## 1 Introduction
based on the ensemble machine learning (EML) for fault detection in Series dc arc and tested its performance using techniques such as bagging, boosting, and stacking various linear classifiers such as fault perceptrons, Decision Trees (DTs), and SVMs. [PERSON] et al. [15] proposed a signal reconstruction modeling technique using support vector regression with a sliding-time-window technique for fault detection. [PERSON] [16] et al. proposed Decision Tree and K-Nearest Neighbor to diagnose faults in both unlabeled and specified data of transmission and distribution systems with confidence of microgrids. [PERSON] et al. [17] proposed chiller fault detection to enable fast parameter determination without expert assistance using the Bayesian network. [PERSON] et al. [18] proposed a clustering-based Principal Component Analysis (PCA) to propose a fault detection method for water heat pump systems. [PERSON] et al. [19] proposed a Fault Detection method using multi-mode PCA and Gaussian mixed model in a sewage heat pump system. [PERSON] et al. [20] proposed the fault detection of photovoltaic current and voltage through the ANN-based modeling method. [PERSON] et al. [21] proposed solar current and voltage fault detection using multi-resolution signal composition (MSD) and a two-stage support vector machine classifier. Fault Detection using Deep Learning (DL): In the case of the CNN (Convolution Neural Network), which is an ANN method, training is carried out using the following procedure: multiple inputs are received; the computation is performed using a model form that the user wants; an output is produced. The method of applying a 1-D CNN model using time-amplitude data with a constant period has been presented as a failure diagnosis method [22; 23; 24; 25; 26; 27; 28; 29; 30; 31]. Another CNN model is 2-D CNN, in which the computation produces images of 3-D shapes with a width and length like the input data as the output [6; 32; 33; 34; 35]. There have been many attempts to apply 2-D CNNs to speech recognition and fault diagnosis because 2-D CNNs are transferable with various models [36; 37; 38; 39]. [PERSON] [24] et al. proposed a fault diagnosis of bearing using an autoencoder. [PERSON] et al. [34] performed fault detection based on acoustic spectral imaging visualizing acoustic emission signals. [PERSON] et al. [25] proposed a fault detection method by constructing feature extractors based on denoising auto-encoder (DAE) and conventional auto-encoder (CAE) for fault detection using vibration data. [PERSON] et al. [26] proposed an autoencoder learning method using an artificial fish swarm algorithm for fault detection of rotating machines. [PERSON] et al. [27] proposed a Gaussian-Bernoulli deep Boltzmann machine (GDBM) method for diagnosing rotating machine failures. [PERSON] et al. [28] developed a fault-diagnostic system that can overcome axial velocity fluctuations using a deep neural network based on a complex envelope spectra-stacked sparse autoencoder. [PERSON] et al. [29] presented a fault-finding method based on a Gaussian restricted Boltzmann machine (Gaussian RBM) using envelope spectra of sampled data as a high-dimensional feature vector for fault diagnosis of bearings. [PERSON] et al. [30] proposed a convolutional deep relief network (CDBN) using an expansion moving average (EMA) technique to efficiently learn the fault features of the vibration signal. [PERSON] et al. [35] proposed a bearing classification model for a deep learning model after transforming it into a 2D image using short-time Fourier transform, wavelet transform, and Hilbert-Huang transforms. [PERSON] et al. [31] proposed a one-dimensional CNN-based deep coupled dense convolutional network (CDCN) to integrate information fusion, feature extraction, and fault diagnosis together for intelligent diagnosis.