A significant cost in industrial fluid systems is associated with system maintenance and unexpected downtime due to the troubleshooting and repair of system faults. In large complex systems, faults are often difficult to identify and predict, but recent advances in technology have enabled low-cost wireless-capable sensors, which can provide an unprecedented amount of data to system owners. Combining this data with knowledge of the system dynamics, and a methodical structural analysis-based fault diagnosis approach, provides new opportunities in the field of fault diagnostics. The goal of the project described in this paper is to develop methods to detect and diagnose faults in industrial fluid systems in order to minimize downtime costs and energy losses, with minimum system capital cost in mind.
This paper summarizes a portion of the first phase of this project, which focuses on fault modeling of a small-scale compressed-air test bench. Specifically, the design of the test bench, creation and validation of a flow model used to understand the test bench system dynamics, fault and structural analysis of an element of the test bench (an orifice-pipe combination), and the generation of a sensor installation guide that indicates what type and where to place sensors to detect and isolate faults that may occur. The sensor installation guide provided insight that all faults in the orifice-pipe element were detectable and isolable to the desired level with only a pressure sensor in the orifice and pipe. Orifice and orifice pressure sensor faults were uniquely isolable, but pipe and pipe pressure sensor faults were only isolable.