Predicting pipe failures with CMMS/GIS/Modeling
Expert systems
Rule based expert systems are a natural extension of the use of selection rules. Using an expert
system, complex selection rules could be formulated that would lead to the selection of high risk
facilities. Expert systems offer a mechanism to capture knowledge of experts in the field in an
attempt to retain and share their knowledge. Expert systems are not currently part of the GIMS
effort. Expert systems have been used for sewer network maintenance in Paris with a limited
degree of success and for pavement repair management with some signs of success by the New
Mexico State Highway and Transportation Department.
Predictive statistical methods
Statistical predictive models are based on historical data. Dates of events with attendant
descriptive information need to be available to the model. In time, as available information
increases, the accuracy of this type of approach should improve. There will be more data
available to the models for calibration. Survival analysis using Proportional Hazards Models has
been used successfidly to determine the probability of failure for water mains. Development is
currently underway based on the use of the Andersen-Gill Proportional Hazards Model. This
model can be used to predict multiple future failures. Predicted failure rates can then be used in a
cost benefits analysis to determine whether to ignore, repair, or replace the facility.
One disadvantage of the use of statistical predictive models is their inherent complexity.
Although attempts will be made to isolate the user from all the complexities of this type of
procedure, the user will still be required to have a significant level of understanding of the
statistical process in order to produce valid results.
Neural networks
Another method under investigation for predictive modeling involves the use of neural networks.
Neural networks are a branch of artificial intelligence that is very good at solving pattern
recognition problems. The patterns of attributes and failure events in the database should be
detectable by neural networks. The major benefit to using neural networks is their ability to find
the patterns in the data without a complete understanding of the predictive processes, in addition
they can easily operate on all data types including nominal data. Neural networks offer a
solution that requires less knowledge on the part of the user. This can be considered a black box
approach.
Spatial analysis
The use of GIS as an integral component of a facility management decision support tool brings
an additional set of analytical tools to the system: spatial analysis tools. Operations including
buffering and overlay are found in many geographic information systems. Research has shown
that breaks often occur in clusters. Spatial analysis tools like Quadrat analysis, Thiessen polygon
generation, spatial autocorrelation and fiture analytical techniques may offer new methods for
predictive modeling.
System design
The GIS serves as the integrating technology for the other components. The relational database
management system is available for tabular and spatial attribute storage. The database is
accessible via the facility management front end, and through the GIS tools. Statistical and
neural network tools that require more than just tabular data retrieve these data via GIS access
methods. Attributes are linked to the spatial entity/item.
Geographic information systems typically provide a wide range of display capabilities. These
display functions are closely related to the database contents. Thematic mapping (shading) can
be performed based on different variables. Different layers can be overlaid. Geographical
measurements can be performed on the screen. Interactive exploratory selection and query is
supported to inspect the attributes of particular features.
Conclusions
The development of a decision support tool for facility management planning that incorporates
predictive modeling tools will be of significant value to managers and planners. The
functionality outlined in this paper will allow managers and planners to analyze the effects of
many different factors on failure and failure rate. Developing a comprehensive CMMS is the
foundation for giving the end user the ability to build on and develop an accurate Predictive
Modeling system. Using and integrating GIS as the central component provides a structure that
can facilitate the operation of the different tools necessary to implement a Predictive Modeling
system.
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