Fuel Pipeline Thermal Conductivity in Automatic Wet Stock Reconciliation Systems

Foszner, Paweł; Gruca, Aleksandra; Bularz, Jakub

doi:10.1007/978-3-319-41561-1_22

Cited by 1 publication

(1 citation statement)

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“…The verification system was incorporated into the fuel station tank monitoring system, which consists of the three main parts: the station part (implements software and hardware related to data acquisition, connects directly to devices and sends data to the central server); the server part (receives data from many stations, automatically processes this data and tries to draw and present preliminary conclusions [25]); the analytical part (responsible for analysing the results of the server part and for making decisions with human supervision).…”

Section: Implementation Of Sensor Verificationmentioning

confidence: 99%

Continual learning of a time series model using a mixture of HMMs with application to the IoT fuel sensor verification

Głomb,

Cholewa,

Foszner

et al. 2023

Annals of Computer Science and Information Systems

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This paper presents an application of a mixture of Hidden Markov Models (HMMs) as a tool for verification of IoT fuel sensors. The IoT fuel sensors report the level of fuel in tanks of a petrol station, and are a key component for monitoring system reliability (billing), safety (fuel/oil leak detection) and security (theft prevention). We propose an algorithm for learning a mixture of HMMs based on a continual learning principle, i.e. it adapts the model while monitoring a sensor over time, signalling unexpected or anomalous sensor reports. We have tested the proposed approach on a real-life data of 15 fuel tanks being monitored with the FuelPrime system, where it has shown a very good performance (average area under ROC curve of 0.94) of detecting anomalies in the sensor data. Additionally we show that the proposed method can be used for trend monitoring and present qualitative analysis of the short and long term learning performance. The proposed method has promising performance score, the resulting model has a high degree of explainability, limited memory and computation requirements and can be easily generalized to other domains of sensor verification.This work was partially funded by National Center for Research and Development no POIR.01.01.01-00-0376/17-00 'A system for gathering and analysis of streaming data for fuel stations, for optimization of distribution costs and fuel sales as well as on-line monitoring for leakages-related issues' (FuelPrime).

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Section: Implementation Of Sensor Verificationmentioning

confidence: 99%