Sensor defect detection in multisensor information fusion

Jan-Friedrich Ehlenbröker,Uwe Mönks,Volker Lohweg

doi:10.5194/jsss-5-337-2016

Abstract

Abstract. In industrial processes a vast variety of different sensors is increasingly used to measure and control processes, machines, and logistics. One way to handle the resulting large amount of data created by hundreds or even thousands of different sensors in an application is to employ information fusion systems. Information fusion systems, e.g. for condition monitoring, combine different sources of information, like sensors, to generate the state of a complex system. The result of such an information fusion process is regarded as a health indicator of a complex system. Therefore, information fusion approaches are applied to, e.g., automatically inform one about a reduction in production quality, or detect possibly dangerous situations. Considering the importance of sensors in the previously described information fusion systems and in industrial processes in general, a defective sensor has several negative consequences. It may lead to machine failure, e.g. when wear and tear of a machine is not detected sufficiently in advance. In this contribution we present a method to detect faulty sensors by computing the consistency between sensor values. The proposed sensor defect detection algorithm exemplarily utilises the structure of a multilayered group-based sensor fusion algorithm. Defect detection results of the proposed method for different test cases and the method's capability to detect a number of typical sensor defects are shown.

Highlights

A sensor, which is acquiring signals in an application, is generally assumed to be operating correctly
The sensor defect detection approach proposed – is based on the sensor reliability monitoring approach presented in Glock et al (2011); – demands fuzzification of the measurement scales in all cases, which is delivered at no additional cost in the context of multilayer attribute-based conflict-reducing observation (MACRO); – determines observation consistency within groups of sensors, which are delivered at no additional cost in the context of MACRO; – adapts the majority consistency measure defined in Glock et al (2011)
To demonstrate that the detection of a sensor defect is possible, if the hazardous material store is in a critical state, a smoke detector is falsely activated during an actual leakage in the store

Summary

Introduction

A sensor, which is acquiring signals in an application, is generally assumed to be operating correctly. The approach presented in this article is partly based on Glock et al (2011), which introduces a method to determine the reliability of sensors This is in turn used to weight the sensors during a sensor fusion process. In Glock et al (2011), information fusion for machine condition monitoring is considered It employs multiple sensors acquiring their signals from the same application. Glock et al (2011) defined the dynamic reliability measure in the form of an exponential moving averaging infinite impulse response filter (Meyer-Baese, 2007) to account for noise in the sensor observations and include information about the inertia of the monitored application by the smoothing factor ω: in order to react fast to changes in application with high inertia, the smoothing factor is set to ω → 1. The smoothing effect of small values for ω is clearly visible

Sensor defect detection

Defect detection during non-normal operating conditions

Peaks min-1

Conclusion and outlook