Structural health monitoring (SHM) techniques use a variety of sensors, such as displacement, strain, and acceleration sensors, to assess the current condition of engineering structures that are designed to last for decades. Over time, structures can experience degradation-related damage, while the monitoring systems themselves can age and degrade, becoming less reliable. This aging can lead to sensor malfunctions that produce plausible but incorrect data, leading to misinterpretations of structural integrity and potentially catastrophic failures. Therefore, it is critical to distinguish between sensor anomalies and structural anomalies to ensure robust SHM throughout the life cycle of the structure. To address this issue, this study introduces a two-step redundancy approach. First, strain gauges were aged in a climate chamber in laboratory experiments to determine the time-variant behavior of the measurement system. Measurement drift and physical gain were identified as significant changes in sensor performance. Second, the redundancy approach using correlation analysis and Gaussian process regression was used to effectively detect and isolate these sensor anomalies. The method successfully distinguished between sensor and structural anomalies and proved to be robust in various scenarios. Further research is suggested to explore the reliability of this approach under conditions where structural and sensor anomalies coincide. This study enhances the robustness of SHM systems and supports reliable assessment of structural health over their lifetime.