Condition monitoring has emerged as an important research topic due to the need for timely remedial actions to maintain the safe and ongoing operation of machine drives. This paper describes a rule-based intelligent diagnostic system based on forward-chaining theory for online monitoring of critical sensors in permanent magnet synchronous motor (PMSM) drives. The proposed system assesses one speed sensor and two current sensors under field-oriented control (FOC). A signal-based technique is used to diagnose speed sensor defects while a hybrid model-based and signal-based approach is used to detect current sensor faults. The framework combines data-driven analysis with rule-based logic allowing for the speedy and precise identification and categorization of sensor faults without requiring big training datasets which are generally unrealistic for variable speed drives. This approach makes it especially suited for real-time applications. MATLAB-Simulink, utilizing a fixed-step discrete-time solver ensures efficient and accurate system performance simulation demonstrating that the rule-based intelligent framework effectively detects and monitors sensor faults, identifying speed sensor faults within 0.05–0.15 ms and current sensor faults within 1–4 ms under diverse scenarios.