Diagnostics of mobile work machines

126 Pages·1.442 MB·English

by  Alanen J.

ESPOO 2006 VTT RESEARCH NOTES 2343
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2338 Martikainen, Antti. Ilmastonmuutoksen vaikutus sähköverkkoliiketoimintaan.
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2341 Kara, Mikko. Electricity and emission allowance markets from Finnish viewpoint. Diagnostics of mobile work machines
Stydy. 2006. 105 p.
2342 Häkkinen, Tarja&Wirtanen, Leif. Metlan Joensuun tutkimuskeskuksen ympäristö
ja elinkaarinäkökohtien arviointi. 2006. 29 s.
2343 Alanen, Jarmo, Haataja, Kari, Laurila, Otto, Peltola, Jukka & Aho, Isto. Diagnostics
of mobile work machines. 2006. 122 p.
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VTT TIEDOTTEITA (cid:150) RESEARCH NOTES 2343
Diagnostics of mobile work machines
Jarmo Alanen & Kari Haataja
VTT
Otto Laurila & Jukka Peltola
Helsinki University of Technology
Isto Aho
Plenware Oy
ISBN 951(cid:150)38(cid:150)6798(cid:150)6 (soft back ed.)
ISSN 1235(cid:150)0605 (soft back ed.)
ISBN 951(cid:150)38(cid:150)6799(cid:150)4 (URL: http://www.vtt.fi/publications/index.jsp)
ISSN 1455(cid:150)0865 (URL: http://www.vtt.fi/publications/index.jsp)
Copyright ' VTT Technical Research Centre of Finland 2006

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Technical editing Maini Manninen

Valopaino Oy, Helsinki 2006
Alanen, Jarmo, Haataja, Kari, Laurila, Otto, Peltola, Jukka & Aho, Isto. Diagnostics of mobile
work machines. Espoo 2006. VTT Tiedotteita (cid:150) Research Notes 2343. 122 p.
Keywords mobile work machines, diagnostics, automotive industry, cars, heavy-duty
vehicles, diagnostics architecture, MIMOSA, data analysis, data mining, sensor
fault modes
Abstract
In this research note, we take a look at the field of mobile work machine
diagnostics. The perspective by the authors is limited by the research project
(KODIE) behind this research. However, we have tried to provide generic
guidelines for machine builders to set up their diagnostics strategy. Building
blocks, like SAE J1939/73, ISO 15765 and ODX, from automotive industry are
exhibited to prevent machine manufactures from reinventing diagnostics
protocols and practices. Furthermore, examples of diagnostics architectures are
presented, with OSA-CBM among others. To make the most of diagnostics data,
an extensive set of data analysis methods are introduced. And in order to help
engineers to design diagnostics feature for the sensor system, hints and examples
are supplied as to how to establish the fault modes of sensors; a good knowledge
about the (relevant) sensor and actuator fault modes is a prerequisite for
comprehensive fault detection.
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Preface
This report is an outcome of the national KODIE project (Diagnostics and
remote control of machines) carried out through 2003(cid:150)2005. The project
belonged to the MASINA technology programme organised by Tekes (Finnish
Funding Agency for Technology and Innovation). The project was financed by
Tekes, VTT, Kalmar Industries Oy, Plustech Oy, and Plenware Oy. The research
work was carried out by Helsinki University of Technology and VTT.
We would like to thank all those who participated in this project.

Tampere, April 2006

Authors

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Contents
Abstract.................................................................................................................3
Preface..................................................................................................................4
List of acronyms...................................................................................................5
1. Introduction.....................................................................................................5
2. Clarifying the diagnostics picture...................................................................5
2.1 Definition of the term diagnostics.........................................................5
2.2 Costs and benefits of diagnostics...........................................................5
2.3 Diagnostics and FRACAS.....................................................................5
2.4 What is diagnosed..................................................................................5
2.5 Where do the diagnostics procedures reside..........................................5
2.6 When is the diagnosis performed?.........................................................5
2.7 Who performs the diagnosis?................................................................5
2.8 How to partition diagnostics into more manageable chunks.................5
2.9 Application-specific or generic.............................................................5
2.10 Diagnostics strategy...............................................................................5
3. Automotive and heavy-duty vehicles diagnostics standards...........................5
3.1 ISO and SAE diagnostics standards......................................................5
3.1.1 J1939/73....................................................................................5
3.1.2 ISO 15765-3..............................................................................5
3.2 Open Diagnostic data eXchange (ODX)...............................................5
4. Diagnostics architectures................................................................................5
4.1 MIMOSA...............................................................................................5
4.2 Condition-based maintenance................................................................5
4.2.1 Prognostics................................................................................5
4.2.2 Other component-based CBM systems.....................................5
4.2.3 Interpretation of OSA-CBM in machine automation................5
4.3 IEEE 1451.............................................................................................5
4.4 ISO 13374..............................................................................................5
4.5 The OSGi Alliance................................................................................5
4.5.1 Vehicle Expert Group Charter...................................................5
5
4.6 Agent systems........................................................................................5
4.7 Client - Server........................................................................................5
4.8 Example applications.............................................................................5
4.8.1 Passenger cars...........................................................................5
4.8.2 Airbus (cid:150) Aircraft Maintenance Analysis...................................5
4.8.3 Boeing (cid:150) Aeroplane Health Management.................................5
4.8.4 The National AirSpace System-Wide Simulation.....................5
5. Methods for Data Analysis.............................................................................5
5.1 Model-based approach...........................................................................5
5.2 Data-driven methods.............................................................................5
5.2.1 Principal Component Analysis..................................................5
5.2.2 Fisher Discriminant Analysis....................................................5
5.2.3 Statistical Process Control.........................................................5
5.3 Analytical methods................................................................................5
5.3.1 Parameter estimation.................................................................5
5.3.2 Observer-based methods...........................................................5
5.3.3 Parity relations..........................................................................5
5.4 Knowledge-based methods....................................................................5
5.4.1 Causal Analysis.........................................................................5
5.4.2 Expert Systems..........................................................................5
5.4.3 Artificial Neural Networks........................................................5
5.4.4 Feed forward neural network....................................................5
5.4.5 Case-Based Reasoning..............................................................5
5.5 Data mining...........................................................................................5
5.5.1 What is data mining?.................................................................5
5.5.2 Why data mining?.....................................................................5
5.5.3 Data mining process..................................................................5
5.5.4 Methods used in data mining....................................................5
5.5.5 Data mining time series.............................................................5
5.5.6 Example: Anomaly detection....................................................5
5.6 Combinations of various methods and techniques................................5
5.6.1 Neurofuzzy networks................................................................5
5.6.2 Fuzzy logic and analytical methods..........................................5
5.6.3 Neural networks and expert systems.........................................5
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6. Establishing sensor fault modes......................................................................5
6.1 General fault modes...............................................................................5
6.1.1 Potentiometer fault modes.........................................................5
6.1.2 Encoder fault modes..................................................................5
6.1.3 Proximity switch fault modes....................................................5
7. Conclusions.....................................................................................................5
References.............................................................................................................5

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List of acronyms
ABS Antilock Braking System
AHM Airplane Health Management
AI Artifical Intelligence
ANN Artificial Neural Network
API Application Programming Interface
AREMA American Railway Engineering and Maintenance-of-way Association
ASAM Association for Standardisation of Automation and Measuring Systems
CAN Controller Area Network
CBA Cost and Benefit Analysis
CBM Condition Based Maintenance
CBR Condition Based Reasoning
CBS Condition Based Service
CCP CAN Calibration Protocol
CCW Counter clockwise
CRIS Common Relational Information Schema
CW Clockwise
DTC Diagnostic Trouble Code
EOBD Euro On-Board Diagnosis
EPA Environmental protection agency
ESP Electronic Stability Program
FCM Fuzzy Cognitive Maps
FDA Fisher Discriminant Analysis
FDI Fault Detection and Identification
FIPA Foundation for Intelligent Physical Agents
FMI Failure Mode Identifier
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