Genetic algorithm-aided ensemble model for sensor fault detection and diagnosis of air-cooled chiller system

Due to the change of environment and operation conditions, sensor data in chiller system show the coexistence of gaussianity, non-gaussianity, non-linearity, and dynamic characteristic. Most of the existing methods can only be used for fault diagnosis based on a specific assumption, such as linearity or gaussianity. Therefore, this paper proposes a genetic algorithm (GA)-aided ensemble model without any characteristic assumption to detect and diagnose sensor fault in air-cooled chiller. Different from conventional techniques, the proposed method improves the detection and diagnosis performances by considering the Gaussian, non-Gaussian, non-linear, and dynamic characteristics, simultaneously. Firstly, several statistical models are analyzed and selected as the sub-models in our ensemble framework. The underlying assumptions for these models are different so that all the above characteristics can be considered. Then, an ensemble detection framework and an ensemble diagnosis framework based on GA are proposed to adapt to the complex data characteristics. To maximize the fault detection rate and diagnosis accuracy, two fitness functions of GA are proposed for detection and diagnosis, respectively. Finally, an integrated statistic and an integrated contribution plot are proposed based on the GA-aided ensemble framework. The detection and diagnosis performances of the ensemble statistic and the ensemble contribution plot are validated by using the data collected from a real chiller system. Results show that the proposed method delivers superior performance compared with other methods.