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A Dependency Detection Method for Sensor-based Fault Detection

  • In Sensor-based Fault Detection and Diagnosis (SFDD) methods, spatial and temporal dependencies among the sensor signals can be modeled to detect faults in the sensors, if the defined dependencies change over time. In this work, we model Granger causal relationships between pairs of sensor data streams to detect changes in their dependencies. We compare the method on simulated signals with the Pearson correlation, and show that the method elegantly handles noise and lags in the signals and provides appreciable dependency detection. We further evaluate the method using sensor data from a mobile robot by injecting both internal and external faults during operation of the robot. The results show that the method is able to detect changes in the system when faults are injected, but is also prone to detecting false positives. This suggests that this method can be used as a weak detection of faults, but other methods, such as the use of a structural model, are required to reliably detect and diagnose faults.

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Metadaten
Document Type:Conference Object
Language:English
Author:Pooja Bhat, Santosh Thoduka, Paul G. Plöger
Parent Title (English):30th International Workshop on Principles of Diagnosis DX'19, November 11-13, 2019, Klagenfurt, Austria
Pagenumber:7
URL:https://dx-workshop.org/2019/accepted-papers/
Date of first publication:2019/10/08
Submission status:accepted
Departments, institutes and facilities:Fachbereich Informatik
Projects:ROPOD Ultra-flat, ultra-flexible, cost-effective robotic pods for handling legacy in logistics (EC/H2020/731848)
Dewey Decimal Classification (DDC):0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Entry in this database:2019/11/09