Knowledge-based Systems for Industrial Applications

Model-Based Systems & Qualitative Reasoning

Group of the Technical University of Munich SS 15 KBSIA - 56

Knowledge-based Systems for Industrial Applications

1 The Topic

2 Tasks

3 Modeling

4 Diagnosis

4.2 Component-oriented

Diagnosis

4.2.2 Vehicle diagnosis

Goal:

Prototype Applications

On-board diagnosis

Real-Time-Requirements

Ref: [Struss-Price 04]


Group of the Technical University of Munich - 57 SS 15 KBSIA

Diagnosis and Fault Analysis - Requirements

Variant problem

– versions of subsystems

Safety critical application

– completeness of results

Diagnostics during design

Representation and re-use of

knowledge

Increasing complexity of

systems

Stronger requirements

– Legal restrictions

– Customers



Diagnosis and Fault Analysis - The Opportunity

Knowledge-intensive tasks

require

knowledge-based systems

Computational power available

– During design

– In the workshop

– On-board

Computer support possible



Project Vehicle Model Based Diagnosis (2/97-1/99)

Università degli Studi di Torino



VMBD Guiding Applications: Drive Train

Exhaust

Control signal

Engine

Air supply Exhaust

system

Air

Torque

Fuel supply

Fuel Crankshaft

Combustion

Engine

ECU

Sensor values

Sensor

values Control

signal

Torque

converter

Belt Torque

Transmission

(Mechanics) Torque

Transmission

Control

(hydraulic)

Transmission

ECU

Sensor

values

Control

signal

Torque

CAN

Control

signal

Torque

Torque Torque

converter

Belt Torque

Transmission

(Mechanics) Torque

Transmission

Control

(hydraulic)

Transmission

ECU

Sensor

values

Control

signal

Torque

CAN

Control

signal

Control

signal



Demonstrator: Turbo Charger System

On-board detection and

localization of

faults related to black smoke

under realistic conditions

(e.g. sensors)

with model-based techniques

from Artificial Intelligence



Demonstration Turbo Control Subsystem

Scenario 2

Air flow sensor

Scenario 3

Boost pressure

Scenario 1

Leakage



Demonstrator Vehicle Set-Up

ECU ETK MAC 2

serial

line



The VMBD Demonstrator Cars



Demonstrator Car (Volvo) with RAZ’R



Demonstrator Car (Volvo) Switchboard for Fault Injection



RAZ’R Development System

Modeling

Primitives

Ontology

Definition

Model

Definition

Component

Types

Scenario

Definition

Structure

Observations

Model

Composition

System

Model

Diagnosis



Modeling

Primitives

Ontology

Definition

Model

Definition

Component

Types

Scenario

Definition

Structure

Observations

Model

Composition

System

Model

Diagnosis

RAZ’R Runtime System

Diagnosis

RTS

Observations System

Model

Modeling

Primitives

Model

Composition

Scenario

Definition

Model

Definition

Ontology

Definition

Structure Component

Types

VS100 Signal

Abstraction



Qualitative Modeling with Deviations

Deviations

Dx := xact - xref Model Fragments

[DQ1] [DQ2] = [0] Equations

Q1 + Q2 = 0

D(x + y) = Dx + Dy

D(x - y) = Dx - Dy

D(x * y) = xact * Dy + yact * Dx - Dx * Dy

D(x / y) = (yact * Dx - xact * Dy) / (yact * ( yact - Dy))

y = f(x) monotonic Dx = Dy

Reference can be unspecified!



Engine Model - Structure

Mechanics

Mechanics

Mechanics

...

Exhaust Gas

Combustion

Combustion

Combustion

Intake Air

Injector 1

Injector 2

Injector N

...

Fuel

Fuel

Fuel

Cra

nksh

aft

Load



Engine Model - Combustion (Partial)

fuel atomisation AF

fuel mass MF

air mass MA

air oxygen rate AO

Combustion

E combustion energy

EO exhaust oxygen rate

NO nitrogen oxides

EC carbon emissions

DAF DMF DMA DAO DE DEO DNO DEC

[0] [0] [0] [-] [-] [-] [-] [+]

[0] [0] [0] [+] [0] [+] [+] [0]

[0] [0] [-] [0] [-] [-] [0] [+]

[-] [0] [0] [0] [-] [+] [0] [+]

... ... ... ... ... ... ... ...



Significant Discrepancies ...

• ... are determined by

- effects on overall function

- violation of goals

- accuracy of measurements

- other components

• Hard to determine locally

• General problem of alarm thresholds

• Automated model abstraction

[Sachenbacher-Struss 05]

Induced Essential Distinctions Induced Essential Distinctions

Context

Task Scenarios

Structure Goals Observations



Engine Model: Caracteristic Map

• Numerical values

Which qualitative values necessary to diagnose “black smoke” ?



Automated Qualitative Model Abstraction

Target Distinctions

“black smoke” incomplete combustion

incomplete combustion: air/fuel ratio (l) below

stoichimetric condition ( 14.5 for diesel fuel)

i.e. target distinction: l 14.5 vs. l > 14.5

Observable Distinctions

Determined by sensors

Problem Size

number of variables: 146

number of components: 16

e.g. engine component: 1732 tuples



Demonstration Turbo Control Subsystem

Scenario 2

Air flow sensor

Scenario 3

Boost pressure

Scenario 1

Leakage

Scenario 1

Leakage



VMBD Demonstrator: Leakage in Air Intake



Diagnosis and Fault Analysis of Vehicles

Variant problem

– versions of subsystems

Safety critical application

– completeness of results

Diagnostics during design

Representation and re-use of

knowledge

Increasing complexity of

systems

Stronger requirements

– Legal restrictions

– Customers



Increasing Complexity ...

Source: Hoffmann et al. (DaimlerChrysler), VDI‘01



Increasing Complexity ......

Source: Hoffmann et al. (DaimlerChrysler), VDI‘01



Vehicles: A Mobile Hw/Sw Platform

VL381

SCU/SMLS

Gateway

Kombi

ESP

Airbag

EPB

Auxiliary

Heating

Simos 8.2 EDC17

MED9



Diagnosis Problems through Interacting ECU’s

Example (Renault):

The AC does not come on

Reason: defective tank level sensor!

Explanation:

- AC ECU send a request to Drive

Train ECU

- Drive Train ECU checks fuel level

- Defective tank level sensor

signals low fuel

- Drive Train ECU denies AC

request

Relevant

- to on-board diagnosis

- to diagnosability analysis during design


Group of the Technical University of Munich

Sensor

sensorsignal Tank Level

Sensor

sensorsignal Engine Temperature

Process Engine Temp

Function3in1out

int1erminal in2terminal in3terminal

outterminal

Process Cons Reqst

Sensor

sensorsignal AC Switch

ECUAirconditioning

inuserswitch incabintemp

outstart! inACenablreqst outenable

foutenablreqst finenable

outcabintemp

ECU2

Function3in2 out

in1term in2term in3term out1term

out2term Start AC

UserFeature actuated

AC on?

ECUCockpit

finlowtanklevel fintanklevel

outtankwarning outtanklevel

ECU3

UserFeature

actuated

Tank gauge OK?

UserFeature

actuated

Tank warning OK?

ECUPowerTrain

inenginetemp intanklevel

outenginetemp outtanklevel

inenableconsumer

foutenablcons

fouttanklevel

finconsreqst outconsreqst

inlowtank

foutlowtank

ECU1

Function1in1out

interminal

outterminal

Process Tank Level CAN Bus

SS 15 KBSIA - 82

Diagnosis Problems through Interacting ECU’s

Sensor

sensorsignal Cabin Temperature



Model-based On-board Diagnosis Demonstrator

../../../Presentations/Videos/stepx video.mpg



Failure Modes - CAN

ECU faults in communication

1. CANH or CANL open

(communication maintained)

2. CANH and CANL open

(no comminication)

CAN fault

1. CANH and CANL shorted

(communication maintained)

Diagnosis-ECU

ECU driver

CAN

ECU copilot

ECU rear right ECU rear left

Central ECU



Benchmark (Volkswagen): Comfort System

Log1

Motor Window

MStromMess1

0.0

1

HSS

Source12V-1

W19

Grd1

R35

Hall

2

Hall

1

W20

Node1

Node2

Node3

Grd2

Node6

Node4

Source12V-2



Model-based Diagnosis Runtime System on ECU

Model

RAZ‘R (OCC‘M Software GmbH):

• Code generator

• Infineon C167 (19.5 MHz)

• Comfort system, 4 door ECUs, CAN bus

• Real-time: 50 messages, 50 ms, < 20% of max.performance

• model + diagnosis engine + signal preprocessing: 25 kB

Model

Generation

Model

Lib

C-Code

for ECU

Diagnosis

Code-

Generator

Run Time

System Structure

Relevant faults

Mapping signals to model variables



Model-based Diagnosis Runtime System on ECU

On-board

Diagnosis C-Code

for ECU

Trouble Code

(Diagnoses)

(Raw)

Signals Further

Processing

Demo

Visualization


• Code generator


• Komfortsystem, 4 door ECUs, CAN bus




• Code generator


• Komfortsystem, 4 door ECUs, CAN bus



• Trouble code: set of faults

• Multiple faults (of arbitrary size)!





Model-based On-board Diagnosis Demonstrator

../../../Presentations/Videos/stepx video.mpg



IDD - Industrial Partners and Goals

Objectives

• Integration of diagnosis aspects

in the design process

Tools for developers of on-board

systems

CENTRO RICERCHE FIAT

OCC’M Software



Design of the

whole system

prototype

Design Loops

Outer design loop (component selection)

Specifications loop

Design of the ECU-based

control system and components

Inner design loop

Inner step n



Problems in the Inner Design Process

Observation:

• Weak interaction between development of control/FMEA/diagnostics

• Lack of powerful tools for fault analysis and diagnostics generation

• Feedback and alternative solutions lead to costly outer loops

Control Design (SW + HW)

Control Design Simulation/

Verification

Integration/

Verification ECU

SW +HW

Selection

of comps

and their

layout

Next

Prototype

FMEA Onboard Diagnosis Design

Algorithms Verification



Control Design (SW + HW)

Control Design Simulation/

Verification

Integration/

Verification ECU

SW +HW Next

Prototype

FMEA Onboard Diagnosis Design

Algorithms Verification

IDD‘s Answer

Observation:




Requirements:




Requirements:

• Immediate exchange of information through the model



Requirements:


• Speed up through automated FMEA and generation of diagnostics


Requirements:


• Speed up through automated FMEA and generation of diagnostics

• Exploration of several alternatives in parallel

Models

Selection

of comps

and their

layout

Selection

of comps

and their

layout



IDD Tools: Model Transformation

OBD

Generation Tool

Diagnosabilty

Analysis Tool

FMEA

Support Tool

Control

Generation Tool

Modeling and

Simulation Tool

Design

Tool

Models

Control

Generation Tool

Modeling and

Simulation Tool

Design

Tool

Qualitative

diagnostic model

Numerical model

(quantitative)

Model transformation

MATLAB/

SIMULINK

Model-based Core Components (RAZ‘R)

Documents

Knowledge-based Systems for Industrial Applications