This study proposes an enhanced Kalman filtering algorithm that integrates sensor fault tolerance with a Send-on-Delta (SOD) event-triggered mechanism to address state estimation in complex systems subject to sensor faults and constrained communication resources. A system model incorporating sensor faults is established, and an upper bound for the error covariance matrix is theoretically derived, leading to optimized filter gain. The simulation results verify that the proposed algorithm significantly improves estimation accuracy compared to conventional Kalman filtering under simultaneous sensor faults and SOD-based communication, providing an efficient and reliable state estimation framework for practical applications.