Sensor Degradation Detection in Linear Systems

Jiang, Li; Djurdjanović, Dragan; Ni, Jun; Lee, J

doi:10.1007/978-1-84628-814-2_138

Cited by 13 publications

(8 citation statements)

References 8 publications

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“…To detect anomalies in the sensors, the actual sensor behavior is compared to the sensor behavior estimated by the model. However, this method requires a significant amount of prior knowledge about the dynamics and behavior of the sensors and the plant in order to detect sensor degradation and to separate this from the degradation of the measured plant [21].…”

Section: Analytical Approachesmentioning

confidence: 99%

“…Zhao et al [22] integrated the improved long short-term memory (LSTM) to build a classifier model to overcome drift issues in gas sensors. Other researchers [21] applied the ARMAX model (a generalization of the autoregressive moving average (ARMA) model) to the dynamic and static properties of the sensor to detect sensor degradation. An example of statistical approaches can be found in [23], where the authors used statistical approaches to monitor sensor data for smart structural health monitoring (SHM) systems.…”

Section: Analytical Approachesmentioning

confidence: 99%

“…Hardware approaches are taken to examine redundancy, whereby the same physical phenomenon is measured with multiple sensors. Multiple measurements from different sensors enable the detection of the faulty sensor either by means of a voting algorithm or by conducting correlation analysis [21].…”

Section: Hardware Approachesmentioning

confidence: 99%

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Degradation Detection in a Redundant Sensor Architecture

Kajmaković

Diwold

Römer

et al. 2022

Sensors

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Safety-critical automation often requires redundancy to enable reliable system operation. In the context of integrating sensors into such systems, the one-out-of-two (1oo2) sensor architecture is one of the common used methods used to ensure the reliability and traceability of sensor readings. In taking such an approach, readings from two redundant sensors are continuously checked and compared. As soon as the discrepancy between two redundant lines deviates by a certain threshold, the 1oo2 voter (comparator) assumes that there is a fault in the system and immediately activates the safe state. In this work, we propose a novel fault prognosis algorithm based on the discrepancy signal. We analyzed the discrepancy changes in the 1oo2 sensor configuration caused by degradation processes. Several publicly available databases were checked, and the discrepancy between redundant sensors was analyzed. An initial analysis showed that the discrepancy between sensor values changes (increases or decreases) over time. To detect an increase or decrease in discrepancy data, two trend detection methods are suggested, and the evaluation of their performance is presented. Moreover, several models were trained on the discrepancy data. The models were then compared to determine which of the models can be best used to describe the dynamics of the discrepancy changes. In addition, the best-fitting models were used to predict the future behavior of the discrepancy and to detect if, and when, the discrepancy in sensor readings will reach a critical point. Based on the prediction of the failure date, the customer can schedule the maintenance system accordingly and prevent its entry into the safe state—or being shut down.

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Section: Analytical Approachesmentioning

confidence: 99%

Section: Analytical Approachesmentioning

confidence: 99%

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Degradation Detection in a Redundant Sensor Architecture

Kajmaković

Diwold

Römer

et al. 2022

Sensors

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“…The problem of monitoring the sensor's performance has been considered by many authors. One solution is an approach based on capturing dynamic characteristics of the sensor within the sensor readings separately from those of the monitored system [47], which enables tracking of key dynamic indicators of the sensor and statistically assess the significance of their changes. In [48], the authors discuss three different methods for the sensors' conditions monitoring based on their own measurements which are able to detect deterioration/failures of the sensor parts in time for their replacement.…”

Section: Sensor Ageingmentioning

confidence: 99%

Advanced sensor-based maintenance in real-world exemplary cases

et al. 2020

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Collecting complex information on the status of machinery is the enabler for advanced maintenance activities, and one of the main players in this process is the sensor. This paper describes modern maintenance strategies that lead to Condition-Based Maintenance. This paper discusses the sensors that can be used to support maintenance, as of different categories, spanning from common off-the-shelf sensors, to specialized sensors monitoring very specific characteristics, and to virtual sensors. This paper also presents four different real-world examples of project pilots that make use of the described sensors and draws a comparison between them. In particular, each scenario has unique characteristics requiring different families of sensors, but on the other hand provides similar characteristics on other aspects.

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“…Finally, we assume that robots can estimate their sensor quality. Since there exist techniques for sensor fault detection [10] and degradation estimation [11], [12], this assumption does not impose undue restrictions on the applicability of our method.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Resilience in multi-robot target tracking through reconfiguration

Ramachandran¹,

Fronda²,

Sukhatme³

2019

Preprint

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We address the problem of maintaining resource availability in a networked multi-robot system performing distributed target tracking. In our model, robots are equipped with sensing and computational resources enabling them to track a target's position using a Distributed Kalman Filter (DKF). We use the trace of each robot's sensor measurement noise covariance matrix as a measure of sensing quality. When a robot's sensing quality deteriorates, the systems communication graph is modified by adding edges such that the robot with deteriorating sensor quality may share information with other robots to improve the team's target tracking ability. This computation is performed centrally and is designed to work without a large change in the number of active communication links. We propose two mixed integer semi-definite programming formulations (an 'agent-centric' strategy and a 'team-centric' strategy) to achieve this goal. We implement both formulations and a greedy strategy in simulation and show that the teamcentric strategy outperforms the agent-centric and greedy strategies.

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Sensor Degradation Detection in Linear Systems

Cited by 13 publications

References 8 publications

Degradation Detection in a Redundant Sensor Architecture

Degradation Detection in a Redundant Sensor Architecture

Advanced sensor-based maintenance in real-world exemplary cases

Resilience in multi-robot target tracking through reconfiguration

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