A Feature Engineering-Assisted CM Technology for SMPS Output Aluminium Electrolytic Capacitors (AEC) Considering D-ESR-Q-Z Parameters

Abstract

Recent research has seen an interest in the condition monitoring (CM) approach for aluminium electrolytic capacitors (AEC), which are present in switched-mode power supplies and other power electronics equipment. From various literature reviews conducted and from a failure mode effect analysis (FMEA) standpoint, the most critical and prone to fault component with the highest percentage is mostly capacitors. Due to its long-lasting ability (endurance), CM offers a better paradigm for AEC due to its application. However, owing to severe conditions (over-voltage, mechanical stress, high temperature) that could occur during use, they (capacitors) could be exposed to early breakdown and overall shutdown of the SMPS. This study considered accelerated life testing (electrical stress and long-term frequency testing) for the component due to its endurance in thousands of hours. We have set up the experiment test bench to monitor the critical electrical parameters: dissipation factor (D), equivalent series resistance (ESR), quality factor (Q), and impedance (Z), which would serve as a health indicator (HI) for the evaluation of the AECs. Time-domain features were extracted from the measured data, and the best features were selected using the correlation-based technique.

A Filter-Based Feature-Engineering-Assisted SVC Fault Classification for SCIM at Minor-Load Conditions

“…DT has been used in a variety of studies for regression and classification problems; for example, in [28], the authors combined DT with Optimized Stationary Wavelet Packet Transform for incipient bearing fault diagnosis. RF is based on the grouping of trees for regression and classification and is thought to mitigate underfitting and overfitting, both of which are common problems in DT [2,9,19]. RF is an ensemble learning method in which each individual tree reveals a class prediction, with the class receiving the most votes becoming the prediction model.…”

“…Another ML-based classifier used in our study is the k-Nearest Neighbor; a classy and simple ML method used for classification problems. k-NN, as the name implies, is a non-parametric and instance-based ML algorithm in which similar items are grouped together to learn their pattern [2,9,29]. Its performance is based on a mathematical sequence, which means that it readily assumes that any category of data with related features displays correlated characteristics and value.…”

“…Gradient boost is one of the most exceptional classifiers among boosters, with a high level of dependability and effectiveness. Due to the fact that DT is used as one of its weak classifiers and is structured in a greedy manner, gradient boost is also known as gradient boosting trees or gradient-boosted trees [2,9,27]. Gradient boost is sometimes referred to as a "genetic algorithm" because of its ability to minimize any differentiable loss function due to its agnostic behavior when dealing with loss functions [27].…”

“…These algorithms, in various degrees of strength, help in modifications for the most ideal realizable result [31]. Stochastic gradient descent (SGD) is a type of algorithm that focuses on convex loss functions, and this includes logistic regression and linear support vector machine [9,32]. SGD iteratively selects random samples to update the model's parameter; this accelerates the learning rate, which speeds up convergence at minimal training computational times [32,33].…”

“…One must comprehend the use of various methods and techniques to be able to craft a methodology that would perfectly fit the available data to achieve a robust framework for fault prediction and classification either by using machine learning or statistical approachs. Therefore, "feature engineering" entails the capacity to draw on a variety of domain expertise to extract the most important variable from the available raw data [9]. The creation and introduction of computer intelligence such as artificial intelligence (AI), machine learning (ML), and also deep learning (DL) positively impacted research studies and resulted in a progressive CBM approach.…”