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Sensor faults in heating, ventilation, and air conditioning (HVAC) systems are inevitable and result in significant energy waste. This paper presents an innovative data-driven approach for sensor fault detection and isolation in multi-zone HVAC systems. The proposed solution integrates bilinear Koopman model realization, deep learning, and bilinear parity-space. A deep neural network realizes a bilinear model, enabling bilinear parity-space sensor fault detection and isolation. This yields a reliable, accurate, and interpretable data-driven framework. The method requires no prior HVAC dynamics knowledge, relying solely on normal operation data. It diagnoses additive, multiplicative, and complete failure sensor faults while minimizing false alarms, even with severe faults. A four-zone HVAC system is simulated in TRNSYS as a case study to demonstrate the performance and efficacy of the proposed approach. The …