Recurrence-Based Techniques for Data-Driven Fault Diagnosis and Monitoring in Neutral-Point-Clamped Inverters

The high cost and operational complexity of inverter technology make system reliability a key concern, which can be enhanced through advanced monitoring techniques. This paper introduces a novel data-driven condition monitoring (CM) approach for Neutral-Point-Clamped (NPC) inverters based on Recurrence Plot Analysis (RPA) and Recurrence Quantification Analysis (RQA). Unlike conventional deep learning-based approaches, the proposed method requires minimal data, offers high interpretability, and enhances the feature extraction process by capturing nonlinear dynamics and hidden patterns in the time-series data, which are often not accessible through traditional statistical or spectral techniques. The study investigates both normal operation with varying load conditions and open-circuit faults in power switches. A detailed theoretical background on RPA is provided, emphasizing the impact of parameter selection on performance. RQA metrics are extracted from PWM voltage and current signals and employed as diagnostic features to identify operating conditions and fault states. The proposed approach is experimentally validated using a laboratory prototype, confirming its effectiveness in fault detection and classification. Three operating conditions were evaluated: an RL load, an induction motor, and an open-circuit fault scenario. The experimental results demonstrate that the proposed method achieves high diagnostic accuracy (up to 100%), exhibits strong robustness to noise, and maintains consistent classification performance across varying load conditions. By employing a decision tree classifier applied to RQA metrics, the approach effectively distinguishes between healthy and faulty states, providing an interpretable solution for real-time fault diagnosis. These innovations position the proposed technique as accurate, and hardware-friendly alternative to complex neural-network-based solutions. These findings highlight the method’s potential for integration into practical data-driven fault diagnosis frameworks for power electronic converters.