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To solve data imbalance and insufficient prior knowledge problems in using quick access recorder （QAR）for health management and abnormal detection of large passenger aircraft air⁃conditioning system, this paper carries out an algorithm based on improved fuzzy C⁃means （FCM） to evaluate components degradation status. This algorithm is to calculate the distance between fault state and normal state. Based on the physical characteristics of large passenger aircraft’s air⁃conditioning system, the distance function of FCM algorithm is improved, and the difference of the left and the right packs is adopted as the evaluation standard. The overfitting problem of current methods is effectively solved, and the improved FCM algorithm is more sensitive to the degradation of the components in the early stage. It can also effectively reflect the intermediate process of performance degradation. This study provids strong technical support for airlines in arranging maintenance plan and reducing operation cost. [ABSTRACT FROM AUTHOR]

• A decentralized control method for multi-split air conditioning system is proposed. • Up to 8.6% energy savings are achieved with the proposed control strategy. • The EOTF-based PI controller has better robustness than model predictive controller. Multi-evaporator air conditioning system has found increasing applications in buildings. Energy-efficient control for this kind of system presents significant challenge because of strong coupling effects and various operation modes. Existing control methods are most likely to encounter uncontrollable problems and lack robustness under uncertain operation conditions. This paper proposes an energy-efficient decentralized control method with enhanced robustness for multi-evaporator air conditioning system. The proposed method is featured by a self-optimizing control strategy and a decentralized proportional-integral (PI) control algorithm based on the effective open-loop transfer function (EOTF). Different from most of previous studies that regulated compressor speed to control the evaporating pressure by using a supervisory optimizer, the proposed self-optimizing control strategy regulates compressor speed to maintain constant suction superheat and achieves energy-efficient operation in a simple and robust manner. Meanwhile, the EOTF-based decentralized PI algorithm overcomes the weakness of decentralized controllers in dealing with coupling effects, while retaining robustness against model errors and component faults. Control performance of the proposed method is tested using a validated system dynamic model. Through extensive controllability tests, it is found that the proposed control method significantly improves temperature control performance of the conventional strategy. Up to 8.6% energy savings are achieved with the proposed control strategy. Moreover, the proposed EOTF-based PI controller demonstrates enhanced robustness in comparison with an advanced model predictive controller. [ABSTRACT FROM AUTHOR]