Application of Exergy-Based Fault Detection in a Gas-To-Liquids Process Plant

Greyling, Sarita; Marais, Henri; Schoor, George van; Uren, Kenneth R.

doi:10.3390/e21060565

Cited by 12 publications

(3 citation statements)

References 46 publications

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“…These difference matrices are then analysed to enable FDI. The work of [41] illustrated the usefulness of exergy for this process, however, the paper only focused on steady-state scenarios testing the distance parameter method. The work of [31] expanded the research by comparing the performance of the eigenvalue decomposition method with that of PCA on a two-tank system.…”

Section: Fdi Techniquesmentioning

confidence: 99%

A comparative study of energy graph-based fault detection and isolation techniques applied to a lignite plant

Smith,

van Schoor,

Uren

et al. 2023

Heliyon

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Section: Fdi Techniquesmentioning

confidence: 99%

A comparative study of energy graph-based fault detection and isolation techniques applied to a lignite plant

Smith,

van Schoor,

Uren

et al. 2023

Heliyon

Self Cite

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“…The isolability performance of the fault detection scheme can be evaluated by comparing the fault signature of each fault to all the others. A measure of the difference between two fault signatures can be obtained by subtracting the one signature from the other [21]. From [21] the distance between two 6 × 5 fault signatures x and y (as typical of the fault signatures in Tables 8-10) can be defined as…”

Section: Fault Condition Plus Signs Minus Signsmentioning

confidence: 99%

An Energy Graph-Based Approach to Fault Diagnosis of a Transcritical CO2 Heat Pump

et al. 2020

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The objective of this paper is to describe an energy-based approach to visualize, identify, and monitor faults that may occur in a water-to-water transcritical CO 2 heat pump system. A representation using energy attributes allows the abstraction of all physical phenomena present during operation into a compact and easily interpretable form. The use of a linear graph representation, with heat pump components represented as nodes and energy interactions as links, is investigated. Node signature matrices are used to present the energy information in a compact mathematical form. The resulting node signature matrix is referred to as an attributed graph and is populated in such a way as to retain the structural information, i.e., where the attribute points to in the physical system. To generate the energy and exergy information for the compilation of the attributed graphs, a descriptive thermal-fluid model of the heat pump system is developed. The thermal-fluid model is based on the specifications of and validated to the actual behavioral characteristics of a physical transcritical CO 2 heat pump test facility. As a first step to graph-matching, cost matrices are generated to represent a characteristic residual between a normal system node signature matrix and a faulty system node signature matrix. The variation in the eigenvalues and eigenvectors of the characteristic cost matrices from normal conditions to a fault condition was used for fault characterization. Three faults, namely refrigerant leakage, compressor failure and gas cooler fouling, were considered. The paper only aims to introduce an approach, with the scope limited to illustration at one operating point and considers only three relatively large faults. The results of the proposed method show promise and warrant further work to evaluate its sensitivity and robustness for small faults.Energies 2020, 13, 1783 2 of 34 Zogg also refers to a field survey [2] that indicates that the average energetic efficiencies of the heat pumps installed in Swiss buildings were about 10% lower than the efficiencies measured under standard laboratory conditions. It was also found that for some installations the performance degradation was as high as 30%. A similar study in the United States of over 55,000 air conditioning units showed that more than 90% were operating with one or more kinds of faults [3]. Yet another study surveying roof-top units indicates an operating efficiency of 80% of designed performance with 63% of these units having performance degradation due to refrigerant leakage [4]. These observations motivate the need for diagnostic techniques which ideally can monitor these energy conversion systems during transient and steady-state operating conditions.In the review papers by Zhao et al., and Shi and O'Brien [5,6] the classification of fault detection methods for energy systems are presented. Fault detection methods can be classified into two subcategories: data-based and knowledge-based. The knowledge-based methods rely on domain and prior knowledge to deri...

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“…Ref. [ 21 ] considered energy-based fault detection in the petrochemical industries. However, the work is focused on showing that a list of common process faults can indeed be identified while using fixed thresholds.…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection for Individual Sequences with Applications in Identifying Malicious Tools

Siboni

Cohen

2020

Entropy

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Anomaly detection refers to the problem of identifying abnormal behaviour within a set of measurements. In many cases, one has some statistical model for normal data, and wishes to identify whether new data fit the model or not. However, in others, while there are normal data to learn from, there is no statistical model for this data, and there is no structured parameter set to estimate. Thus, one is forced to assume an individual sequences setup, where there is no given model or any guarantee that such a model exists. In this work, we propose a universal anomaly detection algorithm for one-dimensional time series that is able to learn the normal behaviour of systems and alert for abnormalities, without assuming anything on the normal data, or anything on the anomalies. The suggested method utilizes new information measures that were derived from the Lempel–Ziv (LZ) compression algorithm in order to optimally and efficiently learn the normal behaviour (during learning), and then estimate the likelihood of new data (during operation) and classify it accordingly. We apply the algorithm to key problems in computer security, as well as a benchmark anomaly detection data set, all using simple, single-feature time-indexed data. The first is detecting Botnets Command and Control (C&C) channels without deep inspection. We then apply it to the problems of malicious tools detection via system calls monitoring and data leakage identification.We conclude with the New York City (NYC) taxi data. Finally, while using information theoretic tools, we show that an attacker’s attempt to maliciously fool the detection system by trying to generate normal data is bound to fail, either due to a high probability of error or because of the need for huge amounts of resources.

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Application of Exergy-Based Fault Detection in a Gas-To-Liquids Process Plant

Cited by 12 publications

References 46 publications

A comparative study of energy graph-based fault detection and isolation techniques applied to a lignite plant

A comparative study of energy graph-based fault detection and isolation techniques applied to a lignite plant

An Energy Graph-Based Approach to Fault Diagnosis of a Transcritical CO2 Heat Pump

Anomaly Detection for Individual Sequences with Applications in Identifying Malicious Tools

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