2019 IEEE International Conference on Prognostics and Health Management (ICPHM) 2019
DOI: 10.1109/icphm.2019.8819438
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Abstract: Early detection of incipient faults is of vital importance to reducing maintenance costs, saving energy, and enhancing occupant comfort in buildings. Popular supervised learning models such as deep neural networks are considered promising due to their ability to directly learn from labeled fault data; however, it is known that the performance of supervised learning approaches highly relies on the availability and quality of labeled training data. In Fault Detection and Diagnosis (FDD) applications, the lack of… Show more

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Cited by 22 publications
(22 citation statements)
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“…If physically redundant sensors are not installed, analytical redundancy approaches are applied that estimate sensor error from the relation between a range of different target sensors. These approaches can be classified into model-based [9,10], knowledge-based [11][12][13], and data-driven methods [14][15][16][17] depending on the understanding of the depth of the system and the available amount of data. Jiang et al (2011) demonstrated this classification of general sensor fault detection [18].…”
Section: Sensor Fault Detection and Identificationmentioning
confidence: 99%
“…If physically redundant sensors are not installed, analytical redundancy approaches are applied that estimate sensor error from the relation between a range of different target sensors. These approaches can be classified into model-based [9,10], knowledge-based [11][12][13], and data-driven methods [14][15][16][17] depending on the understanding of the depth of the system and the available amount of data. Jiang et al (2011) demonstrated this classification of general sensor fault detection [18].…”
Section: Sensor Fault Detection and Identificationmentioning
confidence: 99%
“…A review of the literature reveals that data-driven approaches relying on supervised learning have demonstrated promising results in various applications, e.g. Fault Detection and Diagnosis (FDD) in air conditioning systems [9], [13], [14].…”
Section: A Learning-based Anomaly Detectionmentioning
confidence: 99%
“…This is a limitation of supervised methods because it is almost impossible to obtain every possible type of anomaly that could happen on a system. To address this problem, Jin et al recently proposed a FDD method that uses Monte-Carlo dropout [9] to estimate the prediction uncertainty of deep neural networks. The method was applied to the identification of incipient faults that are not represented in the training data that only consists of labeled data of normal and severe faults.…”
Section: A Learning-based Anomaly Detectionmentioning
confidence: 99%
“…Fault detection and diagnosis (FDD) in general is classified into three categories: model based, signal based and data based [13]. The first and the second categories require system experts and at the same time is costly because of which these methods become inconvenient for complex systems.…”
Section: Introductionmentioning
confidence: 99%
“…The deep neural network is one of the machine learning techniques which is reliable and effective in indicating incipient faults [13]. It is a supervised learning method which requires labelled data for training.…”
Section: Introductionmentioning
confidence: 99%