Abstract
Operational faults in centrifugal chillers will lead to high energy consumption, poor indoor thermal comfort, and low operational safety, and thus it is of significance to detect and diagnose the anomalies timely and effectively, especially for those at their incipient stages. The least squares support vector machine (LSSVM) has been regarded as an effective algorithm for multiclass classification. One of the most difficult issues in LSSVM is parameter tuning. Therefore, this paper reports a development of a gravitational search algorithm (GSA) optimized LSSVM method for incipient fault diagnosis in centrifugal chillers. Considering the inadequacies of conventional principle component analysis (PCA) algorithm for nonlinear data transformation, kernel principle component analysis (KPCA) was firstly employed to reduce the dimensionality of the original input data. Secondly, an optimized “one against one” multi-class LSSVM classifier was developed and its penalty constant and kernel bandwidth were tuned by GSA. Based on the fault samples of seven typical faults at their incipient stages in chillers from ASHRAE RP 1043, the proposed GSA optimized LSSVM fault diagnostic model was trained and validated. For the purpose of demonstrating the priority of the proposed fault diagnosis method, the obtained results were compared to that of using the LSSVM classifier optimized by another two algorithms, namely, the conventional cross-validation method and particle swarm optimizer. Results showed that the best fault diagnosis performance could be achieved using the proposed GSA-LSSVM classifier. The overall average fault diagnosis accuracy for the least severity faults was reported over 95%.
Highlights
Centrifugal chillers are one of the most widely used heating, ventilation and air conditioning (HVAC) systems in large-scale buildings for maintaining a desired indoor thermal environment
The proposed kernel principle component analysis (KPCA) based least squares support vector machine (LSSVM) (KPCA-LSSVM) fault diagnosis method with its parameters optimized by grid search with 10-fold cross validation was performed as the basis for comparison
This article reports on the development of an effective way of diagnosing chiller failure using KPCA based LSSVM classifier with its parameters being optimized by gravitational search algorithm (GSA)
Summary
Centrifugal chillers are one of the most widely used heating, ventilation and air conditioning (HVAC) systems in large-scale buildings for maintaining a desired indoor thermal environment. Half of the energy utilized in commercial buildings is used to maintain indoor thermal comfort (Enteria and Mizutani, 2011). Water chillers are more beneficial in large-scale buildings than direct expansion (DX) type A/C systems in terms of a larger operational range, higher system efficiency, and better part-load characteristic. Almost five million water chillers were in use in China at the end of 2017 (IEA, 2019), and space air conditioning (A/C) accounted for nearly 9.2% of total. Chiller Fault Diagnosis Using LSSVM building energy consumption in 2016 (IEA, 2018). Chiller failures are one of the most common problems in building automation systems, lowering system reliability and reducing energy efficiency. It is critical to detect and diagnose chiller abnormalities as soon as possible for energy saving of buildings
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