Empirical Wavelet Transform Method Research Articles

New formulation for predicting total dissolved gas supersaturation in dam reservoir: application of hybrid artificial intelligence models based on multiple signal decomposition

Total dissolved gas (TDG) concentration plays an important role in the control of the aquatic life. Elevated TDG can cause gas-bubble trauma in fish (GBT). Therefore, controlling TDG fluctuation has become of great importance for different disciplines of surface water environmental engineering.. Nowadays, direct estimation of TDG is expensive and time-consuming. Hence, this work proposes a new modelling framework for predicting TDG based on the integration of machine learning (ML) models and multiresolution signal decomposition. The proposed ML models were trained and validated using hourly data obtained from four stations at the United States Geological Survey. The dataset are composed from: (i) water temperature (Tw), (ii) barometric pressure (BP), and (iii) discharge (Q), which were used as the input variables for TDG prediction. The modelling strategy is conducted based on two different steps. First, six singles ML model namely: (i) multilayer perceptron neural network, (ii) Gaussian process regression, (iii) random forest regression, (iv) random vector functional link, (v) adaptive boosting, and (vi) Bootstrap aggregating (Bagging), were developed for predicting TDG using Tw, BP, and Q, and their performances were compared. Second, a new framework was introduced based on the combination of empirical mode decomposition (EMD), the variational mode decomposition (VMD), and the empirical wavelet transform (EWT) preprocessing signal decomposition algorithms with ML models for building new hybrid ML models. Hence, the Tw, BP, and Q signals were decomposed to extract the intrinsic mode functions (IMFs) by using the EMD and VMD methods and the multiresolution analysis (MRA) components by using the EWT method. Then after, the IMFs and MRA components were selected and regraded as new input variables for the ML models and used as an integral part thereof. The single and hybrid prediction models were compared using several statistical metrics namely, root mean square error, mean absolute error, coefficient of determination (R2), and Nash–Sutcliffe efficiency (NSE). The single and hybrid models were trained several times with high number of repetitions, depending on the kind of modeling process. The obtained results using single models gave good agreement between the predicted TDG and the situ measured dataset. Overall, the Bagging model performed better than the other five models with R2 and NSE values of 0.906 and 0.902, respectively. However, the extracted IMFs and MRA components using the EMD, VMD and the EWT have contributed to an improvement of the hybrid models’ performances, for which the R2 and NSE were significantly increased reaching the values of 0.996 and 0.995. Experimental results showed the superiority of hybrid models and more importantly the importance of signal decomposition in improving the predictive accuracy of TDG.Graphical abstract

Prediction of Heat and Cold Loads of Factory Mushroom Houses Based on EWT Decomposition

Load forecasting has significant implications on optimizing the operation of air conditioning systems for industrial mushroom houses and energy saving. This research paper presents a novel approach for short-term load forecasting in mushroom houses, which face challenges in accurately modeling cold and heat loads due to the complex interplay of various factors, including climatic conditions, mushroom growth, and equipment operation. The proposed method combines empirical wavelet transform (EWT), hybrid autoregressive integrated moving average (ARIMA), convolutional neural network (CNN), and bi-directional long short-term memory (BiLSTM) with an attention mechanism (CNN-BiLSTM-Attention) to address these challenges. The first step of this method was to select input features via the Boruta algorithm. Then, the EWT method was used to decompose the load data of mushroom houses into four modal components. Subsequently, the Lempel–Ziv method was introduced to classify the modal components into high-frequency and low-frequency classes. CNN-BiLSTM-Attention and ARIMA prediction models were constructed for these two classes, respectively. Finally, the predictions from both classes were combined and reconstructed to obtain the final load forecasting value. The experimental results show that the Boruta algorithm selects key influential feature factors effectively. Compared to the Spearman and Pearson correlation coefficient methods, the mean absolute error (MAE) of the prediction results is reduced by 14.72% and 3.75%, respectively. Compared to the ensemble empirical mode decomposition (EEMD) method, the EWT method can reduce the decomposition reconstruction error by an order of magnitude of 103, effectively improving the accuracy of the prediction model. The proposed model in this paper exhibits significant advantages in prediction performance compared to the single neural network model, with the MAE, root mean square error (RMSE), and mean absolute percentage error (MAPE) of the prediction results reduced by 31.06%, 26.52%, and 39.27%, respectively.

Research on Transformer Partial Discharge Feature Extraction Based on Empirical Wavelet Transform and Multiscale Permutation Entropy

Aiming to extract efficiently the fault features of partial discharge in the process of fault diagnosis of power transformer, a method of combining Empirical Wavelet Transform (EWT) with Multiscale Permutation Entropy (MPE) is advanced to extract fault features of transformers partial discharge. Firstly, four different partial discharge pulse signals are analyzed by EWT method, and the fault signal is decomposed according to different frequency domain characteristics of the signal to obtain the intrinsic mode function (IMF) of the signal. Then, the calculated multi-scale permutation entropy of different IMFs to complete the fault feature extraction. Finally, the multi-scale entropy of the fault semaphore is used as the eigenvector of the Support Vector Machine (SVM) for glitch diagnosis, and the accurate systematization of the partial discharge semaphore of the transformer is realized. semaphore Compared with the Continuous Wavelet Transform (CWT), Empirical Mode Decomposition (EMD), and Ensemble Empirical Mode Decomposition (EEMD) feature extraction way, it shows that the raised EWT-MPE is more valid and accurate in diagnosing and analyzing transformer partial discharge faults, and the accuracy of fault classification 96.43%.

A novel carbon price forecasting method based on model matching, adaptive decomposition, and reinforcement learning ensemble strategy.

Carbon price is closely related to energy conservation and emission reduction cost, and carbon price forecasting is conducive to improving and stabilizing market trading mechanisms. Considering the matching relationship between the prediction model and the carbon price market, a decomposition ensemble carbon price forecasting method based on reinforcement learning model fusion is designed. Firstly, considering the problem of disharmony between different base models and data patterns, a model matching strategy is developed to match the appropriate prediction base model for each carbon trading market. Secondly, the empirical wavelet transform (EWT) algorithm is used to decompose the carbon price into several subseries, which reduces the complexity of the original data and improves the prediction effect of each base model. Finally, for each carbon price market, the Q-learning algorithm of reinforcement learning is used to integrate the prediction results of the selected multiple base models into the final price forecasting of the corresponding market. The model is verified in the seven carbon trading markets, and experiments show that the model has superior and stable prediction performance compared with other methods. The mean absolute percentage errors of the Hubei, Beijing, Shenzhen, Chongqing, Tianjin, Shanghai, and EU markets are only 0.3515%, 1.2335%, 1.3388%, 1.2128%, 0.3229%, 0.2418%, and 0.4094%, respectively. It shows that the EWT method and reinforcement learning ensemble strategy do improve the prediction performance, and the proposed model can be used as a feasible tool for price assessment and management in carbon price markets.

Research on the Prediction Model of Blasting Vibration Velocity in the Dahuangshan Mine

In order to improve the prediction accuracy of blast vibration velocity, the model for predicting the peak particle velocity of blast vibration using the XGBoost (Extreme Gradient Boosting) method is improved, and the EWT–XGBoost model is established to predict the peak particle velocity of blast vibration by combining it with the EWT (Empirical Wavelet Transform) method. Calculate the relative error and root mean square error between the predicted value and measured value of each test sample, and compare the prediction performance of the EWT–XGBoost model with the original model. There is a large elevation difference between each vibration measurement location of high and steep slopes, but high and steep slopes are extremely dangerous, which is not conducive to the layout of blasting vibration monitoring equipment. The vibration velocity prediction model adopts the numerical simulation method, selects the center position of the small platform as the measurement point of the peak particle velocity, and studies the variation law of the blasting vibration velocity of the high and steep slopes under the action of top blasting. The research results show that the EWT–XGBoost model has a higher accuracy than the original model in the prediction of blasting vibration velocity; the simultaneous detonation method on adjacent high and steep slopes cannot meet the relevant requirements of safety regulations, and the delayed detonation method can effectively reduce the blasting vibration of high and steep slopes. The shock absorption effect of the elevation difference within 45 m is obvious.

Uterine Ultrasound Doppler Hemodynamics of Magnesium Sulfate Combined with Labetalol in the Treatment of Pregnancy-Induced Hypertension Using Empirical Wavelet Transform Algorithm.

The aim of this study was to explore the hemodynamic changes of magnesium sulfate combined with labetalol in the treatment of pregnancy-induced hypertension (PIH) under Doppler uterine ultrasound based on the empirical wavelet transform (EWT) algorithm. 500 patients with PIH in the hospital were selected and randomly divided into the control group (n = 250) and the observation group (n = 250). The control group was treated with conventional magnesium sulfate; the observation group was given labetalol based on magnesium sulfate drip in the control group. The uterine artery blood flow simulation model was established based on the EWT algorithm and compared with a short-time Fourier transform (STFT). The normalized root mean square error (NRMSE) of the STFT method was 0.19, and the NRMSE extracted by the EWT method was 0.13. After treatment, the blood pressure index, 24-hour urinary protein, and incidence of adverse birth outcomes in the observation group were lower than those in the control group; the effective rate of the control group (90.4%) was lower than that of the observation group (97.6%); the hemodynamic indexes of the uterine artery in the observation group were lower than those in the control group, and the differences were statistically significant (P < 0.05). The estimation accuracy of the EWT method was higher than that of the traditional STFT method; the combined treatment of magnesium sulfate and labetalol in patients with PIH had a remarkable effect, which could control the blood pressure index and reduce the 24-hour urinary protein; the uterine artery Doppler ultrasound examination could change hemodynamics and improve the adverse outcomes of mothers and infants.

An improved empirical wavelet transform and sensitive components selecting method for bearing fault

The empirical wavelet transform (EWT) has made outstanding contributions in the field of fault diagnosis. However, when EWT processes complex signals, there may be modal aliasing or meaningless components. In order to solve the spectral segmentation defect of the EWT method and improve its ability to extract bearing fault features, this paper proposes a new algorithm to improve EWT. In order to weaken the influence of extreme points in the complex Fourier spectrum on modal differentiation, the cycle envelope spectral segmentation method is proposed. The maximum envelope fitting method not only reduces the number of useless extreme points, but also highlights each mode. The number of filters is reduced while suppressing the interference of noise on the modal. The components that contain similar information in the result will be merged based on their correlation. Then sensitive IMFs assessing index (SIAI) is proposed to combine correlation and symmetry of the signal, and obtains the characteristics of time domain, frequency domain, and amplitude domain to extract fault information. Locomotive bearing and wind turbine gearbox bearing fault data verified the effectiveness of the method.

Empirical Wavelet Transform Method for GNSS Coordinate Series Denoising

Random noise inevitability affects positioning accuracy for Global Navigation Satellite System (GNSS) measurements. For effectual elimination of the noise mixed in the coordinate series, this paper proposes applying the empirical wavelet transform (EWT) method, which has the multi-scale decomposition capability identical to the empirical mode decomposition (EMD) method. EWT adaptively constructs the wavelet basis functions, divides the different signal modes by designing appropriate wavelet filters, and provides a complete mathematical theory. Compared to EMD, EWT exhibits higher calculation efficiency, sufficient theory, and an excellent adaptation capability. Thus, this paper analyzes and compares EWT with EMD and its improved version. The experimental results demonstrate that the EWT method has no frequency liaising phenomenon that characterizes EMD, and the signal after EWT noise reduction is quite close to the original. The measured data reveals that the noise reduction effect of EWT is superior to EMD and its improved version.

A Time Series Prediction Model of Foundation Pit Deformation Based on Empirical Wavelet Transform and NARX Network

Large deep foundation pits are usually in a complex environment, so their surface deformation tends to show a stable rising trend with a small range of fluctuation, which brings certain difficulty to the prediction work. Therefore, in this study we proposed a nonlinear autoregressive exogenous (NARX) prediction method based on empirical wavelet transform (EWT) pretreatment is proposed for this feature. Firstly, EWT is used to conduct adaptive decomposition of the measured deformation data and extract the modal signal components with characteristic differences. Secondly, the main components affecting the deformation of the foundation pit are analyzed as a part of the external input. Then, we established a NARX prediction model for different components. Finally, all predicted values are superpositioned to obtain a total value, and the result is compared with the predicted results of the nonlinear autoregressive (NAR) model, empirical mode decomposition-nonlinear autoregressive (EMD-NAR) model, EWT-NAR model, NARX model, EMD-NARX model and EWT-NARX model. The results showed that, compared with the EWT-NAR and EWT-NARX models, the EWT-NARX model reduced the mean square error of KD25 by 91.35%, indicating that the feature of introducing external input makes NARX more suitable for combining with the EWT method. Meanwhile, compared with the EMD-NAR and EWT-NAR models, the introduction of the NARX model reduced the mean square error of KD25 by 78.58% and 95.71%, indicating that EWT had better modal decomposition capability than EMD.

Point and Interval Solar Power Forecasting Using Hybrid Empirical Wavelet Transform and Robust Wavelet Kernel Ridge Regression

In this paper, a new and efficient hybrid empirical wavelet transform (EWT)-based reduced robust Mexican hat wavelet kernel ridge regression (RMHWK) model is proposed to achieve both point and interval forecasting of solar power in a smart grid scenario. Initially, the actual nonlinear solar power data series was decomposed by the EWT method. A reduced robust kernel ridge regression (RKRR) approach was incorporated that shows a notable decrease in training time without appreciable loss in forecasting accuracy. The reduction in the size of the kernel matrix was achieved by selecting a set of random support vectors from the training data set. For validating the superior performance of the proposed EWT-RMHWK forecasting model, a numerical experimentation implementing a real-time data set of 1 MW solar power plant (Odisha, India) as well as an online historical data set (Florida, USA) was considered and compared with other hybrid models using either empirical mode decomposition- or wavelet decomposition-based RKRR and EWT-ELM, etc. The kernel parameters were optimized with the chaotic water cycle algorithm to boost the performance of the proposed prediction model. Further, the proposed EWT-RKRR method was used to construct prediction interval forecasting with three different confidence levels with 90%, 95%, and 99% for Florida solar power plant using different time horizons of 15 min, 1 h, and 1 day, respectively.

Automatic picking method of microseismic signal first arrival time based on empirical wavelet transform

For problems in the process of microseismic signal acquisition, there is a large number of interference signals, which leads to that the first arrival time of signal is difficult to pick up.A picking method combines empirical wavelet transform (EWT) and signal first arrival algorithm is proposed to pick up the first arrival time of microseismic signal.Firstly,the component signals are obtained by the EWT method, which is used to decompose the microseismic signals with the characteristics of adaptive and conquering modal aliasing. Then the correlation coefficient and variance contribution rate of each component signals are calculated, and the component signals within the threshold range are selected for reconstruction to reduce the interference of noise signals. Finally, the modified energy ratio (MER) and fractal-dimension (FD) are used to pick up the first arrival time for the reconstructed denoising signal. According to the experimental verification, the accuracy of signal first arrival time picking combined with the MER and FD method based on EWT is 0.59% and 0.36% respectively. But in terms of efficiency, the former is obviously higher than the latter. The experimental and analytical results show that the model is highly accurate in picking up first arrival time of the effective signals and has a strong applicability to the signals of different data quantities.

An Effective Chatter Detection Method in Milling Process Using Morphological Empirical Wavelet Transform

The empirical wavelet transform (EWT) has shown its effectiveness in some applications. However, when noisy and nonstationary signals are analyzed, some local maxima may appear and be retained in the peak sequence mistakenly, so improper segmentation in the frequency domain will occur. In our research, the morphological EWT (MEWT) method is proposed based on morphological filters (MFs) and the 1-D Otsu method to mitigate the boundary segmentation drawback of the EWT, and it can be applied in chatter detection because of its good performance in finding the optimal chatter frequency band. First, the local maxima distribution plane is calculated using MFs. Then, the 1-D Otsu method processes the distribution plane and then achieves the optimal threshold for MFs. For MEWT’s better application in chatter detection, an automatic selection of the parameter $K$ using kurtosis is proposed and it breaks away from the previous empirical selection. Finally, since it is known that energy will transfer to chatter frequency bands when chatter occurs in the milling, the energy entropy of each subsignal used is to detect chatter frequency bands and choose the optimal chatter monitor indicator. The relevant simulation and experimental signals have been analyzed for verifying the validity of the MEWT and the novel chatter detection method with strong sensitivity to chatter.

An Improved Empirical Wavelet Transform and Refined Composite Multiscale Dispersion Entropy-Based Fault Diagnosis Method for Rolling Bearing

The vibration signals collected by the sensor often have non-stationary and non-linear characteristics owing to the complexity of working environment of rolling bearing, so it is difficult to obtain useful and stable vibration information for diagnosis. Empirical Wavelet Transform (EWT) can effectively decompose non-stationary and nonlinear signals, but it is not suitable for signal analysis of bearing with a complicated spectrum. In this paper, an improved EWT (IEWT) method is proposed by developing a new segmentation approach. Meanwhile, the IEWT is compared with empirical mode decomposition (EMD) and EWT to verify the superiority of IEWT in decomposition accuracy. By combining with the refined composite multiscale dispersion entropy (RCMDE), which is a powerful nonlinear tool for irregularity measurement of vibration signals, a new diagnosis method based on IEWT, RCMDE, multi-cluster feature selection and support vector machine is proposed. Then the method is applied to analysis of bearing in this paper and the results show that the new method has higher identifying rate and better performance than that of the methods of RCMDE combining with EMD or EWT. Also, the superiority of RCMDE to dispersion entropy and multiscale dispersion entropy is investigated, together with the superiority of MCFS for feature selection.

Seismic noise attenuation based on waveform classification

Traditionally, a denoising algorithm is usually applied to enhance useful reflection seismic signals, or to increase the quality of the waveforms recorded in the seismic data. The strong noise left in the filtered data after denoising is a long standing drawback of many state-of-the-art denoising algorithms. In addition to removing noise, we are proposing a new way to detect the waveforms, which can also enhance the useful signals. We propose a waveform classification algorithm based on a clustering method, e.g., to classify a data point in the data into signal or noise. The waveform classification algorithm can be applied directly to the noisy data to solely output the picked signals and thus can be a complete substitute to a traditional denoising algorithm. A better way for “denoising” is to pick the signals from a traditional denoising algorithm with the labels classified from the noisy data. At the same time, we also propose a novel automatic denoising method based on the empirical wavelet transform (EWT). We use the EWT method as a template to show how the proposed waveform classification method can be combined with a traditional denoising algorithm to output a superb denoised signal.

Application of a new EWT-based denoising technique in bearing fault diagnosis

The vibration signal analysis is a popular method for extracting sensitive fault features. The vibration signals are usually contaminated by noise, and therefore the extracted features cannot be providing sufficient information about the bearing faults. In this paper, a new technique is introduced for denoising the vibration signals and recognizing the bearing faults based on the empirical wavelet transform (EWT). Firstly, the vibration signals are decomposed by the EWT method into a set of functions called the empirical modes. Then, the noise-dominate modes have been denoised by an improved thresholding function that has been recently presented. Finally, the kurtosis parameter and the envelope spectrum of the denoised signal are used for early fault detection and diagnosing the fault type, respectively. The result of the simulated signal and different experimental datasets illustrate that the presented work is preferable for the empirical mode decomposition based denoising technique in the early fault detection.

Fault Feature Extraction and Enhancement of Rolling Element Bearings Based on Maximum Correlated Kurtosis Deconvolution and Improved Empirical Wavelet Transform

In order to extract and enhance the weak fault feature of rolling element bearings in strong noise conditions, the Empirical Wavelet Transform (EWT) is improved and a novel fault feature extraction and enhancement method is proposed by combining the Maximum Correlated Kurtosis Deconvolution (MCKD) and improved EWT method. At first, the MCKD method is conducted to de-noise the signal by eliminating the non-impact components. Then, the Fourier spectrum is segmented by local maxima or minima in the envelope of the amplitude spectrum with a pre-set threshold based on the noise level. By building up the wavelet filter banks based on the spectrum segmentation result, the signal is adaptively decomposed into several sub-signals. Finally, by choosing the most meaningful sub-signal with the maximum kurtosis, the fault feature can be extracted in the squared envelope spectrum and teager energy operator spectrum of the chosen component. Both simulations and experiments are performed to validate the effectiveness of the proposed method. It is shown that the spectrum segmentation result of improved EWT is more reasonable than the traditional EWT in strong noise conditions. Furthermore, compared with commonly used methods, such as the Fast Kurtogram (FK) and the Optimal Wavelet Packet Transform (OWPT) method, the proposed method is more effective in the fault feature extraction and enhancement of rolling element bearings.

Motor Imagery EEG Signals Classification Based on Mode Amplitude and Frequency Components Using Empirical Wavelet Transform

As one of the key techniques determining the overall system performances, efficient and reliable algorithms for improving the classification accuracy of motor imagery (MI) based electroencephalography (EEG) signals are highly desired for the development of brain-computer interface (BCI) systems. In this study, we propose, for the first time to the best of our knowledge, a novel data adaptive empirical wavelet transform (EWT) based signal decomposition method for improving the classification accuracy of MI based EEG signals. Specifically, to reduce the system complexity and execution time, the proposed method selects 18 electrodes out of 118 to analyze the non-stationary and nonlinear EEG signal behaviors. Meanwhile, the method adopts the Welch power spectral density (PSD) analysis method for single mode selection out of the total 10 for each channel, and the Hilbert transform (HT) method for both instantaneous amplitude (IA) and instantaneous frequency (IF) signal components extraction for each selected mode. With seven commonly used machine-learning classifiers adopted, extensive experiments were conducted with the benchmark dataset IVa from BCI competition III to evaluate the performance of the proposed method. Results show that with the IA and IF component features being tested using the least-square support vector machine (LS-SVM) classifier, the EWT method achieves an average classification accuracy of 95.2% and 94.6% respectively, which is higher as compared with the existing methods. While for every participant, a classification accuracy of at least 80% could be achieved by employing a single feature only. Results also show that a combination of EWT and higher order statistics features, which contain both kurtosis and skewness of the extracted instantaneous components, help achieve a higher success rate. The better performances of EWT over those of the existing methods demonstrate the effectiveness and great potential of EWT for BCI system applications.

Frequency Phase Space Empirical Wavelet Transform for Rolling Bearings Fault Diagnosis

As one of the most important components in rotating machinery, rolling bearings are fragile due to their harsh working environment. The empirical wavelet transform (EWT) method has been used to identify bearing faults by constructing an adaptive filter bank and decomposing the vibration signal into different modes. However, the EWT method separates different parts based on the local extremum of the Fourier spectrum. This segmentation strategy is highly susceptible to other components such as gears, and sometimes the fault characteristics of bearings cannot be detected. To overcome the above drawbacks, frequency phase space empirical wavelet transform (FPSEWT) method is proposed in this paper, which divides the spectrum into several parts and uses the Teager energy distribution as a reference for dividing the Fourier spectrum. Then, the differential search (DS) algorithm is applied to automatically identify the optimal right and left boundaries of the sensitive frequency band. The effectiveness of the proposed approach is verified by both simulated signals and experimental data. The fault feature ratio (FFR) values of sensitive components increased from 8.08% to 18.67%, which indicate that the proposed method can successfully extract fault symptoms of rolling element bearings in the presence of environmental disturbance.

An Improved Empirical Wavelet Transform and Its Applications in Rolling Bearing Fault Diagnosis

As essential but easily damaged parts of rotating machinery, rolling bearings have been deeply researched and widely used in mechanical processes. The real-time detection of bearing state and simple, rapid, and accurate diagnosis of bearing fault are indispensable to the industrial system. The bearing’s inner ring and outer ring vibration acceleration can be measured by high-precision sensors, and the running state of the bearing can be effectively extracted. The empirical wavelet transform (EWT) can adaptively decompose the vibration acceleration signal into a series of empirical modes. However, this method not only runs slowly, but also causes inexplicable empirical modes due to the unreasonable boundaries of the frequency domain division. In this paper, a new method is proposed to improve the empirical wavelet transform by dividing the boundaries from the spectrum, named the fast empirical wavelet transform (FEWT). The proposed method chooses different points in the Fourier transform of the spectrum (key function) to reconstruct the trend component of the spectrum. The minimum points in the trend component divide the spectrum into a series of bands. A more reasonable set of boundaries can be found by choosing appropriate trend components to obtain effective empirical modes. The simulation results show that the proposed method is effective and that the acquired empirical mode is more reasonable than the EWT method. Combining kurtosis with fault feature extraction of inner and outer rings of bearings, the method is successfully applied to the fault diagnosis of rolling bearings.

A Novel Adaptive Signal Processing Method Based on Enhanced Empirical Wavelet Transform Technology.

Empirical wavelet transform (EWT) is a novel adaptive signal decomposition method, whose main shortcoming is the fact that Fourier segmentation is strongly dependent on the local maxima of the amplitudes of the Fourier spectrum. An enhanced empirical wavelet transform (MSCEWT) based on maximum-minimum length curve method is proposed to realize fault diagnosis of motor bearings. The maximum-minimum length curve method transforms the original vibration signal spectrum to scale space in order to obtain a set of minimum length curves, and find the maximum length curve value in the set of the minimum length curve values for obtaining the number of the spectrum decomposition intervals. The MSCEWT method is used to decompose the vibration signal into a series of intrinsic mode functions (IMFs), which are processed by Hilbert transform. Then the frequency of each component is extracted by power spectrum and compared with the theoretical value of motor bearing fault feature frequency in order to determine and obtain fault diagnosis result. In order to verify the effectiveness of the MSCEWT method for fault diagnosis, the actual motor bearing vibration signals are selected and the empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) methods are selected for comparative analysis in here. The results show that the maximum-minimum length curve method can enhance EWT method and the MSCEWT method can solve the shortcomings of the Fourier spectrum segmentation and can effectively decompose the bearing vibration signal for obtaining less number of intrinsic mode function (IMF) components than the EMD and EEMD methods. It can effectively extract the fault feature frequency of the motor bearing and realize fault diagnosis. Therefore, the study provides a new method for fault diagnosis of rotating machinery.