<i>Review Article</i>
            : Methods for Fault Detection, Diagnostics, and Prognostics for Building Systems—A Review, Part I

Katipamula, Srinivas; Brambley, Michael R.

doi:10.1080/10789669.2005.10391123

Cited by 712 publications

(311 citation statements)

References 56 publications

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“…Zur Einordnung der im Rahmen dieser Arbeit entwickelten Verfahren werden diese grundlegenden Ansätze im Folgenden kurz erörtert, ausführlichere Betrachtungen finden sich zum Beispiel in [118].…”

Section: Residuenerzeugungunclassified

Entwurf robuster modellbasierter Fehlerisolationsfilter

Wahrburg

2014

At - Automatisierungstechnik

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Section: Residuenerzeugungunclassified

Entwurf robuster modellbasierter Fehlerisolationsfilter

Wahrburg

2014

At - Automatisierungstechnik

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“…Two relevant tasks for predictive maintenance are failure detection and failure prediction. For the purpose of early failure detection, techniques from novelty, anomaly and outlier detection have been used (Basseville 1988;Basseville and Nikiforov 1993;Patton et al 2010;Papadimitropoulos et al 2007;Katipamula and Brambley 2005;Yilboga et al 2010).…”

Section: Related Workmentioning

confidence: 99%

Sequential anomalies: a study in the Railway Industry

2016

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Concerned with predicting equipment failures, predictive maintenance has a high impact both at a technical and at a financial level. Most modern equipments have logging systems that allow us to collect a diversity of data regarding their operation and health. Using data mining models for anomaly and novelty detection enables us to explore those datasets, building predictive systems that can detect and issue an alert when a failure starts evolving, avoiding the unknown development up to breakdown. In the present case, we use a failure detection system to predict train door breakdowns before they happen using data from their logging system. We use sensor data from pneumatic valves that control the open and close cycles of a door. Still, the failure of a cycle does not necessarily indicates a breakdown. A cycle might fail due to user interaction. The goal of this study is to detect structural failures in the automatic train door system, not when there is a cycle failure, but when there are sequences of cycle failures. We study three methods for such structural failure detection: outlier detection, anomaly detection and novelty detection, using different windowing strategies. We propose a two-stage approach, where the output of a point-anomaly algorithm is post-processed by a low-pass filter to obtain a subsequence-anomaly detection. The main result of the two-level architecture is a strong impact in the false alarm rate.

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“…Examples of research efforts include the deployment of field-scale monitoring systems (Piette et al, 2001) and development of automated diagnostics of HVAC systems Brambley, 2005a and2005b).…”

Section: Sensor Systems In the Built Environmentmentioning

confidence: 99%

Bayesian based design of real-time sensor systems for high-risk indoor contaminants

Sreedharan¹

2007

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The sudden release of toxic contaminants that reach indoor spaces can be hazardous to building occupants. To respond effectively, the contaminant release must be quickly detected and characterized to determine unobserved parameters, such as release location and strength. Characterizing the release requires solving an inverse problem. Designing a robust real-time sensor system that solves the inverse problem is challenging because the fate and transport of contaminants is complex, sensor information is limited and imperfect, and real-time estimation is computationally constrained.This dissertation uses a system-level approach, based on a Bayes Monte Carlo framework, to develop sensor-system design concepts and methods. I describe three investigations that explore complex relationships among sensors, network architecture, interpretation algorithms, and system performance. The investigations use data obtained from tracer gas experiments conducted in a real building.The influence of individual sensor characteristics on the sensor-system performance for binary-type contaminant sensors is analyzed. Performance tradeoffs among sensor 2 accuracy, threshold level and response time are identified; these attributes could not be inferred without a system-level analysis. For example, more accurate but slower sensors are found to outperform less accurate but faster sensors.Secondly, I investigate how the sensor-system performance can be understood in terms of contaminant transport processes and the model representation that is used to solve the inverse problem. The determination of release location and mass are shown to be related to and constrained by transport and mixing time scales. These time scales explain performance differences among different sensor networks. For example, the effect of longer sensor response times is comparably less for releases with longer mixing time scales.The third investigation explores how information fusion from heterogeneous sensors may improve the sensor-system performance and offset the need for more contaminant sensors. Physics-and algorithm-based frameworks are presented for selecting and fusing information from noncontaminant sensors. The frameworks are demonstrated with door-position sensors, which are found to be more useful in natural airflow conditions, but which cannot compensate for poor placement of contaminant sensors.The concepts and empirical findings have the potential to help in the design of sensor systems for more complex building systems. The research has broader relevance to additional environmental monitoring problems, fault detection and diagnostics, and system design. i

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Review Article : Methods for Fault Detection, Diagnostics, and Prognostics for Building Systems—A Review, Part I

Cited by 712 publications

References 56 publications

Entwurf robuster modellbasierter Fehlerisolationsfilter

Entwurf robuster modellbasierter Fehlerisolationsfilter

Sequential anomalies: a study in the Railway Industry

Bayesian based design of real-time sensor systems for high-risk indoor contaminants

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