Robust model-based fault diagnosis for air handling units

Mulumba, Timothy; Afshari, Afshin; Yan, Ke; Shen, Wen; Norford, Leslie K.

doi:10.1016/j.enbuild.2014.10.069

Cited by 117 publications

(54 citation statements)

References 41 publications

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“…Principal Component Analysis (PCA) [16,17,18], Statistical Process Control (SPC) [19,20,21], Multivariate Regression Models [22], Bayes Classifier [23,24,25], Neural Networks (NN) [26,27,28], Fisher Discriminant Analysis 40 (FDA) [29], Gaussion Mixture Model [30], Support Vector Data Description (SVDD) [31,32], and Support Vector Machines (SVM) [33, 34,35,36,37]. A c c e p t e d M a n u s c r i p t [13,38,39].…”

Section: Introductionmentioning

confidence: 99%

Fault detection and diagnosis for building cooling system with a tree-structured learning method

Zhou

et al. 2016

Energy and Buildings

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Section: Introductionmentioning

confidence: 99%

Fault detection and diagnosis for building cooling system with a tree-structured learning method

Zhou

et al. 2016

Energy and Buildings

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“…The resulting performance was considered excellent due to the fact that the training was conducted using fault data. For instance, the average fault diagnosis rate was 93.5% for eleven faults reported in Mulumba et al [31] and around 99.8% for nine faults reported in Yuwono et al [32]. The proposed method is appropriate in situations that fault data are unavailable.…”

Section: Discussionmentioning

confidence: 91%

Diagnostic Bayesian networks for diagnosing air handling units faults – Part II: Faults in coils and sensors

Zhao

Wen

Wang

2015

Applied Thermal Engineering

117

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“…In contrast, the meaningful clusters with the number of the data points 189 fewer than MinPts are likely to be neglected if MinPts is too large. The results from Ankerst 190 et al [28] showed that MinPts in the range of [10][11][12][13][14][15][16][17][18][19][20] The OPTICS algorithm starts with a random point and expands the visiting to its directly 211 density-connected neighbours, followed by sorting the points with the reachability-distance 212 visited so far in an ascending order and then expanding again from the first unexpanded point 213 in the order list. This process virtually walks through the Minimum Spanning Tree [35].…”

Section: Optics Cluster Analysis 179mentioning

confidence: 99%

A sensor fault detection strategy for air handling units using cluster analysis

Yan

Zhao

2016

Automation in Construction

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. (2016). A sensor fault detection strategy for air handling units using cluster analysis. Automation in Construction,[70][71][72][73][74][75][76][77][78][79][80][81][82][83][84][85][86][87][88] A sensor fault detection strategy for air handling units using cluster analysis AbstractSensors are an essential component in the control systems of air handling units (AHUs). A biased sensor reading could result in inappropriate control and thereby increased energy consumption or unsatisfied indoor thermal comfort. This paper presents an unsupervised learning based strategy using cluster analysis for AHU sensor fault detection. The historical data recorded from sensors is first pre-processed to reduce the dimensions using principal component analysis (PCA). The clustering algorithmOrdering Points to Identify the Clustering Structure (OPTICS) is then employed to identify the spatial separated data groups (i.e. clusters),which possibly indicate the occurrence of sensor faults. The data points in different clusters are then checked for temporal separation in order to confirm the occurrence of sensor faults. The proposed sensor fault detection strategy is tested and evaluated with the data collected from a simulation system. The results showed that this strategy can detect single and non-simultaneously occurred multiple sensor faults in AHUs. The fault detection results were not strongly affected by the selection of the user defined input parameters required in OPTICS. (AHUs). A biased sensor reading could result in inappropriate control and thereby increased 10 energy consumption or unsatisfied indoor thermal comfort. This paper presents an 11 unsupervised learning based strategy using cluster analysis for AHU sensor fault detection. 12The historical data recorded from sensors is first pre-processed to reduce the dimensions 13 using principal component analysis (PCA). The clustering algorithm Ordering Points to 14Identify the Clustering Structure (OPTICS) is then employed to identify the spatial separated 15 data groups (i.e. clusters), which possibly indicate the occurrence of sensor faults. The data 16 points in different clusters are then checked for temporal separation in order to confirm the 17 occurrence of sensor faults. The proposed sensor fault detection strategy is tested and 18 evaluated with the data collected from a simulation system. The results showed that this 19 strategy can detect single and non-simultaneously occurred multiple sensor faults in AHUs. 20 The fault detection results were not strongly affected by the selection of the user defined input 21 parameters required in OPTICS. 22

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Robust model-based fault diagnosis for air handling units

Cited by 117 publications

References 41 publications

Fault detection and diagnosis for building cooling system with a tree-structured learning method

Fault detection and diagnosis for building cooling system with a tree-structured learning method

Diagnostic Bayesian networks for diagnosing air handling units faults – Part II: Faults in coils and sensors

A sensor fault detection strategy for air handling units using cluster analysis

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