## 3 Cutting-Edge Online Fault Diagnosis Techniques for Al-Caps
### Data-Based Online Fault Diagnosis Techniques (MB-ON-FDTs)  
Data-based online fault diagnosis techniques (DB-ON-FDTs) can be classified into two categories: deep learning DB-ON-FDTs (DL-DB-ON-FDTs) and traditional ML DB-ON-FDTs (TML-DB-ON-FDTs). The DL-DB-ON-FDTs require a very substantial volume of training data, for which some solutions will be presented below. Although DL algorithms are capable of operating directly on raw data, the following solutions require data preprocessing. Consequently, it is essential to extract specific features from the sampled signals prior to model training. The first approaches address the problem as a regression task, wherein the dependent variable corresponds to the capacitor capacitance value. The first solution proposes the implementation of an artificial neural network (ANN) to estimate the DC-link capacitor capacitance in a back-to-back converter [44]. The features correspond to the RMS values of the input and output voltage and current of phase A together with DC-link voltage, with the C value being the target. Subsequently, in [45], the number of inputs to the ANN is reduced to two, namely the RMS value of a single-phase output current and the voltage ripple across the DC-link. In [46], the authors propose a feature set comprising a combination of DC-link voltage harmonics. In [47], an adaptive neuro-fuzzy inference system (ANFIS) is employed to evaluate the condition of the capacitors within a wind power system supplying a single-phase electrical grid. One capacitor is positioned at the output of the three-phase rectifier, while the other is situated between the boost converter and the input of the single-phase inverter. The input features for the ANFIS model include the converter's input voltage and the voltages across the terminals of both capacitors. Unlike the previously discussed approaches, which address the problem as a regression task, this method formulates it as a classification problem. Accordingly, the ANFIS output identifies one of three possible states: normal operation, failure of the first capacitor, or failure of the second capacitor. Subsequently, in [17], the authors employ an autoencoder trained exclusively on data from a healthy 3 dIBC, which enables the accurate reconstruction of the input signal under healthy conditions. As expected, the reconstruction error remains low in healthy scenarios; however, when the autoencoder processes input data from a system with aged capacitors, the reconstruction error increases significantly, thereby enabling fault detection. The inputs to the autoencoder include the output voltage amplitude, which are extracted using the short-time least squares Prony (STLSP) algorithm, the load resistance, and the average output voltage. Although the autoencoder is provided with three inputs, it produces a single output: the reconstructed amplitude of the output voltage. The other two inputs play a crucial role in enabling the autoencoder to identify the operational state of the converter. Deviations in the reconstruction error serve as a reliable indicator of the capacitor's health status. Recently, in [48], the authors introduced a new DL-DB-ON-FDT that uses raw data as its input, specifically the voltage across the DC-link capacitor of a three-phase PWM inverter for induction motor drives. To estimate both the ESR and C values of capacitors, a hybrid convolutional neural network (HCNN) with an attention mechanism is employed. Alongside DL-based solutions, there are also TML-DB-ON-FDTs, which will be discussed below. In [49], traditional ML algorithms such as the K-nearest neighbors (KNN), support vector machine (SVM), and naive Bayes (NB) algorithms are used to monitor the condition of a DC-link capacitor in a back-to-back (BTB) converter. Prior to training the models, features are extracted from the raw signal (the DC-link voltage) using wavelet decomposition.