Building energy doctors: SPC and Kalman filter-based fault detection

Sun, Biao; Luh, Peter B.; O’Neill, Zheng; Song, Fangting

doi:10.1109/case.2011.6042429

Cited by 10 publications

(12 citation statements)

References 6 publications

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“…[3,4], we developed an FDD method which was data-driven and had good performance for water-cooled chillers. The method was based on gray-box models with insights and understanding of faults.…”

Section: Spc and Kalman Filter-based Methods For A Watercooled Chillermentioning

confidence: 99%

“…(1) used for the FDD of water-cooled chillers [3] cannot be applied to an aircooled chiller because of the time-varying air temperature and air flow rate. In Sect.…”

Section: Gray-box Model Improvement and Data Pre-processing To Satisfmentioning

confidence: 99%

“…2, the idea of SPC and Kalman filterbased FDD method in Refs. [3,4] is to view these parameters as constant state variables, and estimate them by using a Kalman filter. If there is no fault, then the estimated state variables should not change rapidly.…”

Section: Improvement Of Gray-box Modelmentioning

confidence: 99%

“…Since parameters predicted by gray-box models tend to be more robust than those by black-box models, graybox models have better potential for robust FDD, and can also provide insights and understanding of faults [5]. This method is developed and verified by us in another paper for a water-cooled chiller [3]. The verification was based on a simple and universal gray-box model that had parameters regarded as constants.…”

Section: Introductionmentioning

confidence: 97%

“…In Refs. [3,4], the fault detection and diagnosis for water-cooled chillers is discussed with the difficult that they worked under uncertain cooling load. In this paper, the FDD for aircooled chillers and the related sensors are studied, including both sudden faults and gradual degradations.…”

Section: Introductionmentioning

confidence: 99%

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SPC and Kalman filter-based fault detection and diagnosis for an air-cooled chiller

Sun

Luh

O’Neill³

2011

Front. Electr. Electron. Eng. China

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Buildings worldwide account for nearly 40% of global energy consumption. The biggest energy consumer in buildings is the heating, ventilation and air conditioning (HVAC) systems. In HVAC systems, chillers account for a major portion of the energy consumption. Maintaining chillers in good conditions through early fault detection and diagnosis is thus a critical issue.In this paper, the fault detection and diagnosis for an air-cooled chiller with air coming from outside in variable flow rates is studied. The problem is difficult since the air-cooled chiller is operating under major uncertainties including the cooling load, and the air temperature and flow rate. A potential method to overcome the difficulty caused by the uncertainties is to perform fault detection and diagnosis based on a gray-box model with parameters regarded as constants. The method is developed and verified by us in another paper for a water-cooled chiller with the uncertainty of cooling load. The verification used a Kalman filter to predict parameters of a gray-box model and statistical process control (SPC) for measuring and analyzing their variations for fault detection and diagnosis. The gray-box model in the method, however, requires that the air temperature and flow rate be nearly constant. By introducing two new parameters and deleting data points with low air flow rate, the requirement can be satisfied and the method can then be applicable for an air-cooled chiller. The simulation results show that the method with the revised model and some data points dropped improved the fault detection and diagnosis (FDD) performance greatly. It can detect both sudden and gradual air-cooled chiller capacity degradation and sensor faults as well as their recoveries.

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Section: Spc and Kalman Filter-based Methods For A Watercooled Chillermentioning

confidence: 99%

“…(1) used for the FDD of water-cooled chillers [3] cannot be applied to an aircooled chiller because of the time-varying air temperature and air flow rate. In Sect.…”

Section: Gray-box Model Improvement and Data Pre-processing To Satisfmentioning

confidence: 99%

Section: Improvement Of Gray-box Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

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