A Predicting pipe failures with CMMS/GIS/Modeling

The intent of this paper is to describe an important planning tool for the City of Houston’s underground infrastructure maintenance program. The development of a GIS based predictive modeling tool is part of a general trend toward reducing corrective maintenance by increasing preventive and predictive maintenance activities. There are benefits of a design based on the use of a GIS as the central technology incorporating predictive modeling tools.

Experience has shown that a significant number of infrastructure repairs are performed on an unscheduled basis. In this time of budget cuts and limited resources, the ability to optimize the use of maintenance dollars by employing predictive models in the planning stages is rapidly becoming a reality of underground infrastructure management.

Planned maintenance for a facility in need of repair can yield significant savings over unscheduled or emergency repairs. The key is to enable planners to predict accurately which components are in the most urgent need of repair, and when others will need repair. This differs from corrective maintenance where repairs are made to fix an existing problem, or preventive maintenance where repairs are performed on a scheduled basis.

Knowledge of which facilities are most in need of repair, or most likely to fail offers planners several advantages.

It allows repairs to be grouped together based on location and facility type.
Bids on the repair work can be coordinated.
Scheduling the facility down time can be controlled to minimize its impact on its users.
Costs are anticipated up front and can be incorporated into tiding requests.

For this effort to be optimal and successfid, methods must be developed to obtain information as to which items are most likely to fail, and when these failures are likely to occur. Predictive Modeling includes a collection of techniques that can be used to determine the likelihood of failure or failure rate, for a particular entity. These modeling techniques range from very basic selection rules to complex analyses including statistical methods and artificial intelligence based systems. They often require the collection of time stamped information describing failure trends for the item being modeled.

Commercial use of predictive modeling has been limited by a number of obstacles. One reason is models were primarily designed for research. Another is the need to draw upon skills from multiple disciplines (engineering, statistics, and computer science) to develop a robust predictive modeling tool. In addition, accessible, comprehensive, historical databases are necessary to provide the models with the data they require. Typically, considerable computational power was necessary to perform the analyses.

The ability to develop a predictive modeling system now is due in part to the convergence and advancement of several key technologies. Computer hardware is plentifbl and relatively inexpensive in terms of computation power, disk capacity, and graphical display. Software packages such as geographic information systems, relational databases, statistical packages and artificial intelligence tools have reached a significant level of maturity. Further, these technologies are interacting through interfaces to each other. Statistical packages can operate on data in a GIS or RDBMS and links exist between GIS and facility management systems. In addition research activities are providing improved methods for prediction and display.

Houston GIMS and predictive modeling
The city of Houston is the fourth largest city in the United States with a population of 1.7 million covering an area of over 1500 square kilometers (over 600 square miles). The metropolitan area is comprised of over 19,000 square kilometers (over 7,500 square miles) with a population of 3.7 million.