## 3 Cutting-Edge Online Fault Diagnosis Techniques for Al-Caps
### Comparing Online Fault Diagnosis Methods  
Most SB-ON-FDTs are invasive, as they rely on current sensors. Further, many of these techniques depend on a sensor in the capacitor branch, which makes them impractical for real-world applications due to space constraints and potential operational limitations. MB-ON-FDTs generally require high sampling frequencies and involve complex models, which can vary depending on the converter topology. Additionally, MB-ON-FDTs typically demand access to the converter internal state variables, which makes these approaches intrinsically invasive by nature. On the other hand, DB-ON-FDTs, particularly those based on deep learning, offer excellent performance but come with significant drawbacks. They require large training datasets and substantial computational resources, which poses a considerable challenge to their integration into commercial systems. It is worth noting that the aforementioned issue related to the generation of large datasets can be substantially mitigated by implementing the proposed approach, which simply involves adapting the pipeline to accommodate a deep learning model. This paper presents an innovative solution to address these aforementioned limitations by integrating signal processing techniques (SPTs) with traditional machine learning algorithms (TMLs) in order to evaluate the state of capacitors used in the DC-link of PCs. Although TML models require preliminary data preprocessing, they offer substantial advantages over deep learning approaches, including the ability to learn effectively from significantly smaller training datasets with a lower computational cost. Furthermore, in contrast to SB-ON-FDTs and MB-ON-FDTs, TML-DB-ON-FDTs enable the implementation of non-invasive and efficient FDTs using features derived from the converter input and output signals, eliminating the need for integrating internal sensors into the converter system. Specifically, a logistic regression (LR) model is used to evaluate the health status of an al-cap used in the output filter of a buck converter. The proposed methodology only uses the input and output currents of the converter, together with the output voltage, thus constituting a non-invasive approach. The proposed solution has a minimal computational cost, which occurs exclusively during the training phase, since the LR generates a mathematical equation and a decision criterion that allows the evaluation of the capacitor condition. Once the equation and decision criterion are established, implementing this solution requires minimal computational resources and can be readily integrated into microcontroller-based systems. The development of the proposed FDT seeks to provide a computationally efficient, non-invasive, and scalable solution that is well-suited for practical implementation in real-world applications. Proposed Online Fault Diagnosis Technique for AI-Caps Using Traditional Machine Learning Algorithms  
This paper proposes a traditional machine learning data-based online fault diagnosis technique (TML-DB-ON-FDT) that relies solely on input and output signal measurements, eliminating the need for internal sensors within the converter and ensuring independence from the converter model. The method employs logistic regression (LR), a lightweight ML algorithm that requires far less data than artificial neural networks (ANNs), to learn patterns and detect when capacitor replacement is necessary. Once trained, the FDT model is represented as a simple mathematical equation combined with a condition, which makes it highly efficient and significantly reduces the computational cost and processing time compared to alternative approaches. Traditional ML algorithms cannot operate directly on raw data, requiring prior feature extraction from the given raw data. Therefore, as the ML algorithm used is LR, it is necessaryto first extract features from the raw data. Thus, the proposed solution is composed of two fundamental components: the signal processing module (SPM) and the information processing module (IPM).