRU shows any sort of wear behaviour.

• LRU causes high costs through delays and cancellations.

• LRU causes high costs through NFF events.

It is assumed that all LRUs pass through a standardized LRUmaintenance process, which is the focus of this study. Thewear behaviour provides information about the technical fea-sibility of a prognosis application. In order to be able toperform prognosis an observable degradation process is nec-essary, whereas diagnosis requires the binary states ”func-tional” and ”not functional” only. LRUs can cause opera-tional delays through time-consuming replacements or trou-bleshooting (TS) tasks. Costs through NFF events can resultfrom insufficient fault interpretation and the conflicting goalsof different maintenance departments. Line maintenance atan airport aims to assure an aircraft’s availability by perform-ing all tasks as quickly as possible, e.g. by simply replacingan LRU in case of a fault indication, even if a detailed TSwas not conducted. The shop maintenance on the other handoverhauls all incoming LRUs. If a line maintenance replace-ment takes place without any exact finding, the subsequentshop maintenance event might be rated as NFF. This can beconsidered a non-productive maintenance action.

Besides the cost factors, the minimum equipment list (MEL)is considered for the LRU selection as well. A MEL categoryis specified by the corresponding rectification interval (RI) ofa component or its function. The RI shows how urgently aproblem has to be fixed in order to keep an aircraft realeasedto service. Thus, a failure’s priority and operational risk canbe described. Examples for MEL RI are given in Table 1.

Table 1. MEL rectification intervals.

MEL RI Time for rectificationA instantly or failure-specificB within 3 daysC within 10 daysD within 30 days

In order to select adequate LRUs for the study, prior to thesimulation all LRUs are ranked. Based on the available MROdata, a ranking as exemplarily shown in Table 2 for the LRUAir Data Inertial Reference Unit (ADIRU) is obtained. MROcomponent data from the years 2010 to 2013 is considered,providing estimated annual costs for delays and NFF eventsas well as the corresponding MEL RI categories for eachLRU. At the end of this study the exemplary results for theADIRU are discussed.

Table 2. Ranking of LRUs.

ATA LRU Delay costs NFF costs MEL RI34-12-34 ADIRU 1 CDelay CNFF A... ... ... ... ...

2.1.3. Component Maintenance

The LRU maintenance process can be described by the mainmodules shown in Figure 2. The interface between airlineoperation and the MRO involves the TS, the maintenanceplanning and the system maintenance. In the following theterm system describes the aircraft, consisting of subsystems,the LRUs. The TS mainly derives supporting and mainte-nance actions from fault isolation, e.g. by interpretation offault messages. The planning department concentrates on thetime scheduling of maintenance tasks considering priority, re-quired time as well as available ground times. The systemmaintenance consists of line and base maintenance. The sub-system (shop) maintenance, connected by the logistics, dealswith the overhaul of LRUs. Repaired components are sent toand taken from the spare parts inventory. Since the impact ofthis study on the spares inventory is insignificant, it will notbe subject in detail. Furthermore, information and materialflows are illustrated.

MRO

Airline

PlanningTrouble-

shooting

Information

LRU

Spares

Logistics

Operation

System-

Maintenance

Subsystem-

Maintenance

Figure 2. Modules of the component maintenance.

2.1.4. Non-routine Maintenance

The component maintenance can be subdivided into the fieldsroutine and non-routine maintenance. Routine tasks deal withmaintenance actions that are planned in advance. This appliesfor especially safety relevant items or consumables. Non-routine maintenance deals with unscheduled tasks, created byfaults of components that are maintained on-condition. Sincethe earlier mentioned approach includes on-condition main-tained LRUs only, this study focusses on non-routine mainte-nance. Furthermore, only maintenance events carried out atthe homebase are analysed.

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2.2. Discrete Event Approach

In a discrete event simulation (DES) state changes are onlymodelled at discrete time steps, called events. By skippingsimulation times without any changes, the approach is verycomputing time-efficient. States are defined by objects, re-ferred to as entities, and their attributes. Events are caused byattribute changes and the induced state transformations.

If a DES model uses non-probabilistic data only, it is calleddeterministic. Thus, all input variables are exactly definedand all states pre-determined. The use of a simulation modelthen primarily enables the computing of numerous operationsteps. If input data is probabilistically specified, a simula-tion model allows to consider stochastic input by conductinga Monte Carlo simulation. A set of simulation runs then en-ables the representation of distributed variables.

DES allows to analyse interdependencies between particularevents in detail, as described in (Rodrigues et al., 2012). Forinstance, information about failure message generation, LRUreplacements and aircraft delays can exactly be representedand correlations described. Whereas pure probabilistic ap-proaches mainly allow analysis concerning particular factors(consequence-wise analysis), an event-wise analysis providesinformation about specific causes and effects (see Figure 3).In this study both data input types, probabilistic distributionsand deterministic data, are used.

Failure

message

LRU

replacement

Aircraft

delays

LRUi

Event 1

...

Failure

message

LRU

replacement

Aircraft

delays

...

...

...

LRUi

Event n

Event-wise analysis

Consequence-wise analysis

Figure 3. Different analysis approaches.

2.2.1. Stochastic and Deterministic Data

If particular data is not described by a constant value, it isdistributed. According to (Kohn, 2005) probability densityfunctions (PDF) allow to describe the probability of a valueto apply. An example for uncertain data used in this studyis varying process time. Since in reality not every LRU re-placement needs the same amount of time, an analysis ofpast process durations provides statistical information on theempirical distribution. Figure 4 shows different PDF types.Depending on how accurate the empirical data is available,one of the introduced approaches is used. If only one scalarvalue is available, the special case deterministic distributionapplies. This is the case for most input data in this study.

scalar value

(1 input value)

uniform

distribution

(2 input values)

triangular

distribution

(3 input values)

tmean tmin tmax tmeantmin tmaxt t t

P P P

Figure 4. Used probability density functions.

2.2.2. Component Failure Generation

As opposed to many other studies, as (Holzel et al., 2012) or(Feldman et al., 2009), the chosen approach defines compo-nent failures deterministically. Since empirical data regardingdate and time of a component failure or replacement is avail-able, all temporal information is inherited. Thereby differentanalysis scenarios all refer to the same initial failures as theroot cause for replacements and allow exact comparisons.

2.2.3. Process Definition

In order to acquire knowledge about the overall maintenanceprocess, a conducted process analysis provides informationabout the following process factors:

• work type (information-/LRU-processing)• process time (minimum/average/maximum)• number of required personnel• qualification of required personnel• required resources (e.g. hangar)

By mapping the process sequence including conjunctions, theprocess interdependencies are represented (see Section 2.4).Whereas the information on process sequence and personnelrequirements is derived from MRO documents, the processtimes of LRU-specific processes are specified by maintenanceexperts and historical data. Concerning process resourcesonly the demands are modelled as opposed to available ca-pacities.

2.3. Data Acquisition and Preprocessing

The data preprocessing provides the event-based data inputfor the simulation. It is described in the following sections.

2.3.1. Input Data

Input data for the simulation is derived from various MROdatabases. Flight log databases provide information about theflight schedule, ground events and operational irregularities.Fleet databases contain registration-specific information. Avariety of technical logbooks provide data about failure mes-sages, the maintenance history (reports and actions) and lo-gistics. Experts contributed process-specific details.

All databases contain data sets that are exactly defined by theattributes aircraft registration, LRU part- and serialnumber,

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date, time and location. According to the logic introducedin the next section, corresponding data sets from differentdatabases are connected to single events.

2.3.2. Event Definition

An LRU replacement event is specified by data from the afore-mentioned databases. In order to identify and extract dataevent-wise, the linking logic, shown in Figure 5, is applied.(Beynon-Davies, 2004) further discusses data models.

LRU Replacements

Aircraft registration

LRU registration

Date and time

Removal information

Fleet Information


Specific functions

Flight Irregularities


Date and time

Delaycode/-time

Ground Times


Date and time

Location

Ground event type &

duration

Logistics History

LRU registration

Date and time

Shop History

LRU registration

Date and time

Reports and actions

NFF information

Logbooks


Date and time

Location

Reports and actions

Failure Messages


Date and time

Flight phase

Failure message Flight Schedule


Date and time

Location1 1 1 1

0..n

0..n

0..1

0..1

1

1..n

0..n

0..n

1 1

1

1

Figure 5. Relational object data model for an event definition.

As shown in the data model, an LRU replacement data setentry is the basis for an event definition. Based on the avail-able attributes, all other databases are connected by linkingparameters, e.g. aircraft registration and date. As indicatedby the data model, several conjunction types are used. Theconnection of multiple data sets is possible (n) as well as sin-gle data entries or no data at all (1 or 0). By matching allrelevant data, unique subsets specifying separate events aredefined. Matching conflicts, redundancies or incomplete datais accounted for by robust merging, either correcting or skip-ping the particular data set. Insufficient data quality is a ma-jor limitation in this study. Therefore only reliably definedreplacement events are considered for the evaluation.

The data is organised in the structure shown in Figure 6. Dif-ferent hierarchy levels are used in order to classify similarinformation. Thereby results can later be analysed concern-ing particular characteristics, e.g. comparing all events of kdifferent partnumbers for one LRU.

LRU1Partnumber1

Input

DataLRUi

Partnumberk

Event1

Eventn

Failure Messages

Ground Times... ... ...

...

Figure 6. Hierarchy levels of the obtained data structure.

2.4. Modelling

The following sections explain the model building.

2.4.1. Process Modelling

The EPC method is used for the logical maintenance pro-cess modelling. It comprises the elements process, event andBoolean operators (AND, OR, XOR). A process, illustratedby a rectangle, is defined by the aforementioned process fac-tors. An event, displayed as a hexagon, defines the state thatis supposed to be reached after a process completion. Thelogical operators, illustrated by circles, enable the modellingof intersections by defining routing conditions. Informationflow is indicated by dashed lines. Figure 7 shows an example:

Aircraft

available

Replace

LRU

Substitute

LRU

available

V LRU

replaced

Change

order

approved

Figure 7. Example of EPC modelling.

By using the EPC method all modules of the component main-tenance, shown in Figure 2, are described in detail. Due tointellectual property (IP) reasons, a detailed process map isnot presented in this paper.

2.4.2. Simulation Model

The EPC model is transferred to a software model using Mat-lab SimEvents, as applied in (Gray, 2007) or (Bender, Pin-combe, & Sherman, 2009). Matlab Stateflow is used to rep-resent the system (aircraft) and subsystem (LRU) states. Alldefined states are shown in Table 3 and Table 4.

Table 3. System states.

zSystem State description1 flight0 on ground, other station-1 maintenance, other station-2 on ground, homebase-3 unscheduled maintenance, homebase-4 available for maintenance, homebase-5 scheduled maintenance, homebase

An aircraft can only hold one particular system state at a time.Flight operation is represented by alternating system stateszSystem ∈ {−2, 0, 1}. Maintenance times are distinguishedbetween scheduled zSystem ∈ {−5,−4} and unscheduledevents zSystem ∈ {−3,−1}.

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Table 4. Subsystem states.

zSubsystem State description1 regular operation0 rectification in progress-1 maintenance required-2 deferred-3 deferrable

An LRU holds the states functioning zSubsystem = 1, in re-pair zSubsystem = 0 or not properly functioning zSubsystem ∈{−3,−2,−1}, further described by the urgency accountedfor by the MEL logic. Items that do not require immedi-ate rectification, can be deferred. By defining discrete statesand parameter dependent transitions, the toolbox allows toaccount for and evaluate different operating modes.

2.5. Simulation

The simulation characteristics are explained in the followingsections.

2.5.1. Simulation Time Characteristics

One simulation cycle represents all events within the analysedtime period for one aircraft at a time. This allows the evalua-tion of subsequent, interrelated events generated by differentLRUs on the same aircraft. Due to computing time issues andthe study objectives, only time frames of two weeks around anLRU replacement event are examined. Taking advantage ofDES all dates without any relevant occurrences are skipped.

2.5.2. Scenario-based Analysis

If the degree of particular process transformations throughPHM is supposed to be analysed, the definition of differentsimulation scenarios is useful. Defined scenarios are:

1. current state maintenance (data-based only)

2. best-case current state maintenance (data- / logic-based)

3. target state maintenance with PHM (data- / logic-based)

If the maintenance in its current state is to be analysed, thefirst scenario applies. In this case the simulation model di-rectly uses the data input in order to represent all actions andqueue times as they occurred in reality. The second scenarioaims at the representation of a best-case evaluation of today’smaintenance. The input data is used partially, e.g. date andtime of first failure occurrence. The missing information isthen generated by the modelled process logic. The third sce-nario is targeted on the evaluation of possible future stateswith PHM, by assessing the impacts of different prognosisparameters, as PH and accuracy. In this case only a smallamount of the historical input data is used, e.g. first occurenceof a failure message, in order to dissolve dependencies on to-

day’s procedure and to generate an ideal state case. The fur-ther rectification process is represented by the implementedprocess logic. By comparing the significant changes to pos-sible maintenance characteristics with PHM, today’s mainte-nance deficits can be analysed.

2.5.3. Monte Carlo Simulation

In order to account for input data provided as distributionfunctions, a Monte Carlo simulation carries out various sim-ulation runs. Based on the in section 2.2.1 described distri-butions, at each cycle the stochastically provided input datais randomly assigned, creating slightly differing simulationresults. This way especially the varying process times areaccounted for. By defining and saving seed values - initialvalues for random number generators - all Monte Carlo sim-ulation runs can be reproduced. The effects of the MonteCarlo simulation are considered in the model output interpre-tation by including the result’s distributions and illustratingparticular risks.

2.6. Target Values

The simulation results can be classified as process data andoperational aircraft data. The results interpretation covers thestatistical analysis of costs as well as raw, time-based sim-ulation data. Cost values are obtained from calculation oftime-based simulation data with available MRO cost rates.The simulations outputs are available on different levels ofdetail, allowing versatile result interpretation (see Figure 6).The different categories of target values are explained in thefollowing sections. (Linser, 2005) e.g. gives an overview ofother prevalent target values.

2.6.1. Costs

Cost analysis can be performed on all levels of detail. If de-sired, the IATA MRO cost structure, presented in (Fromm,2009) or (Linser, 2005), can be considered. Primarily the ap-proach determines costs for an event k according to the logicshown in eq. 1-3.

Event-based costs consist of process and operation irregular-ity expenses. Process costs are defined by labour, materialand overhead expenses. Operational charges arise from flatrates defining compensation and opportunity costs of delaysor Aircraft-on-Ground (AOG) times multiplied by the corre-sponding event duration.

CEventk =

m∑i=1

CProci +

n∑j=1

COpsj (1)

CProci = tLi· nLi

· cLi+ nMi

· cMi+ COi

(2)COpsj = tOj

· cOj(3)

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CEventk Total cost of event kCProci Cost of process iCOpsj Cost of operational irregularity jtLi

Process timenLi

Amount of labourcLi

Labour cost ratenMi

Amount of materialcMi Material cost rateCOi Overhead coststOj Irregularity durationcOj

Compensation cost rate

Future model updates will include ROI calculation, as de-scribed in (Feldman et al., 2009). This will enable the com-parison of different scenarios concerning PHM investmentsand avoided costs.

2.6.2. Process Characteristics

The simulation output directly provides process-specific in-formation, as time distributions and process sequences. Byevaluating the raw data, non-monetary target values can beanalysed. Some examples are:

• response and wait times• time savings through process transformations• process loops• bottlenecks

2.6.3. Additional Results

Examples for parameters, relevant for the MRO company andnot expressed as costs or process times, are:

• aircraft dispatch reliability and availability• delay characteristics• NFF characteristics• effectiveness of actions• real-time data transmission benefit

Regarding a PHM design the following prognosis parametersare evaluated:

• minimum required PH• minimum required prognosis accuracy

As explained in the introduction, these parameters will par-tially be based on cost factors. Statistical values as MeanTime Between Repair (MTBR) are not evaluated in this study,because the results will not have any impact on these param-eters. For further information see e.g. (Saxena et al., 2008).

3. MODEL APPLICATION

In this section the results of an exemplary simulation modelapplication are summarised. Due to IP reasons a detailed de-

scription of the maintenance process logic as well as partic-ular process factors are not presented. Regarding the scenar-ios, introduced in section 2.5.2, the analysis represents dataobtained from scenario 1. Results of the other scenarios arenot presented in this paper due to IP reasons and model mod-ifications not implemented yet.

3.1. Numerical Example

The conducted test run presents LRU-specific data for theADIRU using the Lufthansa Airbus A320 fleet. The MROdata provides complete information for the ADIRU from theyears 2010 to 2013. 294 exemplary replacement events at thehomebase are generated. Since the LRU is not maintainedperiodically, all replacements are unscheduled.

According to redundancy requirements each aircraft has threeADIRUs. ADIRU 1 is classified as particularly critical (MELRI A). Regarding the examined fleet, four modifications (part-numbers) of the ADIRU are currently in service (see Table 5).

Table 5. ADIRU-specific model input values.

Parameter Valuenumber of events 294installed ADIRUs per aircraft 3MEL RIADIRU 1 AMEL RIADIRU 2,3 Cdifferent ADIRU modifications (PNs) 4

General simulation input parameters are defined in Table 6.The labour cost rate is an average value for different em-ployee qualifications. In reality, different qualifications withvarying cost rates apply. An ADIRU replacement does notrequire any extra materials, thus not creating additional ma-terial costs. Logistics are considered as overhead costs.

Table 6. Simulation input values.

Parameter ValuenMonte Carlo Runs 250cL $200 per man hourcOpsDelay $82 per delay minutecOLogistics $100 per component

3.2. Input Data Analysis

Analysing the preprocessed data input without any simula-tion, provides information about LRU-specific maintenancecharacteristics, made available through the event-wise dataclustering. A target value, supposed to be reduced by PHM, isthe component’s NFF rate. The influence of particular eventcharacteristics on the NFF ratio is illustrated in Table 7. TheNFF rate provides information about the diagnosis accuracy.An ideal 100% accuracy is not realistic, since the aim of low-

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ering risks of false positive statements (NFF), falsely assum-ing an LRU is defective, is opposed to the aim to reduce falsenegative statements, falsely assuming an LRU is functioning.

It is shown that 35% of all replacement events are classifiedas NFF. Replacements involving AOG times (7%) show aslightly higher NFF ratio. As expected, cost-intensive ground-times as AOGs mainly cause quick part removals even with-out exact findings. Subsequent NFF findings in the subsys-tem maintenance then often occur. However, the sample sizeis low in this case and a direct correlation cannot reliably bestated. Replacements, that were deferred in the past (22%),show a higher NFF ratio as well. This behaviour is not ex-pected. A deferral should leave more time for troubleshoot-ing, thus improving diagnosis quality resulting in less NFFcases. The ability of an aircraft to provide fault messages inreal-time (RTS) (72% of the events) has no influence on theNFF ratio. Regarding the mounting location, the evaluationshows that the replacements are equally distributed over thedifferent ADIRU positions. If the ADIRU 1 is affected, theNFF rate is lower. Since the ADIRU 1 is more critical (MELRI A), this behaviour is contrary to the AOG results. On theother hand a higher components priority can lead to more pre-cise troubleshooting, eventually creating less NFF events.

Table 7. NFF analysis w.r.t. event characteristics.

Event characteristic nevents nNFFnNFFnevents

[%]

1. all events 294 103 35.02.a) AOG 21 13 61.92.b) no AOG 273 90 32.93.a) deferred 66 40 60.63.b) non-deferred 228 63 27.64.a) with RTS 211 74 35.14.b) without RTS 83 29 34.95.a) ADIRU 1 94 13 13.85.b) ADIRU 2 91 46 50.55.c) ADIRU 3 109 44 40.4

By analysing LRU-specific delay characteristics the effects ofa PHM system introduction can exactly be quantified. A de-lay analysis, concerning technically caused delays only, pro-vides the results shown in Table 8. 20.4% of the events gener-ated technically caused (primary) delays. The average delayduration is 18.1 minutes. Within subsequent flights furtherdelays (secondary) were generated. Their accumulated aver-age duration is 19.6 minutes. The results are relevant for thecost calculation in Section 3.3.3.

Analysing LRU data on an aircraft-based level provides in-formation about correlations between events (see Figure 8).For three exemplary aircrafts it is shown that ADIRU replace-ments occur w.r.t. all mounting positions. Table 7 also illus-trates the nearly equal distribution over all positions. A fur-

Table 8. Analysis of initial (primary) and subsequent (sec-ondary) delays.

Delay type ndelayndelay

nevents[%] tO,mean[min]

primary delay 60 20.4 18.1secondary delay 53 18.0 19.6

ther analysis shows that within the period of examination 131consecutive ADIRU replacements occur. Out of 131 events,59 replacements (45%) occur at the same mounting positionas the prior one, being slightly higher than the probability ofan equally distributed behaviour (33% for 3 mounting posi-tions). Probably not all replacements actually solved the rootcause of the problem.

2009 2010 2011 2012 2013123

AD

IRU

Pos

itio

nYear

Aircraft 1Aircraft 2Aircraft 3

Figure 8. Aircaft-specific failure sequence analysis w.r.t. theADIRU mounting positions.

3.3. Simulation Results

The following subsections deal with results obtained from thesimulation.

3.3.1. Simulation States

The system states (see Table 3 and 4) of an exemplary eventare illustrated in Figure 9. The subsystem state illustrates thepoint of time of failure (tSimulation = 0) and the further pro-cessing. The failure rectification, starting after the aircraft haslanded, is represented by zSubsystem = 0.

−5 −4 −3 −2 −1 0 1

z Syst

em

−139 0 78 159 301 439 590 718 1089−1 0 1

z Subs

yste

m

tsimulation

[min]

State

Figure 9. System and subsystem states of an exemplary event.

The plot primarily enables model validation by visualisationof the system states. It shows available maintenance timesas well as generated delays and rectification process charac-teristics. zSystem is a result of the flight plan and particularboundary conditions generated by maintenance actions. Theeffects on aircraft availability can be represented, if the entireflight operation is considered.

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3.3.2. Time-based Analysis

Analysing processes w.r.t. temporal data, provides informa-tion about particularly time-consuming or delay-causing pro-cesses and modules. Concerning the ADIRU, the overall pro-cess time from failure identification to rectification is repre-sented in Figure 10. The plot shows two distributions causedby different rectification procedures. If a failure occurs dur-ing flight operation and is classified as urgent, the rectificationusually takes place at the ramp immediately (left distribution,short duration). If the complaint is deferred, the rectificationis carried out in a hangar at the next planned plug (right dis-tribution, long duration). This usually involves higher main-tenance efforts, e.g. through detailed planning and repeatedtroubleshooting tasks and thus is more time-consuming. Forthe ADIRU the mean average is trectification = 71.7min.

0 50 100 1500

0.05

0.1

0.15

trectification

[min]

P(t

)

Overall rectification time

Figure 10. Overall processing time of ADIRU replacements.

Figure 11 shows ADIRU diagnosis process times. The meanaverage time is tdiagnosis = 37.6min. One aim of PHMis to consistently carry out system diagnosis prior to failuresin order to reduce replacement durations. Since the averagediagnosis time is almost half the average total rectificationtime, the effects on unscheduled groundtimes and delays areexpected to be significant.

0 50 100 1500

0.05

0.1

0.15

0.2

tdiagnosis

[min]

P(t

)

Diagnosis process time

Figure 11. ADIRU diagnosis process time.

If a failure requires specific action, the TS creates an ActionOrder (AO), a detailed task manual. The completion of re-placements with an AO requires more time in most cases, asconfirmed by the results shown in Figure 12. Since eventsinvolving AOs can be classified as special case treatment, theuse of PHM is expected to standardize the rectification and toreduce the number of AO processes.

Based on a detailed delay analysis w.r.t. process times, all

0 50 100 1500

0.05

0.1

0.15

trectification

[min]

P(t

)

Event with AOEvent without AO

Figure 12. ADIRU processing time w.r.t. AO characteristics.

events are categorized into four classes. 79.6% of the ADIRUreplacements do not generate any delays. 6.6% of the eventscause delays, but could have been prevented, if the diagnosisprocesses were carried out prior to the unscheduled ground-time. 13.6% of the events generated delays that could only beprevented by planning the replacement into a prior ground-time. 0.2% of the events would always cause delays, becausea unique ADIRU problem occured.

Based on the event characteristics of the second and third cat-egory (events with avoidable delays) the results shown in Fig-ure 13 and 14 can be obtained.

0 2 4 6 8 10 12 14 16 18 20 22 240

0.2

0.4

FC

P(F

C)

PH for delay avoidance

Figure 13. Required prognostic horizon for delay avoidanceas a function of flight cycles.

0 10 20 30 40 500

0.1

0.2

FH [h]

P(F

H)

PH for delay avoidance

Figure 14. Required prognostic horizon for delay avoidanceas a function of flight hours.

By means of the flight schedule and the calculated processtimes, the prior groundtime for every event, not generatinga delay, can be identified. The necessary time-shift to thatparticular groudtime can be specified in terms of FC or FH,illustrated as a PDF. Since only replacements at the homebaseare analysed in the first place, the FC analysis shows the ex-pected behaviour that only every second flight is accountedfor (groundtimes at the homebase). For instance, if an ide-ally working PHM system with a PH of 4 FC or 9 FH is used,

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60% of the delays could have been avoided completely. Addi-tionally the delays of other events could partially be reducedby scheduling them into more adequate groundtimes than theactual ones.

3.3.3. Cost-based Analysis

A cost-based analysis provides information about specific costdistributions. Table 9 gives an overview of the calculatedADIRU replacement costs. The average value for the annualcosts as well as the lower and upper boundaries of the con-fidence interval (CI), including 95% of the values, are given.Due to deterministic input data, for logistics overhead costsno CI applies.

The average overall costs for ADIRU replacements sum up to$125,365 per year. One event generates average total costs of$1,706. The uncertainty is described by the given CI, rang-ing from $269 to $4,419. Two thirds of the costs of an ordi-nary replacement event are generated by MRO processes, onethird by operational irregularities. The module-wise analysisshows that especially the maintenance modules and the logis-tics account for a large portion of the costs. A further anal-ysis determines the costs of NFF events (CSubsys.M.NFF

) asa fraction of the subsystem maintenance costs. The subsys-tem maintenance process is the costliest process, due to thefact that all on-aircraft ADIRU tasks are performed quickly,whereas a detailed component maintenance - the ADIRU isa computer - is time-consuming. Furthermore, the costs ofdiagnosis tasks (CDiagnosis) are analysed, being part of trou-bleshooting (CTS), system maintenance (CSys.M.) and sub-system maintenance costs (CSubsys.M.).

Table 9. ADIRU replacement cost analysis.

Cost type mean costs 95% CI mean costs[per event] [per event] [per year]

min - maxCEvent $1,706 $269 - $4,419 $125,365COps $593 $0 - $2,291 $43,558CProc $1,113 $269 - $2,524 $81,807CTS $35 $11 - $127 $2,597CPlanning $13 $8 - $22 $948CSys.M. $164 $112 - $207 $12,039CSubsys.M. $801 $0.4 - $1,859 $58,873CLogistics $100 $7,350CSubsys.M.NFF $183 $26 - $432 $11,282CLogisticsNFF $35 $2,573CDiagnosis $125 $59 - $348 $9,212

Out of the listed costs only some are avoidable (eq. 4). Theseare delay costs COps, costs of NFF events CSubsys.M.NFF

,logistics costs of NFF events CLogisticsNFF

and costs of di-agnosis processes CDiagnosis. The avoidable, annual costsreach Cavoidable = $66, 625 or 53.1% of the average overall

costs per year.

Cavoidable = COps+CSubs.NFF+CLog.NFF

+CDiag. (4)

3.3.4. Derivation of PHM Design Parameters

Based on the calculated operational and economic constraints,the benefit of particular PHM design parameters can be eval-uated. Figure 15 shows the impact of different PHM systemprognosis horizons, specified by the numbers of FH, and dif-ferent prognosis accuracies on the costs of operational irreg-ularities (COps). An imperfect system is accounted for by aconfidence value, representing a simplified accuracy. A con-fidence of 0.25 implies that 25% of the delay causing eventscould have been prevented by performing proactive mainte-nance. It is shown that an effective cost reduction requiresa reliable prognosis (high confidence) as well as a sufficientPH (high number of FH). A full reduction of delay costs isnot feasible because of few unavoidable major events withinthe evaluation period.

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 3010.000

20.000

30.000

40.000

50.000

FH [h]

CO

ps [

$]

PHMconf=1

PHMconf=0.75

PHMconf=0.5

PHMconf=0.25

Figure 15. Impact of different inaccurate PHM systems withvarying PH on costs of operational irregularities.

Some potential cost reductions are quantified in Table 10. Thereductions for realistic PHM systems (confidence < 1, shortPH) appear to be low. If the parameters of an exemplary PHMsystem are set to PHMconf = 0.5 and PH = 2 FH, the po-tential savings reach $987 per year only. If investment costsof PHM systems are considered, the cost-benefit might turnout negative in the end.

Table 10. Impact on costs of operational irregularities w.r.t.prognosis accuracy and horizon.

PHMconf 2 FH 5 FH 10 FH 20 FH0.25 -$494 -$1,756 -$4,768 -$6,0380.5 -$987 -$3,513 -$9,536 -$12,076

0.75 -$1,481 -$5,269 -$14,304 -$18,1141.0 -$1,974 -$7,025 -$19,072 -$24,152

Besides the impact on delay costs, the influence on MRO pro-cess costs is evaluated as well. Table 11 gives an overviewof potential savings concerning the aforementioned avoid-able cost categories. It is assumed that the PHM system’sconfidence allows to avoid the calculated costs proportion-ally. For instance, a PHM system with 50% accuracy enables

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the reduction of 50% avoidable costs, generating savings of$11,534 per year in this case.

Table 11. Impact of different inaccurate PHM systems onavoidable MRO process costs.

PHMconf CSub.NFF CLog.NFF CDiag.

∑0.25 -$2,821 -$643 -$2,303 -$5,7670.5 -$5,641 -$1,287 -$4,606 -$11,534

0.75 -$8,462 -$1,930 -$6,909 -$17,3001.0 -$11,282 -$2,573 -$9,212 -$23,067

The overall savings potential is illustrated in Figure 16. Itdepends on accuracy and PH of the PHM system. Whereasthe accuracy reduces costs in both categories, operational andMRO costs, a longer PH primarily allows to prevent moredelays. So the effects on process costs only depend on the ac-curacy. For instance, a realistic PHM system for the ADIRUwith 50% accuracy and PH = 2FH reduces the avoidablecosts to Cavoidable = $54, 104 per year, an annual reductionof $12,521 or 18.8%. Since no investment costs are consid-ered in this study, the savings potentials specify a boundaryfor reasonable PHM investment costs.

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

10.00020.00030.00040.00050.00060.00070.000

FH [h]

Cav

oida

ble [

$]

PHMconf=1

PHMconf=0.75

PHMconf=0.5

PHMconf=0.25

Figure 16. Impact of different inaccurate PHM systems withvarying PH on avoidable costs.

Since no prognosis algorithm performance data is availablefor this study, the effects of correlations between PH and ac-curacy are not represented. It is assumed that a shorter PHwill result in a higher prediction accuracy. By quantificationof the exact correlations, the analysis quality and the conclu-sions could be described more detailed in the future.

3.4. Model Validation

The model validation is carried out by conducting plausibil-ity checks. By comparing the simulated process sequenceswith the process analysis EPC model, the model logic is val-idated. A comparison of the simulated process time distribu-tions to the input distributions verifies correct data usage. Thesystem state diagram enables the validation of the interactionbetween flight operation and MRO processes. This way alsothe generation and recording of delay data can be confirmed.Further methods for model validation include Gantt charts for

visualisation as well as process route marking for plausibilitychecking.

4. CONCLUSION AND OUTLOOK

This study presents a new approach for the assessment ofPHM relevant components concerning avoidable costs of un-scheduled events. The aim is to evaluate the characteristicsof today’s maintenance on LRU level and to derive design in-formation for future PHM systems. Therefore, a DES modelis built up in order to represent the MRO process logic us-ing empirical maintenance data. After a data preprocessing iscarried out, a Monte Carlo simulation enables the representa-tion of uncertain parameters. Process times and costs of ex-emplary LRUs are calculated and analysed. Unique featuresof this study are the use of mostly deterministic data and theevent-discrete approach. Both procedures allow to evaluatedependencies, causes and effects within replacements events.

The results of an exemplary LRU, the ADIRU, show a de-cent savings potential. Operational irregularities and non-productive MRO processes cause $66, 625 avoidable costsper year. A sensitivity analysis of the impact of imperfectPHM systems on the aforementioned costs reveals that thebenefit largely depends on the prediction accuracy as wellas the PH. Whereas the PH allows to facilitate planning pro-cesses and thereby reduces delay costs, a PHM system’s ac-curacy mostly saves costs of non-productive MRO processesthrough improved diagnosis. Not considering PHM invest-ment costs, a realistic PHM system allows to save approxi-mately 20% of the annual costs for the entire fleet.

A final specification of a PHM system is enabled by a ROIcalculation, considering avoidable as well as investment costs,and an analysis of the correlation between prognosis accu-racy and horizon, providing prognosis algorithm performancecharacteristics. Future work will focus on the simulation oftarget state scenarios in order to evaluate the effects of differ-ent diagnosis and prognosis approaches in detail. Influentialparameters will be considered by performing further sensi-tivity analysis. The analysis of a large number of LRUs willfurther improve the understanding.

It is assumed that there is a standardized LRU maintenanceprocess and that the analysed LRUs show an observable wearbehaviour. LRUs that do not meet these requirements, are notapplicable for the simulation. Furthermore, the quality of thesimulation results largely depends on the input data quality,as inaccurate or conflicting data degrades the conclusions.

ACKNOWLEDGMENT

This study was carried out as part of a research project ofLufthansa Technik AG. The authors would like to thank theproject partners for the support and contributions to the re-search conducted in this paper.

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ABBREVIATIONS

ADIRU Air Data Inertial Reference UnitAO Action OrderAOG Aircraft on GroundATA Air Transport AssociationCI Confidence IntervalDES Discrete Event SimulationDMC Direct Maintenance CostsDOC Direct Operating CostsDR Dispatch ReliabilityEPC Event-driven Process ChainFC Flight CycleFH Flight HourIP Intellectual PropertyLRU Line replacable UnitMEL Minimum Equipment ListMRO Maintenance, Repair and OverhaulMTBR Mean Time Between RepairNFF No-Fault-FoundPDF Probability Density FunctionPH Prognostic HorizonPHM Prognostics and Health ManagementPN PartnumberRI Rectification IntervalROI Return on InvestmentRTS Real-Time-SendingRUL Remaining Useful LifeTS Troubleshooting

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