Filter Clogging Data Collection for Prognostics

O. F. Eker, F. Camci, and I. K. Jennions
Submission Type: 
Full Paper
phmc_13_025.pdf1.11 MBSeptember 13, 2013 - 4:59pm

Filtration is a critical process in many industrial systems to obtain the desired level of purification for liquids or gas. Air, fuel, and oil filters are the most common examples in industrial systems. Filter clogging is the main failure mode leading to filter replacement or undesired outcomes such as reduced performance and efficiency or cascading failures. For example, contaminants in fuel (e.g. rust particles, paint chips, dirt involved into fuel while tank is filling, tank moisture rust) may lead to performance reduction in the engine and rapid wear in the pump. Prognostics has potential to avoid cost and increase safety when applied to filters. One of the main challenges of prognostics is the lack of failure degradation data obtained from industrial systems. This paper presents the process of design and building of an experimental rig to obtain prognostics data for filter clogging mechanism and data obtained from the rig. Two types of filters have been used during the accelerated filter clogging and 23 run-to-failure data have been collected. Flow rate and pressure sensors are used for condition monitoring purposes. The filter clogging has been recorded through a camera to evaluate the findings with pressure and flow sensors. The data collected is very promising for development of prognostics methodologies.

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Submission Keywords: 
Data-driven prognostics
Filter Clogging
Experimental Rig Design
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
Health management system design and engineering
Model-based methods for fault detection, diagnostics, and prognosis
Modeling and simulation
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