Enhanced chiller sensor fault detection, diagnosis and estimation using wavelet analysis and principal component analysis methods

Xu, Xin; Xiao, Fu; Wang, Shengwei

doi:10.1016/j.applthermaleng.2007.03.021

Cited by 105 publications

(20 citation statements)

References 24 publications

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“…The quantity of chillers is assumed to be 20. The rated capacity (kW) of chillers 1-20 is assumed to be 15,20,33,41,57,69,15,12,10,24,12,15,12,69,15,12,33, 41, 57, and 69, respectively. To simplify the calculation, the cost , = 1, 2, .…”

Section: Simulation Resultsmentioning

confidence: 99%

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Decentralized Optimization for a Novel Control Structure of HVAC System

Wang

Xing

Jiang

et al. 2016

Mathematical Problems in Engineering

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A decentralized control structure is introduced into the heating, ventilation, and air conditioning (HVAC) system to solve the high maintenance and labor cost problem in actual engineering. Based on this new control system, a decentralized optimization method is presented for sensor fault repair and optimal group control of HVAC equipment. Convergence property of the novel method is theoretically analyzed considering both convex and nonconvex systems with constraints. In this decentralized control system, traditional device is fitted with a control chip such that it becomes a smart device. The smart device can communicate and operate collaboratively with the other devices to accomplish some designated tasks. The effectiveness of the presented method is verified by simulations and hardware tests.

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Section: Simulation Resultsmentioning

confidence: 99%

“…Under the conventional HVAC control structure, the majority of sensor fault detection and diagnosis (SFDD) methods are based on a centralized algorithm [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. To the best of our knowledge, these methods have a series of disadvantages, such as poor adaptability and instantaneity.…”

Section: Introductionmentioning

confidence: 99%

Decentralized Optimization for a Novel Control Structure of HVAC System

Wang

Xing

Jiang

et al. 2016

Mathematical Problems in Engineering

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“…1 (τ ), u (I) (τ )) dτ (31) Consider that there are: i) a known boundx (I) such that x (I) (0) ≤x (I) , and ii) positive constants ρ (I) , ξ (I) such that e A (I) L t ≤ ρ (I) e −ξ (I) t for all t. Moreover, given (7), (8) and (14), (15) under healthy conditions it yields:…”

Section: B Computation Of Adaptive Thresholdsmentioning

confidence: 99%

Distributed detection and isolation of sensor faults in HVAC systems

Reppa

Papadopoulos

Polycarpou

2013

21st Mediterranean Conference on Control and Automation

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This paper presents the design of a methodology for distributed detection and isolation of sensor faults in heating, ventilating and airconditioning (HVAC) systems. The proposed sensor fault detection and isolation (SFDI) method is developed in a distributed framework with the HVAC system modeled as a set of interconnected, nonlinear subsystems. For each of the interconnected subsystems, we design a model-based SFDI module that uses input and sensor output data of its underlying subsystems, as well as sensor output data of its neighboring, interconnected subsystems. The distributed sensor fault detection is conducted in each SFDI module, using robust analytical redundancy relations, formulated by estimationbased residuals and adaptive thresholds. The distributed sensor fault isolation is carried out by applying a diagnostic reasoningbased decision logic, taking into account multiple sensor faults. Simulation results are used for illustrating the effectiveness of the proposed methodology applied to a two-zone HVAC system.

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“…PCA can be used to detect faults using the statistical analysis, and to diagnose sensor faults using the reconstruction algorithms. It has been applied to an air-handling unit sensor FDD (Wang and Xiao 2004;Du and Jin 2008), variable air volume system sensor FDD Du and Jin 2008;Du et al 2009aDu et al , 2009b, an air distribution loop FDD (Xiao et al 2006), a system level FDD Zhou et al 2009), and a chiller sensor FDD Cui 2005, 2006;Xu et al 2008). Generally, variables measured on chillers are highly non-Gaussian distributed, nonlinear, and wide-range distributed.…”

Section: Introductionmentioning

confidence: 99%

A robust pattern recognition-based fault detection and diagnosis (FDD) method for chillers

et al. 2014

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A new chiller fault detection and diagnosis (FDD) method is proposed in this article. Different from conventional chiller FDD methods, this article considers the FDD problem as a typical one-class classification problem. The fault-free data are classified as the fault-free class. Data of a fault type are regarded as a fault class. The task of fault detection is to detect whether the process data are outliers of the fault-free class. The task of fault diagnosis is to find to which fault class does the process data belong. In this study, support vector data description (SVDD) algorithm is introduced for the one-class classification. The basic idea of the SVDD-based method is to find a minimum-volume hypersphere in a high dimensional feature space to enclose most of the data of an individual class. The proposed method is validated using the ASHRAE RP-1043 (Comstock and Braun 1999) experimental data. It shows more powerful FDD capacity than multi-class SVM-based FDD methods and PCA-based fault detection methods. Four potential applications of the proposed method are also discussed.

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Enhanced chiller sensor fault detection, diagnosis and estimation using wavelet analysis and principal component analysis methods

Cited by 105 publications

References 24 publications

Decentralized Optimization for a Novel Control Structure of HVAC System

Decentralized Optimization for a Novel Control Structure of HVAC System

Distributed detection and isolation of sensor faults in HVAC systems

A robust pattern recognition-based fault detection and diagnosis (FDD) method for chillers

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