Robust on-line fault detection diagnosis for HVAC components based on nonlinear state estimation techniques

Bonvini, Marco; Sohn, Michael D.; Granderson, Jessica; Wetter, Michael; Piette, Mary Ann

doi:10.1016/j.apenergy.2014.03.009

Cited by 113 publications

(45 citation statements)

References 18 publications

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“…Bonvini et al [37] proposed a nonlinear state-space model using unscented Kalman Filtering (UKF) and performed FDD based on parameter variations.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Robust model-based fault diagnosis for air handling units

Mulumba¹,

Afshari²,

Yan³

et al. 2015

Energy and Buildings

112

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“…Bonvini et al [37] proposed a nonlinear state-space model using unscented Kalman Filtering (UKF) and performed FDD based on parameter variations.…”

Section: Related Workmentioning

confidence: 99%

“…Various approaches are utilized to estimate parameters online, such as ARX [32,33], recursive least squares [34][35][36] and Kalman Filtering [37,38]. After the construction of these models, measured data is converted into a set of residuals, parameters or performance indices to enhance FDD accuracy.…”

Section: Related Workmentioning

confidence: 99%

Robust model-based fault diagnosis for air handling units

Mulumba¹,

Afshari²,

Yan³

et al. 2015

Energy and Buildings

112

View full text Add to dashboard Cite

“…To facilitate the transition towards reducing the energy consumption and the green-house gas emissions, mathematical models of buildings have been intensively used. The simulation tools available today [10] and the ongoing research offer a high variety of possibilities when it comes to design optimization [21], renewable integration [41], retrofitting [14], on-line fault detection diagnosis [7] and even optimized real-time control.…”

Section: Introductionmentioning

confidence: 99%

On-line parameter and state estimation of an air handling unit model: experimental results using the modulating function method

Ionesi

Ramezani²,

Jouffroy

2019

MIC

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This paper considers the on-line implementation of the modulating function method, for parameter and state estimation, for the model of an air-handling unit, central element of HVAC systems. After recalling the few elements of the method, more attention is paid on issues related to its on-line implementation, issues for which we use two different techniques. Experimental results are obtained after implementation of the algorithms on a heat flow experiment, and they are compared with conventional techniques (conventional tools from Matlab for parameter estimation, and a simple Luenberger observer for state estimation) for their validation. * ai@mci.sdu.dk † ramezani@mci.sdu.dk ‡ jerome@mci.sdu.dk arXiv:1810.08929v2 [cs.SY] 15 Nov 2018 state and parameter estimation on a continuous-time ROM based on a simple thermal-electrical analogy [15]. In comparison with more conventional approaches using discrete-time such as Kalman filtering, performing both state and parameter estimation in continuous-time relates better to the formalism in which the system is originally modeled and ensures the convergence of the estimates to the real values when sampling time approaches zero [39]. Compared to their discrete-time counterparts estimating either parameters or state components is the necessity of considering time derivatives. The modulating function technique, originally introduced by Shinbrot [38], allows to circumvent this issue elegantly, with the use of fixed-length time integrals of the measured signals, akin continuous-time FIR filtering. This offers the additional advantages of giving estimates after a fixed and predetermined amount of time, contrary to usual Kalman-based filtering methods. Originally proposed towards parameter estimation, the modulating function method was more recently extended to include state estimation [23] [19].Many studies considering the modulating function approach showed satisfactory performance, but mostly in simulations using artificial data [19] or sources generated from real profiles [5]. In contrast to that and to the best of our knowledge, not many applications can be found in the literature dealing with experiments done on actual measurements, to the notable exception of [11], where Hartley modulating functions are used to estimate the parameters of a thyristor driven DC-motor.

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“…Several researchers have proposed FD schemes that tackle the problem of process faults [Bonvini et al (2014)], or actuator faults [Weimer et al (2012); CarbotRojas et al (2015); Darure et al (2016)] or sensor faults [Wang et al (2010); Reppa et al (2015)]. In practice however it is not known a priori what type of fault will occur.…”

Section: Introductionmentioning

confidence: 99%

Distributed Diagnosis of Actuator and Sensor Faults in HVAC Systems

2017

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This paper presents a model-based methodology for diagnosing actuator and sensor faults affecting the temperature dynamics of a multi-zone heating, ventilating and airconditioning (HVAC) system. By considering the temperature dynamics of the HVAC system as a network of interconnected subsystems, a distributed fault diagnosis architecture is proposed. For every subsystem, we design a monitoring agent that combines local and transmitted information from its neighboring agents in order to provide a decision on the type, number and location of the faults. The diagnosis process of each agent is realized in three steps. Firstly, the agent performs fault detection using a distributed nonlinear estimator. After the detection, the local fault identification is activated to infer the type of the fault using two distributed adaptive estimation schemes and a combinatorial decision logic. In order to distinguish between multiple local faults and propagated sensor faults, a distributed fault isolation is applied using the decisions of the neighboring agents. Simulation results of a 5-zone HVAC system are used to illustrate the effectiveness of the proposed methodology.

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Robust on-line fault detection diagnosis for HVAC components based on nonlinear state estimation techniques

Cited by 113 publications

References 18 publications

Robust model-based fault diagnosis for air handling units

Robust model-based fault diagnosis for air handling units

On-line parameter and state estimation of an air handling unit model: experimental results using the modulating function method

Distributed Diagnosis of Actuator and Sensor Faults in HVAC Systems

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