Service-Oriented Architecture on the Grid for FDI
Integration
X. Ren*, M. Ong*, G. Allan*, V. Kadirkamanathan*, H. A. Thompson*† and P. J. Fleming*†
†
Rolls-Royce University Technology Centre in Control and Systems Engineering
*
Department of Automatic Control and Systems Engineering
University of Sheffield
Sheffield S1 3JD
UK
Email: {X.Ren, M.Ong, Jeff.Allan, Visakan, H.Thompson, P.Fleming}@shef.ac.uk
Abstract
Many model-based fault diagnosis approaches have been proposed so far and some of them are
applied in industrial practice. But for modern complex processes, due to the variable nature of faults
and model uncertainty, no single approach can diagnose all faults and meet different contradictory
criteria. In this paper, the importance of integration of different fault diagnosis schemes in a
common framework is emphasised. A service-oriented architecture for the integration is proposed
based on Grid technologies. Some implementations of this integration for the gas turbine engine
fault diagnosis are presented.
1.
Introduction
Model-based approaches are widely accepted
modern approaches for industrial fault detection
and isolation (FDI). It is based upon the idea
that measurements from dissimilar sensors are
functionally related because they are all derived
from the same state of the system. Any violation
of these relationships indicates the occurrence
of faults. Although the model-based approach is
commonly accepted as a modern approach for
fault diagnosis, due to model uncertainties,
intense computational requirements and
unknown complicated nature of fault diversity,
there is no single widely accepted generic
solution of model-based fault diagnosis.
Researchers working in this area have proposed
different approaches of using different
algorithms under the name ‘model-based’ [1-3].
Each approach, however, has its own focus and
none of them is a universal approach, neither
suitable nor available for all fault types.
To overcome these shortfalls and exploit
advantages of different approaches, an
integration of different methods or hybrid
schemes are highly recommended [4, 5]. With
the latest development in Internet and Intranet
technologies, especially with the development
of the Grid computing, it is now possible to
provide different algorithms as individual Grid
services
and
combine
these
services
dynamically to generate a ‘virtual organisation’
for fault diagnostic purposes. In this paper, a
service-oriented architecture on the Grid for
integrated fault diagnostics is proposed.
Different fault diagnosis algorithms and
analysis tools are provided as Grid services in
this framework. Through configuration and
workflow management, these services can be
dynamically invoked to form a flexible
distributed fault diagnosis environment for fault
diagnosis and maintenance.
This paper is organised as follows: Section 1
presents an overview of model-based fault
diagnosis. Different approaches developed for
model-based fault diagnosis are summarised
and compared. In Section 2, the concept of Grid
computing is introduced. The service-oriented
architecture on the Grid for fault diagnosis is
proposed and the advantages are highlighted. In
Section 3, the DAME project is introduced and
some experimental work on the FDI integration
are presented. The developed gas turbine engine
simulation services for fault detection and casebased reasoning services for decision-making
are detailed. Finally, some concluding remarks
are made.
2.
Model-based Fault Diagnosis
Model-based approaches are widely accepted
modern approach for solving fault diagnosis
problems. Model-based fault detection and
isolation focuses on dynamic consistency
(parity) relations and parameter estimation. The
basic procedure of using model-based FDI is
firstly to generate the analytic symptoms by
using analytical knowledge about the process
based on process observation. Then the
generated analytic symptoms as well as
heuristic symptoms are analysed at the fault
diagnosis stage for finding the type, size and
location of a fault as well as its time of
detection.
In general, model-based FDI is a two-step
procedure: residual generation and residual
evaluation. Residual generation is a process in
which the input and output of a system are
monitored and manipulated to generate a signal
or vector, the so-called residual. The residual
should be normally zero or close to zero when
no fault is present, but is distinguishably
different from zero when a fault occurs.
Residual generation is thus a procedure for
extracting fault symptoms from the system, with
the fault symptom represented by the residual
signal. The residual should ideally carry only
fault information. To ensure reliable FDI, the
loss of fault information in residual generation
should be as small as possible.
Residual evaluation is the analysis of the
residual to examine the likelihood of faults. A
decision is made based on the knowledge about
the process and the symptoms. If a fault has
occurred, more analysis should be made to
isolate or even identify the fault. A decision
process may consist of a simple threshold test
on the instantaneous values or moving averages
of the residuals, or it may consist of methods of
more sophisticated decision theories.
From the various approaches used for the
residual generation and evaluation, there are
roughly four different approaches used by
model-based FDI:
• Observer approach or parity relations
approach. The underlying idea of observer
approach is to estimate the system outputs
from the available inputs and output of that
system. The residual will then be a
weighted difference between the estimated
and actual outputs. In a similar way, the
parity relations approach is based either on
a technique of direct redundancy, making
use of static algebraic relations between
sensor and actuator signals or alternatively,
upon temporal redundancy, when dynamic
relations between inputs and outputs are
used.
• Parameter estimation approach. This
approach makes use of the fact that
component faults of a dynamic system can
be thought of as reflected in the physical
parameters of the system, e.g. friction or
mass velocity resistance. A fault is then
can be detected through the parameter
estimation or model identification.
• Statistical approaches. Mainly used to
improve the fault detection capability,
statistical approaches such as the
generalised likelihood ratio (GLR) test can
be used to find changes of the residual
signal more quickly and accurately.
Principle component analysis (PCA) and
Fisher discriminant analysis (FDA) are the
most widely used techniques for
dimensionality reduction and pattern
classification on FDI.
• Qualitative approach. The qualitative
approach is based upon the concept of a
qualitative model which unlike its
quantitative counterpart only requires
declarative (heuristic) information. An
expert system, for example, is one of the
quantitative approaches that use if-then
rules to represent the human knowledge of
the relation between the normal/abnormal
system behaviour and the causes of faults.
The fault tree approach traces the
evolution of the fault through the dynamic
system described by a fault tree, event
trees or causal networks. There are also
other qualitative model-based approaches
which use a qualitative model derived
directly from the physical laws of the
system under consideration. Bond Graphs
and Petri Nets, for example, can be used to
assist this modelling purpose.
In general, a fault diagnosis technique
should be able to complete the following two
main tasks:
• detect and isolate different faults occurring
in a process, which include sensor faults,
actuator faults and component faults.
• detect and isolate incipient faults as well as
abrupt faults.
While at the same time, a fault diagnosis
scheme should also consider the following
criteria for better fault diagnosis performance:
• Promptness of fault detection
• Sensitivity to incipient faults
• False alarm rate and missed fault detection
• Incorrect fault identification
Although different model-based approaches
are designed to solve fault diagnosis problems,
each approach has its own focus and none of the
above mentioned methods are a generic solution
for complex modern system fault diagnosis.
This is due to the complicated nature of the
monitored system and faults, the applicability of
different modelling approaches or the
insufficient knowledge about the monitored
Table 1. A comparison of different model-based approaches for FDI
process. Table 1 is a summarised comparison of
different selected model-based approaches for
fault diagnosis. From this comparison, it is clear
that none of these approaches can fully satisfy
the requirements of modern fault diagnosis such
as promptness, accuracy and sensitivity to
faults. It is commonly agreed that hybrid
schemes would provide better solutions for
future complex system fault diagnostics [6, 7].
Thus an evaluation of different FDI approaches
and the integration of assorted FDI approaches
in an open computational environment are
crucial. Additionally, it is important to consider
how these approaches should be used in
conjunction to provide the most accurate
diagnosis in the decision-making process.
Another restriction of using some modelbased FDI techniques is the inherent demands
for intensive computing power for modelling
and
simulation.
These
computation
requirements also limit its application on large
scale complex systems. The Grid technology
provides the necessary High-Performance,
High-Throughput
computing
power
to
overcome these restrictions. The distributed
computing structure and the organised resource
sharing features provide excellent opportunities
to integrate different FDI schemes to obtain
better fault diagnostic performance.
3.
Service-Oriented Architecture on
the Grid for FDI Integration
Service-oriented architecture (SOA) is not a
new concept. The early service-oriented
architecture uses the Distributed Component
Object Model (DCOM) [8] or Object Request
Brokers (ORBs) based on the CORBA
specification [9]. Over the last few decades,
software has slowly been decoupled. The
introduction of the Client/Server structure
removed the database from the fat client. The
thin-client decoupled the user interface from the
business logic. Service-oriented architectures
were proposed to decouple the integration logic
from the business logic. Basically, services and
service-oriented architectures are about
designing and building systems using
heterogeneous network addressable software
components. A service-oriented architecture is
thus an architecture that made up of components
and
interconnections
that
stresses
interoperability and location transparency. With
the introduction of Web services and Grid
services, there have been renewed interests in
building service-oriented architectures for
‘virtual organisations’ [10].
Essentially use XML to create a robust
connection, Web services is the most likely
connection technology of service-oriented
architectures. At the core of the Web services
model is the notion of a service, which is
defined as a collection of operations that carry
out some types of tasks. Within the context of
Web services, there are three components,
namely service providers, service requestors
and service brokers. A service is deployed on
the Web by the service provider. The functions
provided by a given Web service are described
using the Web Services Description Language
(WSDL) [11] and published on the Web. A
service broker helps the service provider and
service requester find each other through a
UDDI (Universal Description, Discovery, and
Integration) [12] based registry. A service
requester uses the standard API to ask the
service broker about the services it needs and
then uses SOAP (Simple Object Access
Figure 1. Integrated fault diagnosis on the Grid
Protocol) [13] to invoke the remote service
provider side applications.
The Grid is a name that was first coined in
the mid-'90s to describe a vision for a
distributed computing infrastructure for
advanced science projects. First properly
explained by Ian Foster and Carl Kesselman
[14], the Grid should enable “resource sharing
and coordinated problem solving in dynamic,
multi-institutional virtual organisations”. With
the
first
generation
Grid
involving
“metacomputers” and second generation Grid
focused on middle ware and communication
protocols, now it is claimed that the third
generation Grid is combining service-oriented
architecture concepts and Web services
technologies to create Open Grid Services
Architecture (OGSA) [15], whereby a set of
common interface specifications support the
interoperability of discrete, independently
developed services. The Open Grid Services
Infrastructure (OGSI) service specification is
the keystone in implementing this architecture,
followed by the recent Web Service Resource
Framework (WSRF), which is a set of six Web
services specifications that try to meld Web
service with Grid computing by defining how to
model and manage state in a Web services
context.
Defined by OGSA, a Grid Service is
basically a Web service, which is a set of
Internet-based distributed processes. Based on
the emerging standards such as XML, SOAP,
WSDL and UDDI, the promise of Grid services
is to enable a distributed environment in which
any number of applications, or application
components, can interoperate seamlessly among
organisations in a platform-neutral, languageneutral fashion on the Grid.
To support the complex system fault
diagnostics, a methodology have been proposed
to integrate suites of modelling, estimation and
analysis tools for fault diagnosis on the Grid.
The service-oriented architecture is adopted
here to support this integration and the latest
development on the Grid has been implemented
to meet the specifications of OGSA and WSRF.
By adopting this open service-oriented
architecture for the integrated fault diagnostics,
the most commonly used techniques for FDI,
which include parameter estimation, state
observer, parity relations, statistic approaches
and symbolic approaches, can all be developed
individually as fault diagnostic services. As
illustrated in Figure 1, these services can be
created and maintained by different institutions
for different fault diagnostic purposes and are
all defined through a commonly accepted
description format, namely the Web Service
Figure 2. Engine simulation Grid service
Description Language (WSDL). Registered with
the Universal Description, Discovery, and
Integration (UDDI) registry, these fault
diagnostic services can be discovered and
organised in a flexible way. The result is a
versatile virtual FDI toolbox on the Grid. Users
can access this toolbox by using an Internet
browser via a Grid portal, and they can select
different FDI schemes to fit their own unique
requirements. By adopting the OGSA
specification, the proposed architecture is a Grid
solution to integrated fault diagnostics, which
allows different FDI applications to share
algorithms, data and computing resources as
well as to access them across multiple
organisation in an efficient and secure way.
4.
Implementation
The UK e-Science pilot project Distributed
Aircraft Maintenance Environment (DAME) is
developing a distributed diagnostics and
prognostics system for maintenance of civil
aerospace engines. The techniques will
generalise to other diagnostic domains such as
medicine, transport and manufacturing. The
DAME system uses Grid technology to
demonstrate how remote and diverse
applications and services can be linked into a
virtual diagnostic environment. Various
techniques are employed in the project, which
include advanced pattern matching to search
very large data sets (Terabytes), modelling for
fault diagnosis and simulation for decision
making, case based advice, workflow
management and collaboration environments
[16].
As one effort carried out on the DAME
project,
the
proposed
service-oriented
architecture for integrated fault diagnostics has
been implemented. Initially, A gas turbine
engine performance model was provided as a
Grid service to facilitate the exploitation of
further development and analysis of different
model-based FDI approaches. Figure 2
illustrates a running scenario of this Grid
service through a Web portal.
Figure 3 shows one basic usage of the
engine simulation Grid service for fault
diagnosis. When an accurate system
performance simulation is available on the Grid,
the experienced maintenance engineers can
invoke this simulation against the real
monitored process data. The system that is
being analysed is compared against the
simulation results. The differences between the
current state of the engine and the ideal model
generate residuals. These residuals then need to
be intelligently analysed to form a decision
about the current state of the engine. This can
be used to track changes in engine parameters
which may indicate impending faults.
histories instead of the original binary time
series data can help to overcome the model
uncertainty and unmodeled noise. As the result,
the robustness of the fault diagnosis is
improved.
Figure 4. Event generation and analysis
Figure 3. Simulation-based fault diagnosis
The advantages of providing an engine
performance simulation as a Grid service is that
the engine simulation service is identified by a
URI, whose public interfaces and bindings are
defined and described using XML. Authorised
users can perform the engine performance
simulation through a Web browser remotely
without knowing details of the execution of the
simulation. The simulation service itself is
distributed among a set of high-performance
computers on the Grid. Based on the Globus
Toolkit 3 (GT3) [17], this engine simulation
Grid service can be invoked simultaneously in
different ‘Virtual Organisations’ for different
applications.
The engine simulation Grid service has also
been used in the event generation and analysis
for engine fault diagnosis and maintenance. As
illustrated in Figure 4, there are two stages for
this work. In the observation processing stage,
raw measurement of different engine
performance variables and engine simulation
results as inputs are analysed. A change
detection method is used to characterise the
input time series. The goal is to recognise
changes that are important in the context of
engine
performance
behaviour
which
correspond to engine faults. This process has
two aspects: segmenting data in a meaningful
way and extracting features that are useful about
whether the engine is exhibiting normal or
abnormal behaviour. In the combination stage,
two event sequences from both the raw
measurement and the engine simulation are
compared, any discrepancy will indicate
possible fault.
The advantage of introducing a separate
event history based on the engine simulation is
that a reference of health engine history is
provided to assist the fault diagnostic decisionmaking. The comparison of two discrete event
Another effort of DAME project on the
integration of fault diagnostics on the Grid is the
use of Case-Based Reasoning (CBR) [18]
services to correlate and integrate fault
indicators from different aero engine input
monitoring systems, BITE reports, maintenance
data and dialog with maintenance personnel to
allow troubleshooting of faults. As a qualitative
fault diagnosis approach, case-based reasoning
is a problem-solving paradigm that resolves new
problems by adapting the solutions used to
solve problems of a similar nature experienced
in the past. A further advantage of this approach
is that it allows consolidation of rule knowledge
and provides a reasoning engine that is capable
of probabilistic-based matching. With CBR
technology, development can take place in an
incremental
fashion
facilitating
rapid
prototyping of an initial system. The
development of robust strategies for integration
of multiple health information sources can be
achieved with reasoning algorithms of
progressively increasing complexity.
Also deployed as Grid services on the Grid
environment, the CBR services can be invoked
by other authorised Grid services or
maintenance analyses to perform the high-level
fault diagnostics and decision-making, as
illustrated in Figure 5. The advantage of
deploying CBR as the Grid service is that
maintenance personnel can access a secured
connection to the service via a web browser on
any computer connected to the Internet. The
maintenance personnel will then have access to
stores of accumulated diagnostic knowledge and
maintenance data as well as large computing
resources to support the fault analysis and the
decision-making process. Other fault diagnostic
services can be used to perform the preliminary
fault diagnosis and the results can be used to
facilitate the CBR analysis. As standard Grid
services, the CBR services can be invoked to
produce useful outcomes that are profitable to
Figure 5. Case-bsed reasoning Grid service
other decision-making services as well. In the
future, the CBR services can be upgraded to
accommodate a dynamic learning process.
Anomalous data (data containing unknown
faults) may be analysed in DAME to produce
new fault cases that are dynamically appended
to the casebase, further increasing the
knowledge of the system.
A typical use case which encompasses both
the engine simulation and CBR services in the
fault analysis and maintenance process is
described as follows. Data downloaded from an
aircraft is first analysed for novelties (known
fault occurrences). The existence of fault and
the possible fault type can be checked against
the engine simulation. If a novelty exists, then
further information is extracted from the data
and other available fault diagnostic services to
form a query within the CBR services. The
result returned to the maintenance personnel
consists of previous similar fault cases, known
solutions to the problem, as well as a confidence
ranking for each case. The maintenance
analyses and domain experts can further take
advantages of the integrated fault diagnostic
tools to confirm the fault diagnosis findings. For
example, the domain experts can substantiate a
proposed fault analysis by injecting the similar
fault into an engine model and perform a
simulation to check the uniformity.
By using the Grid technologies and the
proposed serviced-oriented architecture, more
modelling, simulation, and decision-making
services from different providers or institutes
can be shared, coordinated and integrated for
fault diagnostics, prognostics and engine
maintenance.
5.
Concluding Remarks
In this paper the use of service-oriented
architecture and Grid technologies to support
the distributed fault diagnosis of complex
systems was discussed. An open framework
based on the OGSA has been proposed to
address the requirements of integrated
diagnostics. The business benefits of this open,
flexible approach to integrated fault diagnostics
are not only improved fault diagnosis
performance, but also reusable service
assembly,
better
maintainability,
better
parallelism in development, higher availability
and better scalability. The deployment and
integration of more fault diagnostic schemes on
the Grid are needed for this work. Future
research will also focus on the workflow
management, which orchestrates different fault
diagnosis services to deliver the desired
coherent fault diagnostics.
6.
Acknowledgment
This work and the DAME project are supported
by the UK EPSRC under contract 2382. The
authors would also like to thank the anonymous
referees for their helpful suggestions on
improving the presentation.
7.
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