The Use of Modelling within Prognostic Health Management Systems

for a Fowler Flap System

Graham Hardwick

and Isabella Panella

Chief of Systems - Performance, UTC Aerospace Systems, Stafford Road, Wolverhampton, U.K.

APS System Engineering Manager, UTC Aerospace Systems, Stafford Road, Wolverhampton, U.K.

Keywords: Prognostic, Health, Management, Commercial, Aircraft, Secondary, Flight, Control, Systems, Fowler, Flap,

System, Power, Drive, Unit.

Abstract: The aviation industry has been utilising prognostic health management (PHM) to improve scheduled

maintenance, reduce expensive aircraft on ground events (AOG) and improve active safety. PHM systems

utilise legacy and real time aircraft data in conjunction with simulation models to forecast the remaining

useful life (RUL) of components and systems which allow maintenance decisions to be managed. This work

presents an industry based approach to PHM for one of the aircraft line replacement units (LRUs),

specifically, the power drive unit (PDU) within a “generic” commercial aircraft secondary flight control

system. The modelling infrastructure and its importance as a building block for the construction of a

prognostic health management framework are highlighted. Example failure modes of a PDU are provided

and potential benefits of PHM to mitigate these failure modes are examined. Finally simulation results from

a physical model of the system in Simulink have been generated.

1 INTRODUCTION

In the aviation industry, there is an increased

pressure for airlines to reduce costs whilst increasing

their operating performance.

Airline costs are dependent on airline running

costs as well as the need for systems upgrades and

replacement, whilst guaranteeing their integrity

within legacy systems.

As reported by (A. Brüggen and L. Klose,

2010), the significant airline running costs that need

to be addressed can be grouped into three major

areas:

1. Cost of personnel, both ground and airborne

support crews, represented by personnel such

as, for instance, flight crews, pilots, engineers,

administrators, baggage handling ground staff;

2. The costs of marketing, business development,

and sales;

3. The technical costs of operating a fleet,

represented by:

a. Fuel;

b. General maintenance overheads (facilities,

maintenance administration and recording),

maintenance equipment such as spare parts

and oil, aircraft servicing such as de-icing,

electricity and water supply;

c. Traffic servicing, represented by landing,

taxi or parking charges and air traffic

control outlays.

d. Aircraft/capital as in depreciation of aircraft

or leasing costs, and other capital costs,

including outlays for working capital.

Specifically, when looking at the cost of

operating a fleet, AOG and its inability to be in

service represents the major cost for an airliner.

The AOG condition can be due to scheduled or

unscheduled maintenance.

The ability to forecast when LRU’s need to be

replaced or maintained, bundle multiple components

maintenance to minimise the number of AOGs

events, forecasting the probability of failure of a

given LRU to enable the airline to perform the

maintenance activities in their own hanger are

extremely important in the attempt to minimise the

overall fleet costs.

Moreover, current commercial aircraft and their

systems have changed dramatically since their first

introduction and the new civil aircraft market

requires increased cost saving and competitiveness,

whilst virtually every aircraft component that must

Hardwick, G. and Panella, I.

The Use of Modelling within Prognostic Health Management Systems for a Fowler Flap System.

DOI: 10.5220/0006900701510159

In Proceedings of 8th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2018), pages 151-159

ISBN: 978-989-758-323-0

151

be maintained has advanced during that time,

including flight systems, passenger comfort systems,

engines, and structural materials.

The issues of upgrading existing technologies in

a cost effective manner, of innovating current

technical solutions to ensure more cost effective

practices, the need for improved safety and

collaboration not just at airframe level but within a

collaborative air space and fleet management space,

lead to a revise approach on life-cycle management,

which requires to be more focused on data exchange

and real time information extraction.

2 INTEGRATED VEHICLE

HEALTH MANAGEMENT

A platform capable of achieving an integrated

operational information exchange for fleet

management and an individual platform capable of

addressing the ability to evaluate the RUL of a

systems, support fault detection, up to logistic

management of the fleet with the objective of

guaranteeing total asset availability has been

identified to be the integrated vehicle health

management system (IVHM).

The “health state” refers to the ability to

determine the overall health state of the vehicle. By

using diagnostic and prognostic algorithms, the

vehicle and its systems are monitored to detect and

isolate failures. This requires autonomous data

extraction from multiple heterogeneous systems, an

assessment of the impact that each fault can have on

an individual component and on the overall

platform, and the forecasting capabilities to identify

the timeline for the fault to become critical.

Mitigation involves the real-time assessment of

the impact of the failures on the vehicle and its

current mission. Once the impact is assessed, system

redundancy management reconfigures the vehicle to

maintain a safe operating condition and continue the

mission, if possible. In those cases in which

reconfiguration is not sufficient to continue the

mission, the flight crew may modify the mission

IVHM is seen as a key development in order to

reduce the lifecycle total ownership costs of modern

platforms to improve in-service operation. The

envisaged benefits of IVHM are:

• Ensure the ability to support fleet’s

overhauling management and

monitoring;

• Ease in integrating it to legacy systems

– integrated modular avionics (IMA),

full authority digital engine controls

(FADEC);

• Ease development of new cutting edge

technologies within the individual

subsystems

• Optimisation of network information

flow, by selection of data to transfer to

the networked elements.

Figure 1: Enterprise Wide Approach in IVHM (Spitzer, 2006, figure 22.2, pp 369).

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As described by (Baines, Benedetti, Greenough

and Lightfoot, 2009) IVHM is development of both

diagnostic and prognostic systems that when

implemented enables real-time, continuous

monitoring of vehicle health and predicts the

remaining useful life of a system.

Diagnostic is based on the evaluation of the

root causes of a fault that has occurred based on

historic database and can considered a deterministic

automated root-cause analysis carried out a

posteriori. Prognostic is the process of forecasting

future failure of the LRU or system based on historic

data combine with the real-time system

performance. (Kwok L. Tsui, Nan Chen, Qiang

Zhou, Yizhen Hai, 2015) define prognostics as “the

process of predicting the future reliability of a

product by assessing the extent of deviation or

degradation of the product from its expected normal

operating conditions”.

The set of hardware and software that enables

the support of prognostic infrastructures is also

known as the Prognostic Health Management

infrastructure.

In this work, the modelling and simulation

infrastructure required to develop PHM architecture

is described for the high lift system is described. The

model presented is a generic Fowler flap system and

the generic approach to a prognostic health

management framework detailed, from the fault

database management to the modelling and

simulation. A subset of failure modes of the PDU

failure mode examples are also discussed,

specifically the PDU brake failure and the PDU

filter blockage. PHM development opportunities in

those cases are explored.

3 HIGH LIFT SYSTEMS

UTC Aerospace Systems design, manufacture, and

integrate secondary flight control systems for a

variety of commercial aircraft, from wide body to

single aisle configuration, from business jets to the

A380.

Commercial aircraft utilize secondary flight

control surfaces, such as flaps and slats to modify

the wing profile in order to increase aerodynamic lift

for a given air speed. This allows aircraft landing

speeds/distances to be reduced.

At any given speed, the increase of lift from the

wing can be achieved by increasing:

a) The wing surface area, and/or

b) The lift coefficient

Figure 2: Slat and flap (Slat and Flap, https://

www.grc.nasa.gov/www/k-12/airplane/flap.html).

High-lift systems enable to increase the area of

the wing and to change the shape of the wing

aerofoil to support a change in lift coefficient. There

are two types of high-lift systems: the leading-edge

slats and the trailing edge flap (Figure 2).

Due to the complexity of the system and the

multitudes of mechanical, hydraulic, and electrical

components, which provide an example of complex

multi-physics system, it is essential to follow a

model based design (MBD) approach in its design

lifecycle.

The authors will present the step by step

implementation of the modelling and simulation

infrastructure and describe its adoption to support

the following analysis:

• Generate performance envelopes,

• Support a fault database management

system to support the listing of the

component failure effects / consequences,

• Generate dynamic load cases for a

component, sub-system, system,

• Determine the systems/components ability

to withstand load cases.

4 SIMULATION MODEL

DESCRIPTION

This section provides a description of the functional

architecture of the hydraulically powered Fowler

flap system and associated physical model. The

functional architecture is provided in figure 3 and is

based upon the work presented by (Hardwick and

Panella, 2017).

The Use of Modelling within Prognostic Health Management Systems for a Fowler Flap System

153

Figure 3 - Generic High-Lift architecture.

The functional architecture describes a generic

medium sized commercial aircraft flap system,

characterized by a single transmission line and

distributed actuators spaced symmetrically with

respect to the aircraft centreline. This architecture

presents only a flap system and does not include the

slat actuators.

The physical layout includes elements of the

functionalities that a high lift system needs to

present which are:

• Four mechanical rotary geared actuators

(RGA) per wing provide the actuation from the

transmission to the flap carriage; The

mechanical advantage from the transmission to

the flap carriage is increased by the use of

gearboxes. This allows the transmission to

drive large aerodynamic loads.

• The actuators are driven by a hydraulic Power

Drive Unit via transmission shafts which is

located on the aircraft centreline. The PDU has

two independant channels for redundancy and

position sensing capability via resolvers; This

communicates with the secondary flight

controller which controls the hydraulic valves

which regulate the flow to the hydraulic

motors. The motors drive a mechanical

gearbox that drives the transmission.

• The secondary flight control system

communicates with the PDU, position sensors

and safety devices which arrest the system

during failure case scenarios. It also interfaces

with the main aircraft flight controller.

• Synchronous movement of both wings is

achieved by using transmission shafts connect

the PDU to the actuators.

The sequence of operation of the system is as

follows. The flight controller provides a new

position command to the secondary flight controller.

The secondary flight controller compares the

position demand to the present PDU position. If the

error is above a set threshold then the controller

opens the hydraulic control valves and releases the

system brakes. The control for this simple example

is based upon position control. When the PDU

position reaches the demanded position the control

valves are closed and all brakes are engaged.

Figure 4: architecture of a generic secondary flight control system in Simulink – (Hardwick and Panella, 2017).

PDU &

Position

Sensing

Actuator 1 Actuator 1Actuator 2 Actuator 2

Actuator 3

Actuator 4

Flap Inboard Surface Flap Inboard Surface Flap Outboard SurfaceFlap Outboard Surface

Controller

Transmission

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Based on the functional description of the flap

system, the authors now intend to map the functional

representation into a physical model with the aim to

capture the physical behaviour of a PDU. To

represent this operational scenario, a first order

dynamic modelling of the secondary flight control

system was created. These equations were translated

into a state-space model within the Matlab /

Simulink modelling environment using a variable

step ordinary differential solver (ODE). The model

built captured the non-linear time invariant nature of

the system through its continuous states and the

representation of its non-linear behaviour. The

Simulink model employed in this paper is derived

from the work presented by (Hardwick and Panella,

2017). The following provide a brief summary.

Transmission shaft blocks connect the PDU to

the actuators which include component inertia,

stiffness, and damping. Efficiency of the universal

joints and inline gearboxes are included together

with rotational drag torque.

The PDU model incorporates two hydraulic

channels that contain a motor and control valve to

activate the motor. The PDU enable signal controls

both the brake in the PDU and the control valve. The

motors convert the hydraulic power into mechanical.

These both drive a gearbox which has a common

output shaft. The control valve dynamics in the PDU

was modelled using a first order transfer function.

Valve Transfer Function = 1/(1 + T

.(s)) (1)

Movement of the control valve determines the

pressure drop across (ΔP) the motor. The pressure

drop is converted to a motor torque (T

) by

multiplying by the motor displacement (K

mot

) and

incorporating drag (T

drag_m

) and motor efficiency

(η

mot)

as shown by equation 2:

= ΔP*K

*η

- T

dra

(2)

Hydraulic motor acceleration is calculated by

dividing the motor torque by the motor inertia.

Integrating the acceleration provides the angular

velocity of the motor. Both motor speeds are

transferred through a gearbox where the PDU output

shaft position and velocity states are passed to the

wing.

Figure 4 highlights the system architecture

mapped in the Simulink modelling environment.

This contains the following subsystems:

• “Controller” contains the Secondary flight

controller model;

• “Power Drive Unit” contains the power

drive unit mode as described in figure 5;

• “LH and RH Wing” contains the left and

right wing models;

• “Aero Loads and Hydraulics” blocks

represent the aerodynamic loads and

hydraulic system interfaces with the PDU.

The PDU and the controller are connected to the

rest of the model using “GoTo” blocks, described as:

• Ch1/2_Enable – Secondary flight

controller to PDU enable electrical signal.

• PDU_Position – PDU to secondary flight

controller position sensing

• Aero1(N) – Aerodynamic loads between

the interface and wings

• Psupply – Hydraulic supply pressure

between aircraft and PDU.

Figure 5: Power Drive Unit Model – (Hardwick and Panella, 2017).

The Use of Modelling within Prognostic Health Management Systems for a Fowler Flap System

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A “generic” high lift model has been developed

to protect intellectual property concerns for specific

customer programs. Therefore verification of this

model against physical test data model cannot be

presented. However, model verification has been

performed for customer programs at numerous

stages of the system engineering process for

example at component level and at full system rig

level. Excellent model correlation has been achieved

at both individual component and full system level

over a range of environmental temperatures,

aerodynamic loads across multiple programs.

5 POWER DRIVE UNIT FAILURE

MODES

In this section, the analysis of generic failure modes

for the Fowler flap system PDU is presented.

The rational of selecting the PDU as a case study

for fault analysis and as a LRU for future PHM work

is justified by the fact that any failure presented by

the PDU would present the following challenges:

• PDU repair or replacement with delays in the

aircraft future operations. Increase in AOG

time.

• Availability of spare parts depending on port in

which the aircraft is located. Dispatching of a

LRU to a given location would cause a

significant increase in the AOG time.

If PDU failures could be forecasted, the aircraft

could have been directed to a suitable maintenance

facility and PDU repair/replacement could take

place as part of a scheduled maintenance,

minimising the AOG time.

The first step in the design of a PHM is the

creation of a fault database, which support the

description of the component fault modes.

The fault modes are based on the operational

limits of the components and can be represented by

look up tables capturing the envelope of

performance of the LRU.

In order to understand the significance of the

fault, each fault mode is assigned a weight or

probability of its manifestation. This is evaluated

mathematically through fault tree analysis and

failure mode effects analysis (FMEA).

The probability distribution and the analysis of

the failure mode consequences/impact through a

sensitivity analysis of the failure modes provide the

foundation to create a contingency plan to mitigate

the risk of the fault realising.

Depending on the fault and its impact on the

system, different strategies to manage the health of

the system could be implemented, from design to

maintenance such as:

• Design upgrade;

• Additional redundant systems;

• Further safety monitoring systems to detect

issues if they cannot be mitigated;

• Inclusion of addition maintenance checks such

as a built in test (BIT).

Table 1 provides a subset of the failure modes

that can occur within the PDU and the system level

effects which have been simplified to aid

illustration.

The columns of the table represent the following:

1. Failure number – reference number to

support quick reference to fault case through

the paper.

2. Failure Location – the location of the fault is

referring to the Simulink model presented in

Figure 4.

3. Failure description – Brief description of the

fault.

4. Fault Monitor Triggered– Will an existing

system fault monitor identify the fault and

provide automatic corrective action (where

necessary) and annunciate the fault to the

support functions.

5. Failure Effect – Fault impact on the PDU.

6. System Issue - Fault impact on the System.

Failure numbers 1 and 3 indicate the complete

loss of hydraulic supply pressure and loss of PDU

brake capability. Both of these failures will trigger a

“system fault monitor” within the control system

which would detect the issue. For example failure

number 1: “Blockage of the hydraulic filter inside

the PDU channel 1” will cause the PDU channel 1 to

become stationary. The control system would

diagnose this via the motor velocity sensor

indicating that the motor is stationary while the

control system is commanding movement. If the

motor velocity signal remains zero for a predefined

time then a “channel jam” fault monitor would

trigger. This fault monitor would shut down the

affected channel and annunciate the condition to the

PHM system.

Failure number 3 “Complete loss of the Channel

1 PDU Brake” will prevent the PDU brake to

engage. The system would not arrest and move past

the demanded position as indicated by the PDU

position sensor. The control system would diagnose

this by triggering the uncommanded movement fault

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monitor. This fault monitor will automatically shut

down the system because uncommanded movement

of the system is hazardous to the aircraft. The

control system would also annunciate the condition

to the PHM system.

Failures 1 and 3 are also known as reactionary

failures, which imply that if they occurred, they

would automatically shut down the system/channel.

This would lead to the loss of operation the system

for the remainder of the flight. However, if these

failures could be anticipated via a prognostic system

then the remaining useful life predicted and

maintenance of the PDU could be scheduled at an

appropriate time.

A way to support a PHM implementation is to

add additional sensors dedicated to the monitoring of

the PDU and the development of further algorithms

within the control system.

For example the “Blockage of the hydraulic

filter inside the PDU channel 1” will likely be

preceded prior by “reduction of hydraulic flow due

to partial blockage inside the filter” which is failure

number 2. This failure would produce a reduction in

performance velocity/torque envelope of the PDU.

This would be monitored over time and change in

behaviour communicated to the PHM system which

would estimate remaining useful life and make the

appropriate maintenance decisions.

Similarly “Complete loss of PDU brake due to

wear” which is failure number 4 will likely be

preceded prior by failure number 3 “Partial loss of

PDU brake due to wear”. This failure would indicate

increased stopping distances when the brake is

commanded to engage. This stopping distance can

be tracked over time and communicated to the PHM

system which would determine the degradation rate

and plan appropriate maintenance decisions.

6 PHM APPLICATIONS TO THE

POWER DRIVE UNIT

This section will demonstrate how modelling and

simulation is used within PHM applications. The

example PDU failures as provided in table 1 will be

used as case studies. Firstly the approaches used to

simulate the failures will be provided. Then the

modelling results will be provided along with the

sensors required used to monitor the health condition

will be described and then insights into the

algorithms required will be provided.

6.1 Power Drive Unit Blockage

The first failure simulated will be the progressive

blockage of hydraulic supply line within the PDU

(failure 2 in table 1) which could be due to a

clogging filter. The blockage is parametrised in the

model by multiplying the PDU manifold effective

passage area by a constant. A constant value of one

indicates that the passage area in the model is not

changed and hence no blockage occurs. However, a

value of half indicates that only half the effective

passage area is available and therefore for a given

supply pressure and environmental temperature the

restriction will directly reduce the available flow

rate.

Table 1: Example of Flap System Failure Modes – PDU.

Failure

Location

Failure

Description

Fault Monitor

Triggered?

Failure Effect System Issue

PDU

channel 1

Blockage of

hydraulic filter

inside PDU

Yes – Channel

jam monitor

detects

PDU channel 1 would

not move

PDU channel 2 would continue

operation and hence system would

continue at half speed operation.

PDU

channel 1

Reduction of

hydraulic flow

due to partial

blockage inside

the filter

PDU channel 1 would

operate with reduced

performance

PDU channel 1 operates but with

reduced performance. PDU channel

2 would continue operation and

hence system would continue below

full operating performance.

PDU

Brake

Complete loss

of PDU brake

due to wear

Yes – System

uncommanded

movement

monitor

detects

PDU channel 1 brake

cannot engage

System would reach the target

position but then over run the

position due to no brake. The

uncommanded movement monitor

would detect and arrest the system

PDU

Brake

Partial loss of

PDU brake due

to wear

PDU brake will

function will have

reduced capability

System would reach the target

position and stop. Stopping distance

may marginally increase.

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157

Figure 6 provides the performance envelope of

the power drive unit of output torque vs output

velocity for levels of blockage in the PDU manifold.

As the quantity of blockage increases it can be seen

that the PDU output velocity decreases for a given

output torque. When the PDU has a maximum

blockage (value of zero) the PDU operates only on

one channel and the maximum velocity capability is

halved. It is noted that these curves are valid for a

given fluid temperature and supply pressure

available. If these parameters change then the

performance envelope will be modified.

This failure mode is similar to the oil/filter

blocking on aircraft engines as described by

(Bastard, Lacaille, Coupard and Stouky, 2016).

However, direct sensors of pressure drop are not

available hence simulation can be used to map these

performances into the secondary flight control

system that monitors the following sensors:

• Output rotational velocity

• Output torque

• Hydraulic fluid temperature

The PHM system would contain a real-time

simulation model that would provide the expected

level of performance of the power drive unit. This

model would input the PDU design parameters

temperatures and measured external torques. It

would compare the measured output velocity of the

PDU relative to the expected performance model,

based on test trends mapped against flight cycles.

The difference between the actual and simulated

velocity results would provide health monitoring

information and would assess the RUL of the PDU

status. Forecasting algorithms based on simulated

flight cycles would support decision making on

timelines for the PDU’s maintenance.

PHM in this example will anticipate when

degradation of performance occurs and will

intervene before the PDU channel becomes

completely non-operational, i.e. failure number 1.

This increases availability of the PDU channel and

allow maintenance to occur at suitable time.

6.2 Degradation of PDU Brake

The next case study illustrates how system

modelling can provide input into PHM systems via

the degradation of the PDU brake described by

failure 4 in table 1. The PDU brake arrests the

secondary flight control system when it reaches the

target position. However, as this is a mechanical

brake it may be subject to wear which may start

degrading the performance of the brake but not

produce a complete loss.

Figure 6: PDU output torque vs velocity for different

amounts of PDU blockage.

The model simulates degradation of the brake by

progressively reducing the torque capability from a

scale between 0 = most capable to 1 = least capable.

The slipped distance is the difference between the

final position of the PDU output shaft to the position

when the brake was commanded to engage and can

be sensed using the PDU position sensor.

Figure 7 provides the degradation in slipped

distance with respect to PDU brake capability for

this model and indicates as the brake capability is

reduced the slipped distance increases.

The PHM system could either contain a real-time

simulation model or simulation output results

mapped into a lookup table that would provide the

expected braking distance. It would compare the

measured slipped distance of the PDU relative to the

expected braking performances. The PHM system

difference would monitor the increase slipped

between the actual and simulated output would

assess the RUL of the PDU and provide decisions

regarding the maintenance of the PDU to occur at a

suitable time.

7 CONCLUSIONS

This work highlighted the major costs for a typical

airline where the AOG case is considered a

significant contributor to costs. IVHM and PHM

have been described within the commercial aircraft

business that are platforms that reduce AOG events

by utilising legacy and real time data in conjunction

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158

Figure 7: PDU Slipped Distance vs PDU Brake

Capability.

with simulation to predict the health of component

and subsystems. The paper provided an industry

example of how PHM can be applied to a generic

PDU within a secondary flight control system. The

system architecture was presented along with the

physical model within Simulink based upon

previous work presented by (Hardwick and Panella,

2017).

Two example failure modes of the PDU were

provided:

• PDU brake failure

• PDU filter blockage

These failure modes were used as example case

studies to demonstrate how modelling and

simulation can be used to generate data for the fault

monitoring and algorithms within the PHM process.

It is proposed that the simulation models that have

been verified using new product data should be

further developed to by verified utilising aircraft

life-cycle test data. Then PHM algorithms may be

developed using the modelling and simulation tools.

NOMENCLATURE

AOG - Aircraft on Ground

BIT - Built in Test

FADEC- Full Authority Digital Engine Controls

FMEA - Failure Mode Effects Analysis

LRU - Line Replacement Unit

IMA - Integrated Modular Avionics

IVHM - Integrated Vehicle Health Management

System

MBD - Model Based Design

ODE - Ordinary Differential Solver

PDU - Power Drive Unit

PHM - Prognostic Health Management

RGA - Rotary Geared Actuator

RUL - Remaining Useful Life

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