Mother Wavelet Research Articles

A Technique for Bearing Fault Diagnosis Using Novel Wavelet Packet Transform-Based Signal Representation and Informative Factor LDA

This paper proposes a new method for bearing fault diagnosis using wavelet packet transform (WPT)-based signal representation and informative factor linear discriminant analysis (IF-LDA). Time–frequency domain approaches for analyzing bearing vibration signals have gained wide acceptance due to their effectiveness in extracting information related to bearing health. WPT is a prominent method in this category, offering a balanced approach between short-time Fourier transform and empirical mode decomposition. However, the existing methods for bearing fault diagnosis often overlook the limitations of WPT regarding its dependence on the mother wavelet parameters for feature extraction. This work addresses this issue by introducing a novel signal representation method that employs WPT with a new rule for selecting the mother wavelet based on the power spectrum energy-to-entropy ratio of the reconstructed coefficients and a combination of the nodes from different WPT trees. Furthermore, an IF-LDA feature preprocessing technique is proposed, resulting in a highly sensitive set of features for bearing condition assessment. The k-nearest neighbors algorithm is employed as the classifier, and the proposed method is evaluated using datasets from Paderborn and Case Western Reserve universities. The performance of the proposed method demonstrates its effectiveness in bearing fault diagnosis, surpassing existing techniques in terms of fault identification and diagnosis performance.

Self-matching extraction fractional wavelet transform for mechanical equipment fault diagnosis

Time-frequency analysis (TFA) of vibration signals is an important way for fault diagnosis and predictive maintenance of mechanical equipment. However, the existing TFA methods, such as wavelet transform (WT) and synchrosqueezing transform, are incapable to describe local details of strongly time-varying signals, bringing such problems as time-frequency energy dispersion. Theoretically, it is more beneficial for time-varying band-limited signals to make multi-resolution analysis in the time-fraction frequency domain because such action can increase the selection chances of mother wavelets in any direction on the time-frequency plane by changing the angle . In order to improve the ability of analyzing strongly time-varying signals and the readability of the time-frequency plane, this paper has proposed a novel TFA method called self-matching extraction fractional WT (SMEFRWT). Firstly, within the theoretical framework of fractional WT (FRWT), a minimum entropy-based method is proposed to choose an optimal angle in the fractional domain. Such action can obtain the best aggregation angle of time-frequency energy and facilitate the separation of multi-component signals. Then, self-matching extraction operator, one instantaneous-frequency estimated operator that match the time-frequency structure of amplitude modulation signals, is constructed. The operator allows for the distribution of time-frequency energy of fast-changing signals in both frequency and time directions, making it possible to match the time-varying vibration signals from mechanical equipment more accurately. Finally, SMEFRWT is adopted to make TFA and extract fault features of the faulty experiment benches on gear and bearings, with its feasibility validated by experimental results.

Improved T-wave detection in electrocardiogram signals based non-stationary wavelet transform and QRS complex cancellation with kurtosis analysis

Objective. The T-wave in electrocardiogram (ECG) signal has the potential to enumerate various cardiac dysfunctions in the cardiovascular system. The primary objective of this research is to develop an efficient method for detecting T-waves in ECG signals, with potential applications in clinical diagnosis and continuous patient monitoring. Approach. In this work, we propose a novel algorithm for T-wave peak detection, which relies on a non-decimated stationary wavelet transform method (NSWT) and involves the cancellation of the QRS complex by utilizing its local extrema. The proposed scheme contains three stages: firstly, the technique is pre-processed using a two-stage median filter and Savitzky–Golay (SG) filter to remove the various artifacts from the ECG signal. Secondly, the NSWT technique is implemented using the bior 4.4 mother wavelet without downsampling, employing 24 scale analysis, and involves the cancellation of QRS-complex using its local positions. After that, Sauvola technique is used to estimate the baseline and remove the P-wave peaks to enhance T-peaks for accurate detection in the ECG signal. Additionally, the moving average window and adaptive thresholding are employed to enhance and identify the location of the T-wave peaks. Thirdly, false positive T-peaks are corrected using the kurtosis coefficients method. Main results. The robustness and efficiency of the proposed technique have been corroborated by the QT database (QTDB). The results are also validated on a self-recorded database. In QTDB database, the sensitivity of 98.20%, positive predictivity of 99.82%, accuracy of 98.04%, and detection error rate of 1.95% have been achieved. The self-recorded dataset attains a sensitivity, positive predictivity, accuracy, and detection error rate of 99.94%, 99.96%, 99.90%, and 0.09% respectively. Significance. A T-wave peak detection based on NSWT and QRS complex cancellation, along with kurtosis analysis technique, demonstrates superior performance and enhanced detection accuracy compared to state-of-the-art techniques.

Semi-automated Rock Layer Recognition from Borehole Log Data Using Combined Wavelet and Fourier Transform: A Case Study in the KG basin, India

Abstract The accurate identification of rock layer interfaces from wireline logs is one of the crucial issue of geological interpretation as we face difficulties in traditional interpretations due to the presence of various kind of high and low frequencies in the log data. Determination of rock boundaries in shallow marine sediments is very important to understand several issues related to the exploration of conventional and non-conventional hydrocarbons. Presently a combined wavelet and Fourier transform-based approach was demonstrated to discriminate the rock layers from wireline log data in Krishna-Godavari basin, which were acquired during the India National Gas Hydrate Program Expedition 02 (NGHP-Exp.-02). Initially, we selected the suitable mother wavelets and optimum level of decomposition to perform the wavelet transform by computing the error between the original log signal(s) and approximation coefficients (cA’s) of the decomposed signal(s) at several decomposition levels. Subsequently, discrete wavelet transform (DWT) of a particular decomposition level was applied to the log signals to obtain the detail coefficients (cD’s), which contain the high-frequency components of the log signals. Further, these high-frequency components of the log signals were utilized in frequency spectrum-based filtering approaches. This approach was adapted to yield information about the abrupt changes occurring in the log signals across the studied depth interval. The combined application of Fourier and wavelet transforms to shallow marine log data demonstrates that this approach is an efficient tool for delineating rock layer boundaries.

Performance assessment of discrete wavelet transform for de-noising of FBG sensors signals embedded in asphalt pavement

In recent years, the Fiber Bragg Grating (FBG) sensor technology has been increasingly utilized as an optical measurement system in various engineering applications, particularly for structural health monitoring (SHM) purposes. This trend can be attributed to the inherent benefits of FBG sensors, such as their small size, immunity to electromagnetic interference, resistance to corrosion, and high accuracy and sensitivity. Various factors cause noise in the FBG sensor signal, which has a significant effect on measurement precision. As a result, de-noising plays an important role in the use of FBG sensor systems. In this study, strain data collected from FBG sensors embedded in a road section were used to evaluate the performance of discretized wavelet transform (DWT) for denoising FBG signals. The presence of noise poses a significant challenge in accurately measuring low-amplitude strains and light loads. To address this issue, various approaches have been investigated, including the selection of appropriate mother wavelets, levels of decomposition, thresholding functions, and thresholding selection approaches, with the aim of identifying the optimal parameters for effective denoising. The results show that FBG signals could be denoised successfully and low amplitude strains appeared completely without any loss of valuable data.

Improving Rock Classification with 1D Discrete Wavelet Transform Based on Laboratory Reflectance Spectra and Gaofen-5 Hyperspectral Data

The high intra-class variability of rock spectra is an important factor affecting classification accuracy. The discrete wavelet transform (DWT) can capture abrupt changes in the signal and obtain subtle differences between the spectra of different rocks. Taking laboratory spectra and hyperspectral data as examples, high-frequency features after DWT were used to improve the discrimination accuracy of rocks. Various decomposition levels, mother wavelet functions, and reconstruction methods were used to compare the accuracy. The intra-class variability was measured using the intra-class Spectral Angle Mapper (SAM). Our results show that the high-frequency features could improve the discrimination accuracy of laboratory spectra by 13.4% (from 46.5% to 59.9%), compared to the original spectral features. The accuracy of image spectra in two study areas increased by 8.6% (from 68.3% to 76.9%) and 7.2% (from 81.3% to 88.5%), respectively. Haar wavelets highlighted the spectral differences between different rocks. After DWT, intra-class SAM reduced and intra-class variability of rocks decreased. The Pearson correlation coefficient indicated a negative correlation between intra-class variability and overall accuracy. It suggested that improving classification accuracy by reducing intra-class variability was feasible. Though the result of lithological mapping still leaves room for improvement, this study provides a new approach to reduce intra-class variability, whether using laboratory spectra or hyperspectral data.

Spanwise correlation of unsteady lift due to vortex shedding on the square cylinder: Perspectives provided by a time–frequency analysis

Spanwise correlation of unsteady lift due to vortex shedding is of particular interest for slender structures. In this paper, Hilbert transform and Wavelet transform were applied as time–frequency analysis tools to the sectional lift of a square cylinder. For Wavelet transform, the complex Morlet wavelet and the generalized Morse wavelet were considered as the mother wavelets, and the synchrosqueezing technique was used for post-processing. At high Reynolds number and in smooth flow, Hilbert transform and Wavelet transform mutually confirm that vortex shedding is characterized by an instantaneous shedding frequency fluctuating in real-time. As an example, at Reynolds number 3.9×104, the results of Wavelet transform suggest a standard deviation of the instantaneous shedding frequency of at least 4% about the conventional Strouhal frequency. The extracted instantaneous frequency is found stochastic, showing reasonably good Gaussian properties for its probability density function. The instantaneous frequencies simultaneously extracted at two cross sections of a spanwise separation are correlated, being the correlation coefficient lower than that calculated with fluctuating lifts. The shape of the coherence of the instantaneous frequency is found quite similar to that of turbulence, and the magnitude overall decreases with the increase of spanwise separation. The fluctuation of the instantaneous vortex shedding frequency, especially the low-frequency part (lower than about one-half of the Strouhal frequency), is believed critical in determining the spanwise correlation of unsteady lift, which was confirmed reconstructable via the extracted instantaneous shedding frequency. Finally, attention in this paper has also been paid to a reliable extraction and quantification of the concerning time–frequency characteristics. For instance, tests of the used time–frequency tools first on the purposely prepared artificial signal suggest, that the high-frequency fluctuation in the instantaneous vortex shedding frequency (higher than the Strouhal frequency) can actually not be reliably extracted.

A farey wavelet-based mathematical model for biological series

This work lies in the whole biomathematics framework which has as general goal the solving of biological problems with mathematical tools. The main objective of the present paper is to predict the transmembrane helices of proteins using wavelet denoising techniques. As a case of matter, we particularly highlight the interest in solving the problem of localizing these helices. Indeed, these helices play a vital role in the human body, notably in photosynthesis, respiration, neuronal signaling, immune response, absorption of nutrition, and have an important link with drugs as receptors coupled to many proteins. However, due to technical constraints, the crystallization of these helices remains very complex, which limits the exploration of their structure [27]. To overcome these difficul- ties, different prediction tools have been developed, initially based on hydrophobicity. In this paper, we serve the Farey wavelet as a last alternative mother wavelet constructed in [4] to develop a mathematical model suitable for protein series description. We precisely apply a new type of wavelets constructed recently in [4] to localize the and/or predict the position of the transmembrane proteins alpha-helices in a coronavirus strain. The results are compared to existing works for performance, accuracy, and efficiency.

Enhancing Electroretinogram Classification with Multi-Wavelet Analysis and Visual Transformer

The electroretinogram (ERG) is a clinical test that records the retina’s electrical response to light. Analysis of the ERG signal offers a promising way to study different retinal diseases and disorders. Machine learning-based methods are expected to play a pivotal role in achieving the goals of retinal diagnostics and treatment control. This study aims to improve the classification accuracy of the previous work using the combination of three optimal mother wavelet functions. We apply Continuous Wavelet Transform (CWT) on a dataset of mixed pediatric and adult ERG signals and show the possibility of simultaneous analysis of the signals. The modern Visual Transformer-based architectures are tested on a time-frequency representation of the signals. The method provides 88% classification accuracy for Maximum 2.0 ERG, 85% for Scotopic 2.0, and 91% for Photopic 2.0 protocols, which on average improves the result by 7.6% compared to previous work.

A novel WGF-LN based edge driven intelligence for wearable devices in human activity recognition

Human activity recognition (HAR) is one of the key applications of health monitoring that requires continuous use of wearable devices to track daily activities. The most efficient supervised machine learning (ML)-based approaches for predicting human activity are based on a continuous stream of sensor data. Sensor data analysis for human activity recognition using conventional algorithms and deep learning (DL) models shows promising results, but evaluating their ambiguity in decision-making is still challenging. In order to solve these issues, the paper proposes a novel Wasserstein gradient flow legonet WGF-LN-based human activity recognition system. At first, the input data is pre-processed. From the pre-processed data, the features are extracted using Haar Wavelet mother- Symlet wavelet coefficient scattering feature extraction (HS-WSFE). After that, the interest features are selected from the extracted features using (Binomial Distribution integrated-Golden Eagle Optimization) BD-GEO. The important features are then post-processed using the scatter plot matrix method. Obtained post-processing features are finally given into the WGF-LN for classifying human activities. From these experiments, the results can be obtained and showed the efficacy of the proposed model.

Investigation of noise suppression in experimental multi-cell battery string voltage applying various mother wavelets and decomposition levels in discrete wavelet transform for precise state-of-charge estimation

Lithium-ion battery state-of-charge (SOC) estimation algorithms developed employing electrical circuit models (ECMs) require original experimental discharging and charging voltage (EDCV) data for very high accuracy. However, if noisy EDCV signals are detected while sensing, inaccurate SOC estimation, which causes poor BMS performance, is unavoidable. Thus, this paper introduces a novel method for implementing noise suppression employing a discrete wavelet transform (DWT) multi-resolution analysis (MRA) based method. The approach extensively performs two comparison evaluations of denoising based on the information provided by the mother wavelet and the corresponding MRA level decomposition. The former approach is to obtain the level of decomposition with high execution of the denoising under a similar mother wavelet state. The second approach, on the other hand, compares noise suppression for various mother wavelets under identical conditions of the level decomposition with similar decomposition level conditions. Signal-to-noise ratio (SNR) is used to evaluate all comparative analyses. This method compares both hard- and soft-thresholding noise reduction methods in their entirety. From these findings, the study's analytical findings indicate a precise evaluation by demonstrating the variation in SNR with the corresponding mother wavelets and their decomposition levels. Thus, it is certain that the mother wavelet and the decomposition level are optimized for noise suppression for a battery string, series/parallel (2S3P) each of 2.2 Ah, with the EDCV signal incorporated with noise.

A novel optimal wavelet filter banks for automated diagnosis of Alzheimer’s disease and mild cognitive impairment using Electroencephalogram signals

Electroencephalogram (EEG) of Alzheimer’s disease (AD) patients show a slowing effect and less synchronization. EEG signal’s transient and abrupt nature is captured from various mother wavelets. However, better performance can be obtained by balancing time–frequency localization in wavelet filters. We propose a new approach for designing filter banks based on optimal time–frequency localization of a four-step lifting structure (FSLS). First, we design a FSLS using Euler’s Frobenius half-band polynomial (EFHBP). The perfect reconstruction condition and vanishing moments are imposed on EFHBP to achieve maximum flat half-band filters (HBFs). HBFs are optimized using a balanced uncertainty metric to obtain a time–frequency spread balance. Afterward, these HBFs are used in the FSLS to obtain the synthesis and analysis filter banks. The proposed time–frequency optimized biorthogonal wavelet filter banks (TFOBWFBs) achieved a balance between time–frequency localization. Further, these TFOBWFBs are applied to decompose the EEG signals of AD patients. Twenty different features were extracted from decomposed EEG subbands, of which twelve significant features were selected using Kruskal Walli’s test. The machine learning models were trained and tested using obtained features with 10-fold cross-validation and leave-one-subject-out cross-validation. To validate this study, the proposed TFOBWFBs have been applied to two publicly available EEG datasets of mild cognitive impairment (MCI), AD, and healthy control (HC) subjects. The proposed TFOBWFBs achieved 98.90% accuracy for 2-way (AD vs. HC) and 96.50% for 3-way (AD vs. MCI vs. HC) classification using a support vector machine model with a 10-fold cross-validation. The proposed method outperforms the existing AD and MCI detection techniques. The performance of the proposed machine learning framework with optimization of wavelet filter banks is more significant and fast compared to previous studies. Also, the proposed framework can be applied to detect other neurodegenerative disorders.

Identifying a Suitable Signal Processing Technique for MI EEG Data

Motor imagery (MI) electroencephalography (EEG) technology is acquiring great attention from researchers due to its remarkable real-world applications. EEG signals inherit a high degree of non-stationarity, making their analysis not modest. Hence, choosing an appropriate signal processing approach becomes crucial. This comparative paper aims to identify a suitable signal processing method among famous approaches, namely short-time Fourier transform (STFT), continuous wavelet transform (CWT), and two variations of discrete wavelet transform maximal overlap DWT (MODWT) and MODWT multiresolution analysis (MODWTMRA). Different mother wavelet basis filters experimented with wavelet methods: Morse, Amor, Bump, Symlets, Daubechies, Coiflets, and Fejér-Korovkin. The different methods were tested on the classification of the right-hand and left-hand motor imagery tasks using the brain-computer interface (BCI) competition IV 2b dataset. A shallow convolutional neural network containing a single convolution layer was first trained and then used for classification. The experimental outcomes verified that MI EEG signals can be better analyzed and recognized using the maximal overlap-based signal processing methods. The classification accuracy proved that MODWT and MODWTMRA with the Symlets wavelet outperformed the other methods.

Fault detection through discrete wavelet transform in overhead power transmission lines

AbstractTransmission lines are a very important and vulnerable part of the power system. Power supply to the consumers depends on the fault‐free status of transmission lines. If the normal working condition of the power system is disturbed due to faults, the persisting fault of long duration results in financial and economic losses. The fault analysis has an important association with the selection of protective devices and reliability assessment of high‐voltage transmission lines. It is imperative to devise a suitable feature extraction tool for accurate fault detection and classification in transmission lines. Several feature extraction techniques have been used in the past but due to their limitations, that is, for use in stationary signals, limited space in localizing nonstationary signals, and less robustness in case of variations in normal operation conditions. Not suitable for real‐time applications and large calculation time and memory requirements. This research presents a discrete wavelet transform (DWT)‐based novel fault detection technique at different parameters, that is, fault inception and fault resistance with proper selection of mother wavelet. In this study, the feasibility of DWT using MATLAB software has been investigated. It has been concluded from the simulated data that wavelet transform together with an effective classification algorithm can be implemented as an effective tool for real‐time monitoring and accurate fault detection and classification in the transmission lines.

De-noising of microwave reflectometry signal using maximal overlap discrete wavelet packet transform for plasma density measurement

For microwave reflectometry-based fusion plasma diagnostics, the acquired data is contaminated by the surrounding noise environment, making it difficult to measure the beat frequency accurately. Discrete wavelet and wavelet packet transforms are unsuitable as the former cannot decompose detailed coefficients, and the latter is a time-variant transform. This paper presents a correlation-based Maximal Overlap Discrete Wavelet Packet Transform (MODWPT), which decomposes the signal using different mother wavelet families. The suitable mother wavelet is chosen based on the metrics like signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR), mean squared error (MSE), and mean absolute error (MAE). The data used for analysis is the stationary tokamak wall, for which the beat frequency and distance are measured. After rigorous analysis, it is found that sym12 outperforms with the following values 0.0493 (MSE), 0.1736 (MAE), 4.3334(SNR(dB)), 13.0634(PSNR(dB)) respectively. This technique can be extended to plasma scenarios for accurately measuring beat frequency and density profile.

Forecasting of AQI (PM2.5) for the three most polluted cities in India during COVID-19 by hybrid Daubechies discrete wavelet decomposition and autoregressive (Db-DWD-ARIMA) model.

Air pollution has emerged as a significant environmental challenge at the global level, and India is majorly affected by it. Numerous emission sources, such as automobiles, industries, fuel-burning for household and commercial activities, and dust due to construction activities, are responsible for air pollution. The lockdown in India which was clamped for controlling the spread of virulent disease also brought down the level of pollutants in air significantly. The proposed approach deals with the application of the hybrid model of Daubechies discrete wavelet decomposition (Db-DWD) and the autoregressive integrated moving average (ARIMA) model for modeling and forecasting the chaotic data of air quality index (PM2.5) from the three most polluted cities (Agra, New Delhi, and Varanasi) in India for pre and within lockdown periods. The estimated outputs of the component series are then reconstructed to obtain the final forecast of the AQI data. The statistical evaluation compares the performance of the simple ARIMA model and the joint Db-DWD-ARIMA model. Also, the coupled model has been applied for forecasting efficacy with Daubechies mother wavelet of orders 5, 8, 10, and 12. The hybrid model reduced forecasting errors and improved accuracy significantly. Secondly, the forecasting efficiencies in this hybrid model have enhanced with the increase in wavelet order. This study will help to assess and take appropriate steps to control air pollution levels and to monitor the growing air pollutants, which will be significant for our existence.

Damage detection of structures based on wavelet analysis using improved AlexNet

Deep learning-based approaches have garnered a great deal of interest among different methods in structural health monitoring (SHM), whose primary objective is to assess structural health status. AlexNet is renowned for being a practical convolutional neural network architecture utilized to classify images. However, having numerous parameters, AlexNet gives rise to significant computational costs when applied to classify a small number of damage types occurred in civil engineering. Therefore, in this paper, the improved AlexNet with a lower computational cost and images generated by continuous wavelet transform (CWT) have been employed to classify multiple types of damages which is the pivotal characteristics of the proposed procedure. The performance of this approach was validated with responses obtained from three structures. The acceleration responses converted to time–frequency images were derived from undamaged and damaged states, such as scour, damage in connections, and stiffness reduction. Moreover, the efficiency in detecting the damage extent was investigated. The approach exhibited proficient performance in multi-classification, achieving high prediction accuracy rates of more than 99%, 97%, and 94% in a benchmark bridge, a frame model, and a real truss bridge, respectively. The sensitivity analysis results showed that the Bump mother wavelet had the best performance among studied mother wavelets; moreover, investigation of the number of input images showed that the higher the number of pictures, the more accurate the results, and prediction accuracy fluctuated by no more than approximately 5%. Finally, results demonstrated that the accuracy improves as the duration of each record increases, and the related decrease in accuracy is roughly 3%.

Development of Rolling Element Bearing Test Bench Facility And Application of Wavelet Transform for Fault Diagnosis

The aim of this experimental study is to develop an efficient rolling element bearing fault detection technique which could be effectively utilized for easy interpretation of bearing signal data. This paper deals with a new scheme for the diagnosis of localized defects in rolling element bearing based on Wavelet Transform. Vibration signal for healthy bearing, bearing with outer race fault, inner race fault and ball fault were acquired from a bearing test bench fabricated at Naval Institute of Aeronautical Technology, Kochi. The vibration signal was acquired using an accelerometer, control card and Data acquisition card. The acquired signal was further processed using a time frequency analysis technique known as Wavelet Transform. The experiments were carried out using different mother wavelets. However, defect features were best obtained using Complex Gaussian Wavelet of order 8, thereby rendering easier interpretation of the analysed data.

Detection of Ore‐Forming Elements Migrated From Rock and Soil to Vegetation Leaves Using Hyperspectral Data

AbstractIn this paper, we present the detection of ore‐forming elements migrated from soil and rock to vegetation leaves using hyperspectral data. The rock, soil (C, B, and A layer), and vegetation (root, stem, and leaf) samples with the measurements of spectra and element concentrations in the vertical section of the abandoned pits were collected. The wavelet approach is applied to analyze the correlations between spectral features and element concentrations. The results show that the significant correlations are found at a lower order mother wavelets such as Haar. Moreover, the significant correlations are negative between Cu and Mo elements and the wavelet energy vector of the soil and rock to vegetation leaves spectrum. The results also show that ore‐forming elements migrated from rock and soil to vegetation leaves can be detected using hyperspectral data in the study area.

Evaluation Of Digital Wavelet Filter On Low Voltage Arcing Detection Equipment

Arcing is one of the causes of fire disasters that occur quite a lot in the world. The danger of arcing causes enormous losses. The fault current caused by arcing usually occurs in a very short duration, so that safety equipment such as Miniature Circuit Breaker (MCB) and Fuse cannot detect the disturbance. If arcing takes place continuously, there will be heat that can damage the equipment and cause a fire. In this study, an evaluation of the use of mother wavelets in the Discrete Wavelet Transform (DWT) method on low voltage detection equipment will be carried out. DWT is a mathematical function that is the most successful in the field of signal processing. Signal processing using the DWT method will be analyzed and compared its performance with mother wavelets Daubechies-1, Daubechies-4, Coiflet-4, and Symlet-4. Then applied to low voltage arcing detection equipment. From the results of testing and analysis obtained the mother wavelet Daubechies-4 produces a high level of accuracy and sensitivity. Mother wavelet Coiflet-4 and Symlet-4 produce the lowest level of accuracy at 400W and 700W load. Meanwhile, on the mother wavelet Daubechies-1, the normal signal amplitude value is close to the arcing signal amplitude value, so it will be difficult to determine the threshold value. In this study, the results obtained are arcing detection equipment using the mother wavelet Daubechies-4 method can detect arcing disturbances very well and produces a high level of accuracy and sensitivity compared to the mother wavelet Daubechies-1, Coiflet-4 , and Symlet-4.