A Graph-Based Sensor Fault Detection and Diagnosis for Demand-Controlled Ventilation Systems Extracted from a Semantic Ontology

Mallak, Ahlam; Weber, Christian; Fathi, Madjid; Behravan, Ali; Obermaisser, Roman

doi:10.1109/ines.2018.8523895

Cited by 8 publications

(13 citation statements)

References 18 publications

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“…The work in [7] provided a graph-based FDD system for industrial systems using a model-based approach, based on creating a knowledge-base of the system under diagnosis, such as ontologies. Followed by manually feeding a set of static diagnostic rules created by the system expert, into the ontology, in a way that forms a causation relationship between the system sensors and the faults they lead to.…”

Section: Resultsmentioning

confidence: 99%

Section: Challenges and Problem Statementmentioning

confidence: 99%

“…This work is intended as an extension to the method in [7]. The algorithm in [7] demonstrates a model-based component FDD method based on using diagnostic graphs created by static/constant diagnostic rules extracted from semantic ontology. In other words, the system model represented by the ontology [8] is fed directly with the expert knowledge, and later used to generate diagnostic graphs that link between various symptoms and their faults.…”

Section: Challenges and Problem Statementmentioning

confidence: 99%

“…Finally, in this work, a development and extension of the work in [7] is proposed, by offering a dynamic and general-domain approach, with the possibility of deployment in distributed systems, represented in generating dynamic diagnostic graphs using RF.…”

Section: Our Contributionmentioning

confidence: 99%

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A Hybrid Approach: Dynamic Diagnostic Rules for Sensor Systems in Industry 4.0 Generated by Online Hyperparameter Tuned Random Forest

Mallak¹,

Fathi²

2020

Sci

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In this work, a hybrid component Fault Detection and Diagnosis (FDD) approach for industrial sensor systems is established and analyzed, to provide a hybrid schema that combines the advantages and eliminates the drawbacks of both model-based and data-driven methods of diagnosis. Moreover, it shines the light on a new utilization of Random Forest (RF) together with model-based diagnosis, beyond its ordinary data-driven application. RF is trained and hyperparameter tuned using three-fold cross validation over a random grid of parameters using random search, to finally generate diagnostic graphs as the dynamic, data-driven part of this system. This is followed by translating those graphs into model-based rules in the form of if-else statements, SQL queries or semantic queries such as SPARQL, in order to feed the dynamic rules into a structured model essential for further diagnosis. The RF hyperparameters are consistently updated online using the newly generated sensor data to maintain the dynamicity and accuracy of the generated graphs and rules thereafter. The architecture of the proposed method is demonstrated in a comprehensive manner, and the dynamic rules extraction phase is applied using a case study on condition monitoring of a hydraulic test rig using time-series multivariate sensor readings.

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Section: Resultsmentioning

confidence: 99%

Section: Challenges and Problem Statementmentioning

confidence: 99%

Section: Challenges and Problem Statementmentioning

confidence: 99%

Section: Our Contributionmentioning

confidence: 99%

See 2 more Smart Citations

A Hybrid Approach: Dynamic Diagnostic Rules for Sensor Systems in Industry 4.0 Generated by Online Hyperparameter Tuned Random Forest

Mallak¹,

Fathi²

2020

Sci

Self Cite

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“…By considering sensor faults as data deviations, FDD can accurately detect abnormal conditions. FDD for ventilation subsystems is also covered by [22] by using a graph-based approach.…”

Section: B Related Workmentioning

confidence: 99%

Data-Driven Modelling of Smart Building Ventilation Subsystem

et al. 2019

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Considering the advances in building monitoring and control through networks of interconnected devices, effective handling of the associated rich data streams is becoming an important challenge. In many situations the application of conventional system identification or approximate greybox models, partly theoretic and partly data-driven, is either unfeasible or unsuitable. The paper discusses and illustrates an application of black-box modelling achieved using data mining techniques with the purpose of smart building ventilation subsystem control. We present the implementation and evaluation of a data mining methodology on collected data over one year of operation. The case study is carried out on four air handling units of a modern campus building for preliminary decision support for facility managers. The data processing and learning framework is based on two steps: raw data streams are compressed using the Symbolic Aggregate Approximation method, followed by the resulting segments being input into a Support Vector Machine algorithm. The results are useful for deriving the behaviour of each equipment in various modi of operation and can be built upon for fault detection or energy efficiency applications. Challenges related to online operation within a commercial Building Management System are also discussed as the approach shows promise for deployment.

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