Advanced Model-based Diagnosis of Internal Combustion …5...3.2 Common rail injection system and combustion 3.3 Turbocharger 4. Diagnosis of gasoline engines 4.1 Intake system 4.2

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2018

Advanced Model-based Diagnosis

of Internal Combustion Engines

- A short survey of research results -

Prof. Dr.-Ing. R. Isermann

Technische Universität Darmstadt

Institut für Automatisierungstechnik und Mechatronik

FG Regelungstechnik und Prozessautomatisierung

www.rtm.tu-darmstadt.de/rtp.html

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2018

1. Introduction

2. Fault detection and diagnosis methods

3. Diagnosis of diesel engines

31 Intake system

3.2 Common rail injection system and combustion

3.3 Turbocharger

4. Diagnosis of gasoline engines

4.1 Intake system

4.2 Fuel system (high pressure system)

4.3 Combustion

5. Conclusions

Advanced Model based Diagnosis of Internal

Combustion Engines

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2018

1. Introduction

• Increasing complexity of combustion engines:

• More components

• More sensors and actuators

• More control functions

• More sensitive combustion processes

→ Improved monitoring and fault diagnosis capability required

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2018

1. Introduction


• More components





• On-board diagnosis functions (OBD, since 1988)

• Driven by legislation • limited to emission related (large) deviations from normal behaviour

• Only signal changes and plausibility tests

• Off-board diagnosis systems in service-stations: • Workshop testers , connected to the OBD-plug

• allow more OEM-designed diagnosis functions (not all possible faults)

• Improvement of diagnosis functions should enable: • Detection and localisation of defective components

• Detection of faults: small, incipient, abrupt, intermittant

• Appropriate distribution of on-board and off-board diagnosis functions

• Predictive maintenance

• Appropriate reconfiguration to emergency operation

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2018

1. Introduction


• More components





• On-board diagnosis functions (OBD) • Driven by legislation

• limited to emission related (large) deviations from normal behaviour

• Only signal changes and plausibility tests

• Off-board diagnosis systems in service-stations: • Workshop testers connected to OBD plug

• allow more OEM designed diagnosis functions (do not diagnose all possible faults)

• Improvement of diagnosis functions should enable: • Detection and localisation of defective components

• Detection of faults: small, incipient, abrupt, intermittant

• Appropriate distribution of on-board and off-board diagnosis functions

• Appropriate reconfiguration to emergency operation

• Future aspects, challenges: • Maintenance on demand

• Remote (tele)-diagnosis during driving

• Reconfiguration to fault tolerant functions

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2018

1. Introduction



31 Intake system


3.3 Turbocharger


4.1 Intake system


4.3 Combustion

5. Conclusions


Combustion Engines

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2018

Fault Detection Methods

Model-Based

Methods

Analysis of Signal Models

Analysis of Process Models

Parameter Estimation

State Estimation

Parity Equations

Correlation Analysis

Spektral Analysis

Wavelet Analysis

Conventional

Methods

Limit /Trend. Checking

Plausibility Checks

Statistical Evaluation

Time Monitoring

OBD (if emission relevant)

2. Fault detection methods

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2018

Model-based fault detection and diagnosis

I) signal model-based fault detection II) process model-based fault detection

→ Use of single sensor signals only → Use of precise relations between

two or more sensor signals

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2018

Fa ult Diagnosis

- Fault Loc alization

- Fault Type

- Fault Size

- etc.

Diesel Engine

Actu-

ators

Sen-

sors

U Y

Faults

Faults

Faults N

Fault Detection

Fourier transformation, statistical features (standard deviation)Parameter estimationParity equationsState variable estimationNeural networks

Fault Diagnosis

Threshold SupervisionGeometric/statistical methodsFault-symptom-treesFuzzy-rulesNeuro-/Neuro-Fuzzy-approaches

Detec tion

of Changes

Fault Detection

Residual

GenerationNominal

Behavior

Features

Signal or Process

Models

Symptoms

Model based fault detection for combustion engines

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2018

1. Introduction



31 Intake system


3.3 Turbocharger


4.1 Intake system


4.3 Combustion

5. Conclusions


Combustion Engines

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2018

Engine testbench

Asynchronous machine 160kW

Automation with dSpace systems

>15 pressure and temperature sensors

Fuel flow measurement

Exhaust gas measurement AVL:

FTIR

MicroSoot

Opazimeter

SmokeMeter

NOx-sensor

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2018

Investigated diesel engines:

1) 2 l, 4 cyl, 74 kW, 230 Nm 2)1.9 l, 4 cyl, 110 kW, 315 Nm

Measurements at test bench:

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2018

SFA

actuator SFAuEngine

control

system

Injection

system

EGR

valve

ETC

actuator ETCu

iM

EB

EGRu

.vol efficiencySDetection

methods

Intake system

atTatp

2,Ip

2,IT

airm2,( )Iamplitude pS

2,( )Ephase pS

( )Lamplitude mS

( )Lphase mS

Bm

2On

,EGR EGRp T

oilT

wT

en

SFAu

SFA actuatorS

Com-

bustion

Exhaust

gas

recirculat.

boosttempS

Exhaust

turbine

Swirl

flaps

Intercooler

Turbo com-

pressor

Intake

pipes

Exhaust

pipes

Engine

mechanics

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2018

2,IT

2,Ip

,air sensorm

EGR-

Valve-

Fault

Compressor

Turbine power

Intercooler

Crank case vent

Air mass sensor

Air filter

Fresh air

Inlet manifold

Swirl port

Filling port

Manifold

temperature

sensor

Manifold

pressure

sensor

Restriction

Leakage

Built-in faults

Open

joining

Swirl flaps

actuator

Air path with possible, implemented faults

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2018

Volumetric

efficiency:

, ,

2,,

2,

1

2

air eng air eng

vIair th

eng D

I

m m

pmn V

R T

& &

&Distribution of measuring points across

the identified model

Identification with net model (LOLIMOT)

Continuous Function: 2,,v eng If n

Vo

lum

etr

ic e

ffic

ien

cy[-

]

Physical Modeling: ideal displacement pump

,air engm

2,Ip

3p2, IT

engn

Modeling

includes: • Dynamic flow effects

• Charge heating

• exhaust gas effects

Modeling of nonlinear intake behavior

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2018

0 180 360 540 720

1.7

1.8

1.9

2

Boost

pressure

[bar]

neng= 2000min-1

Crank angle [°CA]

10002000

30004000

11.5

22.5

0

0.05

0.1

0.15

Am

plit

ude

[ba

r]

10002000

30004000

1

2

3-20

0

20

40

Ph

ase

[°K

W]

2, 2, 2, 2,, cos 2 ,180

I I eng I eng IppAp p f n f nKW

Amplitude:

Phase:

LOLIMOT-

Modeling:

Modeling of crank angleModeling of Crank Angle Synchronous Boost Pressure Oscillation

Fourier analysis of crank angle synchronous boost pressure oscillation

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2018

2,,eng I vv vr f n

1) Residual Vol. Efficiency

2,,,eng Iair sensorm mmA Ar A f n

& &&

2,,,eng Im mair sensormr f n

& &&

4) Residual amplitude

air mass flow oscillation

5) Residual phase

air mass flow oscillation

2,2,,eng Ip pIpr f n

3) Residual phase

boost pressure oscillation

2) Residual amplitude

boost pressure oscillation

2,2,,eng Ip pIpA Ar A f n

Low pass(optional)

Ausblendung

falls Voraus-

setzungen

Jump-cut function, if

preconditions aren’t fullfilled

Dead-zone

Threshold value

Realprocess

Referencemodel

Inputvariables

Residual

meassuredprocess signal

or feature

Reference model signal

or featureDifference

Symptom

Calculation of fault residuals with parity equations

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2018

-0.1

0

0.1

0.2

0.3

0.4

-30

-20

-10

0

10

Time [sec]

0 50 100 150 200

-20

-10

0

10

20

Operating point: 2000min-1, 130Nm, boost pressure: 1.5bar, air mass flow 165kg/h

Restriction

between

intercooler and

engine

EGR valve open Removed tube of the

crank case vent Leakages after intercooler

5mm 4mm 7mm

Swirl flaps actuator

filling ports closed

Thresholds

of fault

detection

Results f Online Fault Detection at Steady Operating Point

[-]v

r

[mbar]pAr

[kg/h]mAr &

Residual deflections of the intake system for different faults

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2018

Faults Symptoms

Removed tube of thecrank case vent

+ o o o o

Leakage betweenintercooler and engine

- o o o o

Restriction betweenintercooler and engine

o - - + +

Swirl flaps actuator, fillingport is closed

o - - o o

EGR-valve: stuck at open ++ - - o o

Leaky EGR-valve + o o o o

mAS

& pASS

mS & p

S

Fault-Symptom-Table for Fault Diagnosis

Legend:

++ Symptom responds intense positive

+ Symptom responds positive

- Symptom responds negative

o Symptom does not respond

Symptom volumetric efficiency

Symptom amplitude air mass flow oscillation

Symptom amplitude boost pressure oscillation

Symptom phase air mass flow oscillation

Symptom phase boost pressure oscillation

mAS

&

pAS

mS &

pS

S

→ Different fault types can be isolated

Fault-symptom table for the intake system

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2018

1. Introduction



31 Intake system


3.3 Turbocharger


4.1 Intake system


4.3 Combustion

5. Conclusions


Combustion Engines

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2018

fuel filter

electric

fuel pump

high pressure pump

and metering valve

common rail pressure

control valve pressure

sensor

injectors

• manipulation current

of metering valve Imv

• engine speed neng

• crank angle φ

manipulation current of

pressure control valve Ipcv

common rail pressure pcr

components

5 measured variables

Opel 1.9 l DTH Z19

Investigation of a Common Rail Injection System (3 plungers, Bosch CP1H)

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2018

(1)

Mass balance of the common rail:

Characteristics of the Common Rail Pressure Sensor Signal

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2018

Mean Value Model of the Common Rail Pressure

Mean value over the period 2160°C (third cycle) averages the oscillations:

In the fault free case this mean value is a function of

• the orifice cross sections of metering valve and pressure control valve

• the engine speed

• the injector volume flow

The pressure gradient in the common rail follows the equation

Ecr: bulk modulus of the fuel in the common rail, Vcr: volume of the common rail

α: angle for which the computation

is performed

residual 1 “rail pressure mean value”:

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2018

Common rail pressure in a constant operation point is mainly influenced by

• the crank angle dependent injections

• crank angle synchronous fuel delivery of the high pressure pump

periodical oscillations of the rail pressure

Idealized illustration of the oscillations for a constant operation point

main injection cycle: τinj = 180°CA

τinj

period τ = 1/ Ω (angle frequency)

identical

volume flows

to the

injectors


second injection cycle (unbalanced injections): τbank = 720°CA

τinj

volume flows

to the

injectors

unbalanced τbank



main pump cycle: τpist = 180°CA

τpist

identical fuel

delivery of the

three pistons




second pump cycle (unbalanced fuel delivery): τhpp = 540°CA

τpist

unbalanced

fuel delivery

τhpp




second pump cycle (unbalanced fuel delivery): τhpp = 540°CA (3 pistons)

third injection and pump cycle through superposition: τsup = 2160°CA = 3x4x180°CA

τpist

unbalanced

injections and fuel

delivery

τhpp

τinj τbank τsup

τsup

Periods of rail pressure oscillations:

Characteristic of the Common Rail Pressure Sensor Signal

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2018

complex Fourier coefficients:

amplitudes:

(2)

(3)

↓ 540

Common Rail Pressure Signal Analysis – Fourier Amplitudes, normal behavior

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2018

pressure wave

resulting from

injection

↓ 720

Common Rail Pressure Signal Analysis – Fourier Amplitudes

(with unbalanced injections)

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2018

τpist τhpp

τinj τbank τsup

τsup

Rail pressure uniformity analysis

Periodic signal:

Uniformity analysis:

Task: injector or pump fault?

• Injectors: signal is periodic with 720°CA = 4·180°CA (second injection cycle)

•

• High pressure pump signal is periodic with 540°CA = 3·180°CA (3 pistons), (second pump cycle)

• Pressure measurements: filtered with a moving average filter over several periods (1440, 2160 oCA)

Uniformity analysis of rail pressure signal:

Ho

ch-

dru

ckp

um

pe

In

jekto

ren

Schw

ingungsanre

gung

du

rch

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2018

• residual 2 “unequal pump delivery”

• residual 3 “unequal injections”:

τpist τhpp

τinj τbank τsup

τsup

Rail pressure residuals

Periodic function:

Uniformity analysis: difference of phase

shifted signals

Uniformity analysis of rail pressure signal::

Ho

ch-

dru

ckp

um

pe

In

jekto

ren

Schw

ingungsanre

gung

du

rch

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2018

The variance is intended to detect faults in the low pressure part (influences all three

pistons equally). Depends on the

• rail pressure

• engine speed

• position of the metering valve

High Pressure Pump Volume Flow Analysis (no injection, overrun state)

Characterization of the mean pump flow oscillation over 3x180°CA = 540°CA by the

pressure signal’s variance:

(13)

residual 4 “high pressure pump volume flow”:

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2018

Rail pressure mean value

Raildruckmittelwert

Unequal fuel delivery

Gleiche Pumpmengen

Unequal injections

Gleiche Einspritzmengen

High pressure pump volume

flow

Hochdruck-Pumpen-

Volumenstrom

Residuals for different faults (sampling with 1 CA degree)

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2018

Faults Symptoms S*=flimit(r*

) Diagnosability

Sinj,1 Sinj,2 Sinj,3 Sinj,4 general special

F1: low delivery quantity of one pump piston 0 + 0 +

F2: reduced injection quantity of one injector 0 0 + 0

F3: pressure loss in front of high pressure pump

(e.g. a plugged fuel filter) - 0 0 +/- –

F4: pressure in front of high pressure pump too

high (e.g. a faulty metering valve) + 0 0 +/- –

F5: opening of the pressure control valve is too

large - 0 0 0

F6: opening of the pressure control valve is too

small + 0 + 0

F7: pressure sensor signal is too high + 0 0 - –

F8: pressure sensor signal is too low - 0 0 + –

general : fault is generally diagnosable (isolable)

special : fault is diagnosable, if several operation points are taken into account

Diagnosability: different patterns show:

Fault Symptom Table for the Common Rail System

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2018

1. Introduction



31 Intake system


3.3 Turbocharger


4.1 Intake system


4.3 Combustion

5. Conclusions


Combustion Engines

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2018

Investigated Faults

1: blowby tube

10,11: HPEGR Pos.

2: leakage

3,4,5: leakage

13,14: swirl flap pos.

6: restriction

Faults:

1. Blow-by tube

removed

2. Leak exh. 9mm

3. Leak. Int. 5mm

4. Leak. Int. 5mm

5. Leak. Int. max

6. Restriction int.

7. sVGT bl. open

8. sVGT bl. Mid

9. No fan airflow

intercooler

10. hpegr bl. mid

11. hpegr bl. closed

12. Air filter clogged

13. Swirl fl. pos. 0

14. Swirl fl. pos 75

15. Compr. blades

9: no fan airflow

12: air filter clogged

7,8: sVGT blocked

15: Compr. blades damaged

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2018

p3

T3

p4

p2c

p1

T1

Compressor

power PC

Compressor

torque

Friction

torque

Turbine

torque

Turbine

power PT

MC

MF

MT

2π/

Jtc1/s ntc

ntc

ntc

ntc

svgt

ntc

T3

T1

Turbocharger power model

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2018

• Compressor power:

• Turbine power:

• Friction power:

• Turbocharger speed:

1

* 2, 1

1

11

a

ac

c c p a

c

pP m c T

p

&

1

* 4, , 3

3

1

e

e

t t p e t aero

pP m c T

p

2 2(2 )f f tcP K n

21( )2

t c f

tc

tc tc

P P Pn

n J

&

Additional Models

.

Modeling of turbocharger power and speed

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2018

500 1000 1500 2000 25000

2000

4000

6000

8000

10000

12000

14000

16000

pow

er

in W

Pt

Pc

Pf

time in s

Simulation of turbine, compressor and friction power

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2018

generation of turbocharger residuals

Stationary

Reference Power

Models

Thermodynamic

turbocharger model

Measured model inputs

measured

outputs

Calculated model outputs

Calculated outputs

of power models

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2018

Faults Symptoms

Spt Spc Spf Sn Sp2 Sp3 Sp4

A

Air

Path

F1 Restriction air

filter

0 0 0 0 ─ 0 ─

F2 Blow-by tube

removed

0 0 ─ ─ ─ 0 ─

F3 Leakage

intake 5 mm

0 0 + + ─ ─ 0

F4 Leakage

intake 7 mm

+ + + + ─ ─ 0

F5 No cooling

airflow of the

intercooler

0 0 0 0 ─ 0 +

F6 Restriction

behind

intercooler

0 0 ─ ─ ─ 0 ─

B

Exhaust

path

F7 Leakage

exhaust

0 0 0 0 ─ 0 ─

F8 HP-EGR valve

blocked

closed

─ ─ ─ ─ ─ + 0

C

Turbo-

charger

F9 Compressor

blades

damaged

0 0 0 0 ─ 0 0

F10 VGT blocked

middle

position

+ + + + 0 0 ─

Fault-symptom table for the turbocharger, with air and exhaust path

for a medium operation region

Symptoms are changes of residuals which exceed thresholds

→The faults show different symptoms and can therefore be diagnosed, Sidorow et al. (2011)

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2018

Engine Control

Unit

Diagnosis

„engine“

Diagnosis

„exhaust“

Diagnosis

„cooling

&

lubrication“

Detection

„intake system“

Detection

„injection“

Detection

„combustion“

Detection

„exhaust

system“

intake flaps

throttle

injection system

valve train

actuator

ECU ActuatorsAuxiliary

unitsComponents Sensors

Detection

Modules

EGR valve(s)

turbine

actuator(s)

coolant valve

oil pump

actuator

fuel pump

tank ventilation

coolant pump

oil pump

intake system

air compressor

air cooler

combustion

engine

mechanics

EGR path(s)

exhaust gas

turbine

exhaust system

exhaust gas

aftertreatment

cooling system

lubrication

system

Detection

„exhaust

gas

aftertreatment“

Detection

„cooling“

Detection

„lubrication“

Diagnosis

Modules

1,, TTp aa

thflpair ssm ,,flpU

thU

egrU

vtrU

tU

cvU

oilpU

EE Tp 22 ,

egregregr Tps ,,

3,Tst

iSint,

icomS ,

icolS ,

Symptoms

iS lub,

coolcool Ts ,

oiloil pT ,

aDPFbDPFNO TTcx

,,

iinjS ,

iexhS ,

ieatS ,

fpU

injrail Ip ,

2,4 Ovp

cylp

injU

Modular structure for a model based overall fault diagnosis of diesel engines

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2018

1. Introduction



31 Intake system


3.3 Turbocharger


4.1 Intake system


4.3 Combustion

5. Conclusions


Combustion Engines

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2018

Used engine sensors (series production) and investigated faults

Goal: Diagnosis of faults,like:

1. Leakage intake system,

before throttle (after HFM)

2. Leakage intake system, after

throttle

3. Less fuel injection in one

cylinder

4. Increased EGR

5. Reduced ignition energy

1

2

4

5

3

VW FSI 1,6 l, 81 kW, 155 Nm

FVV-project, M. Leykauf

Oxycat NO-storagecat

(continous)

(combined)

dynamometer

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2018

1. Introduction



31 Intake system


3.3 Turbocharger


4.1 Intake system


4.3 Combustion

5. Conclusions


Combustion Engines

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2018

Diagnosis

Fault-

Symptom-

Table

Fault:

type

size

( ): not used for diagnosis

ECU

Throttle

Tumble

Camshaft

EGR-valve

Fuel System

High pressure

pump

Injectors

Control valve

u Throttle

u Tumble

(u ) CS

(t ) i

u CV

u EGR

Ignition system t ignition

Fault detection

Intake system

Symptoms Sensors

S Output Lambda

Controler

S Intake pressure

S Air mass flow

Input signals

Combustion

system with

operating

modes

Homogeneous

Stratified

Exhaust

system

Intake

system

m air

p 2

T 2

(T ) 3

(l ) after Cat

(NO ) x

p rail

n eng

m B

(l ) bef.Cat

.

SRail pressure

(in overrun state)

Fault detection

fuel and combustion

system

S Speed signal

S Rail pressure

(injection)

Modular structure of model based diagnosis for gasoline engines

Exhaust system

time synchr.

evaluation

T0 = 10ms

crankangle synchr.

evaluation

1°CA

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2018

Fault diagnosis with fault-symptom table (FSI)

+: positive symptom -: negative symptom o: no symptom change d: don‘t care

: applicable , : non applicable

(injection and ignition faults can be separated)

Symptoms S1 S2 S3 S4 S5 S6 Mode

Fault

Air

mass

flow

Manifold

press.

Output

Lambda

contr.

Rail

pressure

(overrun

state)

Rail

pressure

(injec-

tion)

Speed

signal

ampl.

ho

mo

ge

n.

stra

tified

Leak before throttle - o -

Leak after throttle (2mm) - + -

Leak after throttle (3mm) - ++ -

Increased EGR-mass flow

(with restriction) o + d

Less injection mass in one

cylinder d + -

Less pump fuel supply - o o () ()

Reduced ignition energy d o --

All investigated 6 faults are isolable

Nonlinear process model based Signal model based

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2018

Conclusions

• Combustion engines: Strongly nonlinear reciprocating multi-input multi-output processes (MIMO)

• Simplification for diagnosis by considering of engine modules with parallel multi-input single - output models (MISO)

• Fault detection by symptom generation with parity equations for output and input variables:

• Primary residual = measured output/input – reference model output/input

AND/OR

• Secondary residual = calculated feature – reference feature (amplitude, frequency,…)

• Reference models for normal behavior:

– Process models (Input/output-behavior) • Physical models

• Semi-physical models (physical based structure and identification)

• Identified input-output models (parameter estimation, local linear neural networks)

• Models used for calculating physical based features (amplitudes ,torques ,masses,...)

– Signal models (periodic): • Single frequencies: amplitudes, phases

• Multi frequencies: Correlation, Fourier analysis (FFT), Wavelet analysis

• Generation of many symptoms allows a large fault detection coverage and fault diagnosis

• Fault diagnosis: e.g. developed as fuzzy inference system

• Most important: Physical/engineering understanding, modeling and experiments

Conclusions

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2018

Future aspects, challenges:

Advanced fault detection and diagnosis is required for:

• Maintenance on demand

• Fault prediction

• Remote (tele)-diagnosis during driving

• Reconfiguration to fault tolerant functions

(In the context of automatic driving the powertrain may become a safety

relevant system)

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2018

R. Isermann

Combustion Engine

Diagnosis

Springer-Verlag, Heidelberg,

Berlin 2017

ATZ/MTZ Fachbuch

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2018

END

Documents

Advanced Model-based Diagnosis of Internal Combustion …5...3.2 Common rail injection system and combustion 3.3 Turbocharger 4. Diagnosis of gasoline engines 4.1 Intake system 4.2