This research presents the design and implementation of a fault diagnosis filter for a high-fidelity simulation model of the AB383 wire bonder. Fault diagnosis, which consists of detecting, isolating, and estimating faults, enables more effective maintenance strategies and potentially mitigates costly downtime in high-precision motion and positioning systems. When a system deviates from its expected behavior, it can be an indication of the presence of a fault within the system. Faults can occur as either multiplicative faults, arising from deviations in parameters within the system, or additive faults, resulting from external fault signals that impact the system’s operation. This study uses a model-based methodology that generates residuals that are subsequently analyzed using a regression method aimed at determining the influence of each fault in the residual signal, thereby facilitating fault estimation. A residual signal represents the difference between the actual system behavior and the expected behavior, mainly serving as an indicator of potential faults within the system. A data-driven threshold design is proposed to determine the detectability of faults. The main contributions of this work include the development of a fault modeling framework for the residual generation method and the application of the fault estimation framework to a linear high-fidelity simulation model affected by both multiplicative and additive faults. The simulation results demonstrate satisfactory performance, that is, accurately detecting and estimating faults. Moreover, the proposed method effectively identifies external disturbances and high levels of noise through power spectral density analysis. The findings highlight the potential of this approach, outperforming alternative methods in terms of accuracy when it comes to fault diagnosis for wire bonder machines. The research findings contribute to the active field of fault detection and estimation for complex systems, offering valuable insights for further studies and potential practical applications.