Sensor Placement for Online Fault Diagnosis

TL;DR

This paper introduces a model-based approach using answer set programming for optimal sensor placement in large systems to improve fault diagnosis efficiency, emphasizing modularity for scalability.

Contribution

It presents a novel ASP-based method for sensor placement that minimizes sensors and introduces modularity to handle larger systems effectively.

Findings

Sensor placement for 500-component systems achieved in minutes.

Modularity allows independent sensor placement in system modules.

Proposed approach reduces sensor count while maintaining diagnosis accuracy.

Abstract

Fault diagnosis is the problem of determining a set of faulty system components that explain discrepancies between observed and expected behavior. Due to the intrinsic relation between observations and sensors placed on a system, sensors' fault diagnosis and placement are mutually dependent. Consequently, it is imperative to solve the fault diagnosis and sensor placement problems jointly. One approach to modeling systems for fault diagnosis uses answer set programming (ASP). We present a model-based approach to sensor placement for active diagnosis using ASP, where the secondary objective is to reduce the number of sensors used. The proposed method finds locations for system sensors with around 500 components in a few minutes. To address larger systems, we propose a notion of modularity such that it is possible to treat each module as a separate system and solve the sensor placement problem for each module independently. Additionally, we provide a fixpoint algorithm for determining the modules of a system.