Heating, Ventilation, and Air Conditioning (HVAC) systems are pivotal to the energy footprint, environmental performance, and health outcomes of buildings. Demand-Controlled Ventilation (DCV) dynamically modulates outdoor-air intake based on occupancy or Indoor Air Quality (IAQ) indicators (often CO₂), and is widely promoted as a pathway to balance energy efficiency with occupant wellbeing. Yet, in practice, large-scale HVAC deployments are frequently hampered by undetected or persistent faults in sensors and actuators, leading to energy waste, thermal discomfort, and IAQ lapses. While the literature includes robust streams on simulation-based fault injection (FI), fault detection and diagnosis (FDD), model-based testing (MBT), and commissioning, there remains a persistent disconnect between simulated faults and the concrete maintenance workflows that technicians actually execute in the field. We address this gap with an integrated framework that couples a composable, component-based FI model with a maintenance-oriented test generator and a test-team simulation capability. Implemented in MATLAB/Simulink (with Stateflow and Simscape), our approach allows users to specify multi-fault scenarios via a GUI; automatically maps each injected fault to manufacturer-style maintenance tests; and allocates those tests to simulated teams based on building topology. The framework outputs (i) a Maintenance Test & Testing Team Table that traces fault types to failed tests and team assignments, and (ii) a Final Test Status Table that consolidates pass/fail per component. We validate the approach on a 6-room, 2-floor DCV–heating model with scenarios spanning intermittent data-loss and stuck-at faults for CO₂ and temperature sensors, damper actuators, and heaters, under varied IAQ and weather conditions. Results show that injected faults consistently produce the expected failed tests across scenarios, with no false failures on healthy components, demonstrating a reliable bridge from FI to maintenance planning. Compared to prior work, our innovation is to close the loop from simulation to actionable test lists and team logistics, thereby accelerating corrective actions and improving maintainability of DCV/HVAC systems at scale.