06-05-2014, 12:11 PM
A review of process fault detection and diagnosis Part I: Quantitative model-based methods
A review of process fault detection.pdf (Size: 250.9 KB / Downloads: 27)
Abstract
Fault detection and diagnosis is an important problem in process engineering. It is the central component of abnormal event
management (AEM) which has attracted a lot of attention recently. AEM deals with the timely detection, diagnosis and correction
of abnormal conditions of faults in a process. Early detection and diagnosis of process faults while the plant is still operating in a
controllable region can help avoid abnormal event progression and reduce productivity loss. Since the petrochemical industries lose
an estimated 20 billion dollars every year, they have rated AEM as their number one problem that needs to be solved. Hence, there is
considerable interest in this field now from industrial practitioners as well as academic researchers, as opposed to a decade or so ago.
There is an abundance of literature on process fault diagnosis ranging from analytical methods to artificial intelligence and statistical
approaches. From a modelling perspective, there are methods that require accurate process models, semi-quantitative models, or
qualitative models. At the other end of the spectrum, there are methods that do not assume any form of model information and rely
only on historic process data. In addition, given the process knowledge, there are different search techniques that can be applied to
perform diagnosis. Such a collection of bewildering array of methodologies and alternatives often poses a difficult challenge to any
aspirant who is not a specialist in these techniques. Some of these ideas seem so far apart from one another that a non-expert
researcher or practitioner is often left wondering about the suitability of a method for his or her diagnostic situation. While there
have been some excellent reviews in this field in the past, they often focused on a particular branch, such as analytical models, of this
broad discipline. The basic aim of this three part series of papers is to provide a systematic and comparative study of various
diagnostic methods from different perspectives. We broadly classify fault diagnosis methods into three general categories and review
them in three parts. They are quantitative model-based methods, qualitative model-based methods, and process history based
methods. In the first part of the series, the problem of fault diagnosis is introduced and approaches based on quantitative models are
reviewed. In the remaining two parts, methods based on qualitative models and process history data are reviewed. Furthermore,
these disparate methods will be compared and evaluated based on a common set of criteria introduced in the first part of the series.
We conclude the series with a discussion on the relationship of fault diagnosis to other process operations and on emerging trends
such as hybrid blackboard-based frameworks for fault diagnosis.
Structural changes
Structural changes refer to changes in the process
itself. They occur due to hard failures in equipment.
Structural malfunctions result in a change in the
information flow between various variables. To handle
such a failure in a diagnostic system would require the
removal of the appropriate model equations and re-
structuring the other equations in order to describe the
current situation of the process. An example of a
structural failure would be failure of a controller. Other
examples include a stuck valve, a broken or leaking pipe
and so on.
Malfunctioning sensors and actuators
Gross errors usually occur with actuators and sensors.
These could be due to a fixed failure, a constant bias
(positive or negative) or an out-of range failure. Some of
the instruments provide feedback signals which are
essential for the control of the plant. A failure in one
of the instruments could cause the plant state variables
to deviate beyond acceptable limits unless the failure is
detected promptly and corrective actions are accom-
plished in time. It is the purpose of diagnosis to quickly
detect any instrument fault which could seriously
degrade the performance of the control system.
Isolability
Isolability is the ability of the diagnostic system to
distinguish between different failures. Under ideal con-
ditions free of noise and modelling uncertainties, this
amounts to saying that the diagnostic classifier should
be able to generate output that is orthogonal to faults
that have not occurred. Of course the ability to design
isolable classifiers depends to a great extent on the
process characteristics. There is also a trade-off between
isolability and the rejection of modelling uncertainties.
Most of the classifiers work with various forms of
redundant information and hence there is only a limited
degree of freedom for classifier design. Due to this, a
classifier with high degree of isolability would usually do
a poor job in rejecting modelling uncertainties and vice
versa.
Conclusions
In this first part of the three part review paper, we
have reviewed quantitative model based approaches to
fault diagnosis. For the comparative evaluation of
various fault diagnosis methods, we first proposed a
set of desirable characteristics that one would like the
diagnostic systems to possess. This can serve as a
common set of criteria against which the different
techniques may be evaluated and compared. Further,
we provided a general framework for analyzing and
understanding various diagnostic systems based on the
transformations of the process data before final diag-
nosis is performed. For quantitative model based
diagnosis methods, we discussed and reviewed various
issues involved in the design of fault diagnosis systems
using analytical models.