Elevator traction machine fault diagnosis based on improved CEEMD and SVM Algorithm

PurposeThis paper proposes an intelligent fault diagnosis method, which aims to obtain the outstanding fault diagnosis results of the gearbox.Design/methodology/approachAn intelligent fault diagnosis method based on energy entropy-weighted complementary ensemble empirical mode decomposition (EWCEEMD) and support vector machine (SVM) optimized by whale optimization algorithm (WOA) is proposed. The raw signal is first denoised by the wavelet noise reduction method. Then, complementary ensemble empirical mode decomposition (CEEMD) is used to generate several intrinsic mode functions (IMFs). Next, energy entropy is used as an indicator to measure the sensibility of the IMF and converted into a weight coefficient by function. After that, IMFs are linearly weighted to form the reconstruction signal, and several features are extracted from the new signal. Finally, the support vector machine optimized by the whale optimization algorithm (WOA-SVM) model is used for gearbox fault classification using feature vectors.FindingsThe fault features extracted by this method have a better clustering effect and clear boundaries under each fault mode than the unimproved method. At the same time, the accuracy of fault diagnosis is greatly improved.Originality/valueIn most studies of fault diagnosis, the sensitivity of IMF has not been appreciated. In this paper, energy entropy is chosen to quantify sensitivity. In addition, high classification accuracy can be achieved by applying WOA-SVM as the final classification model, improving the efficiency of fault diagnosis as well.

Resistance signals of a faulty building electrical system contain a large amount of information about the electrical systems operating status. However, it is difficult to extract the fault features completely because of their characteristics of nonlinearity and non-stationarity which brings a problem of a relatively low fault identification rate of the current fault diagnosis methods based on pattern recognition. Aiming at improving the accuracy of fault diagnosis further, this paper proposes a fault diagnosis method of a building electrical system based on the complementary ensemble empirical mode decomposition and mutual dimensionless index extraction (CEEMD-MDI) combined with the multi-kernel relevance vector machine (MK-RVM). First, the resistance signals of a faulty building electrical system are decomposed into a series of intrinsic mode functions (IMFs) by using an adaptive decomposition ability of the CEEMD. Second, the IMFs are used to extract the MDI and to form a feature vector with the resistance signal. Finally, the processed feature vector is input into the MK-RVM for modeling, and the fault diagnosis result of the building electrical system is provided in the form of a probability output. The experimental results show that the fault diagnosis accuracy rate of the proposed method based on CEEMD-MDI and MK-RVM can reach 97.22%, which has better fault diagnosis performance compared with other methods.

Due to the complexity of the working environment of wind power generation systems, wind turbine power converters (WPC) can experience different types of faults. Traditional fault diagnosis methods suffer from issues such as the need for additional hardware, low accuracy, long execution time, and applicability only to small sample offline fault diagnosis. In order to address these problems, this article proposes a particle swarm optimization-based support vector machine (SVM) algorithm. The algorithm combines PSO algorithm, SVM algorithm, and moving average algorithm to effectively improve the robustness and accuracy of the fault diagnosis algorithm, while reducing the execution time and cost. This article selects three-phase current signals and bus voltage signals as fault diagnosis data, and then uses the moving average algorithm to process the fault data of the power converter, retaining the data features based on effectively smoothing the data. Finally, an improved particle swarm algorithm is used to construct a fault diagnosis model based on support vector machines for diagnosing open circuit faults in the power converter. In a dataset containing 9800 training samples and 4200 testing samples, the accuracy of the training samples is 98.898%, and the accuracy of the testing samples is 98.4524%. This effectively solves the problem of traditional SVM methods being only able to handle small batches of nonlinear datasets. Finally, this article compares the proposed fault diagnosis method with other types and similar types of fault diagnosis methods, verifying the effectiveness and superiority of the proposed approach.

In this study, a data-driven fault diagnosis method was developed for solid oxide fuel cell (SOFC) systems. First, the complete experimental data was obtained following the design of the SOFC system experiments. Then, principal component analysis (PCA) was performed to reduce the dimensionality of the obtained experimental data. Finally, the fault diagnosis algorithms were designed by support vector machine (SVM) and BP neural network to identify and prevent the reformer carbon deposition and heat exchanger rupture faults, respectively. The research results show that both SVM and BP fault diagnosis algorithms can achieve online fault identification. The PCA + SVM algorithm was compared with the SVM algorithm, BP algorithm, and PCA + BP algorithm, and the results show that the PCA + SVM algorithm is superior in terms of running time and accuracy, the diagnosis accuracy reached more than 99%, and the running time was within 20 s. The corresponding system optimization scheme is also proposed.

To improve forecasting accuracy for photovoltaic (PV) power output, this paper proposes a hybrid method for forecasting the short-term PV power output. First, by introducing the noise level, an improved complementary ensemble empirical mode decomposition (EEMD) with adaptive noise (ICEEMDAN) is developed to determine the ensemble size and amplitude of the added white noise adaptively. ICEEMDAN can change PV power output with non-symmetry into intrinsic mode functions (IMFs) with symmetry. ICEEMDAN can enhance the forecasting accuracy for PV power by IMFs with physical meaning (not including spurious modes). Second, the selection method of relative modes (IF), which is determined by the comprehensive factor, including the shape factor, crest factor and Kurtosis, is introduced to adaptively classify the IMFs into groups including similar fluctuating components. The IF can avoid the drawbacks of threshold determination by an empirical method. Third, the modified particle swarm optimization (PSO) (MPSO) is proposed to optimize the hyper-parameters in the support vector machine (SVM) by introducing the piecewise inertial weight. MPSO can improve the global and local search ability to make the particles traverse the global space and strengthen the performance of local convergence. Finally, the proposed method (ICEEMDAN-IF-MPSO-SVM) is used to forecast the PV power output of each group individually, and then, the single forecasting result is reconstructed to obtain the desired forecasting result for PV power output. By comparison with the other typical methods, the proposed method is more suitable for forecasting PV power output.

Rotating devices such as gears often generate fault vibration signals during eration. Under the interference of strong external noise, it is often difficult to extract these weak fault feature. To this end, a method based on Complementary Ensemble Empirical Mode Decomposition (CEEMD) and Singular Spectrum Analysis (SSA) is proposed to reduce the noise of the gear signals. Firstly, original vibration signals are decomposed into a series of intrinsic mode functions (IMFs) by CEEMD, and the reconstructed signals are obtained after the some IMFs are removed according to evaluation coefficients. Then, the reconstructed signals are further denoised using SSA. After singular value decomposition, grouping and reconstruction, the denoised time series are obtained. The actual gear vibration signals from the QPZZ-II rotating machinery fault simulation rig are used to verify the effectiveness of the proposed method. The results of comparing with the different denoising methods show that the presented method can effectively depress the strong noise, and extract weak fault feature.

According to statistics from the Global Burden of Cancer Study (Globocon) of the World Health Organization (WHO), cancer, particularly breast cancer, is a severe health issue in Indonesia with 68,858 new cases and 22,000 deaths recorded in 2020. Ultrasonography (USG) technology is acknowledged as one of the potentials to support early detection, which is vital in reducing mortality from breast cancer. This study focuses on classifying ultrasound images using the Support Vector Machine (SVM) algorithm, GLCM feature extraction, Min-Max normalization, and Mutual Information with SelectKBest Feature Selection. From several experiments using the SVM algorithm with various combinations of parameter values that have been set and different Tests, namely using a Train/Test Split with a proportion of 80/20 and K-Fold Cross Validation, it shows that the SVM algorithm is capable of classifying ultrasound images of breast cancer. into two categories (Benign Tumor and Malignant Tumor) with the same maximum accuracy of 79% after applying the SMOTE Balancing Data technique or without using the Balancing Data technique. As a result, the Support Vector Machine (SVM) algorithm has the potential to be an effective model for identifying breast cancer ultrasound images, both on data from the original set that has not been balanced and data from the set that has been balanced.

Accurately identifying faults in rolling bearing systems by analyzing vibration signals, which are often nonstationary, is challenging. To address this issue, a new approach based on complementary ensemble empirical mode decomposition (CEEMD) and time series modeling is proposed in this paper. This approach seeks to identify faults appearing in a rolling bearing system using proper autoregressive (AR) model established from the nonstationary vibration signal. First, vibration signals measured from a rolling bearing test system with different defect conditions are decomposed into a set of intrinsic mode functions (IMFs) by means of the CEEMD method. Second, vibration signals are filtered with calculated filtering parameters. Third, the IMF which is closely correlated to the filtered signal is selected according to the correlation coefficient between the filtered signal and each IMF, and then the AR model of the selected IMF is established. Subsequently, the AR model parameters are considered as the input feature vectors, and the hidden Markov model (HMM) is used to identify the fault pattern of a rolling bearing. Experimental study performed on a bearing test system has shown that the presented approach can accurately identify faults in rolling bearings.

Aiming at the difficulty of accurate prediction due to the randomness and nonstationary nature of blood glucose concentration series, a blood glucose concentration prediction model based on complementary ensemble empirical mode decomposition (CEEMD) and least squares support vector machine (LSSVM) is proposed. Firstly, CEEMD is used to convert the blood glucose concentration sequence into a series of intrinsic mode functions (IMFs) to reduce the impact of randomness and nonstationary signals on prediction performance. Then, a LSSVM prediction model is established for each mode IMF. The comprehensive learning particle swarm optimization (CLPSO) algorithm is used to optimize the kernel parameters of LSSVM. Finally, the prediction results of all IMFs are superimposed to yield the final blood glucose concentration prediction value. The experimental results show that the proposed prediction model has higher prediction accuracy in short-term blood glucose concentration values.

Planetary gearboxes are widely used in the modern mechanical transmission equipment. The fault diagnosis of the planetary gearboxes is an important work. When the planetary gearbox fault occurs, its vibration signal has the characteristics of complex frequency components and amplitude modulation-frequency modulation. In view of these problems, a signal demodulation analysis method based on complementary ensemble empirical mode decomposition (CEEMD) and Teager energy operator is proposed to extract instantaneous amplitude and instantaneous frequency information of the fault signals. First, the CEEMD method is used to decompose the vibration signals of the complex multi component planetary gearbox into a series of single component intrinsic mode functions (IMFs). Then, the Teager energy operator is used to estimate the amplitude envelope and the instantaneous frequency of each single component signal. The intrinsic mode function is selected as the sensitive component according to the fluctuation characteristic of the instantaneous frequency. Finally, the Fourier transformation is applied in the obtained amplitude envelope and instantaneous frequency of the sensitive component to identify the gear fault characteristic frequencies, and achieve the accurate diagnosis of the planetary gearbox fault. The effectiveness and superiority of the method is verified through the analysis of the fault simulation experiment data of the planetary gearbox.

Spatiotemporal Characteristics of Precipitation and Drought Variability in Taiwan Using Multi-dimensional Complementary Ensemble Empirical Mode Decomposition AbstractMost time series observed in natural systems are nonlinear and nonstationary, particularly under the influence of climate change. Taiwan has experienced increasingly frequent drought events in recent decades. Droughts are characterized by their gradual development, cumulative impacts, and lack of clear early warning signals, which makes their detection and analysis challenging. To address these issues, this study applies Multi-dimensional Complementary Ensemble Empirical Mode Decomposition (MCEEMD) to analyze long-term temperature and precipitation data in Taiwan from 1960 to 2023. MCEEMD is an effective time–frequency analysis method designed for nonlinear and nonstationary time series. It enables the decomposition of multi-dimensional signals into a set of Intrinsic Mode Functions (IMFs), allowing both spatial and temporal characteristics of climate variables to be examined. Through these IMFs, meaningful instantaneous frequencies and long-term trends in the signals can be identified. This study considers both stochastic and deterministic influences by reconstructing the IMFs into two components based on their autocorrelation coefficients. The relationships between temperature, precipitation variability, and drought-related characteristics are then examined, providing insights into the spatiotemporal behavior of drought events in Taiwan. Key word: Multi-dimensional Complementary Ensemble Empirical Mode Decomposition(MCEEMD ),Intrinsic Mode Functions (IMFs)

Dilihat dari sisi konsumen, ada berbagai aspek yang mempengaruhi konsumen untuk membeli suatu kendaraan bermotor. Hal ini karena masing-masing orang memiliki minat dan kebutuhan yang berbeda. Oleh karena itu, tujuan dari penelitian ini adalah untuk mengetahui kelompok umur dan profesi apa saja yang menjadi pelanggan potensial di SPS Motor Honda Lombok Timur. Metode penelitian yang digunakan adalah metode analitik deskriptif, dimana peneliti mengambil data-data penjualan sepeda motor dari SPS Motor Honda Lombok Timur, yang selanjutnya diolah dan dianalisis menggunakan software Rapidminer 5. Hasil analisis data tersebut selanjutnya dideskripsikan dengan detail mengenai kelompok pelanggan potensial. Berdasarkan analisis yang telah dilakukan, dapat ditarik kesimpulan bahwa kelompok yang termasuk dalam pelanggan potensial dari SPS Motor Honda Lombok Timur adalah kelompok dengan kriteria umur berkisar 33-46 tahun yang berprofesi sebagai wiraswasta dan petani, Hal ini disebabkan karena, pelanggan tersebut memiliki penghasilan dengan usia produktif. Sedangkan kelompok pelanggan yang tidak potensial ada 2 irisan yaitu yang pertama apabila kategori usia produktif tapi tidak memiliki penghasilan dengan rentang umur 17-32 tahun dengan profesi pelajar dan wiraswasta. Kedua yaitu, apabila kategori usia tidak produktif dengan rentang usia 47-73 tahun dan tidak memiliki penghasilan dengan profesi petani dan wiraswata.DOI : 10.29408/jit.v2i2.1447

Aiming at the problem of a large number of switch machines, harsh working environment and high frequency of equipment failures caused by many factors, in order to accurately diagnose railway switch machine faults, a switch machine fault diagnosis method based on adaptive neural fuzzy network (ANFIS) optimized by dynamic weight particle swarm (DPSO) algorithm is proposed by analyzing the vibration signals generated during the operation of the switch machine. Firstly, the working vibration signal is decomposed into several intrinsic mode functions (IMFs) and selected by using the ensemble empirical mode decomposition (EEMD) algorithm; then, the feature values of IMFs are extracted by using the improved time domain multiscale scatter extraction (ITMDE) algorithm, and then input into the optimized ANFIS model to learn and realize fault diagnosis; finally, the method is compared and analyzed with various diagnostic model algorithms and learning algorithms. The experimental results show that the proposed method can effectively diagnose switch machine faults, which has certain reference significance for intelligent diagnosis of switch machine faults and related research in the future.

In order to improve the accuracy of the prediction model of lung tuberculosis, a prediction model of lung tuberculosis based on grayscale mixed nucleus AR-SVM model is proposed in the Thesis. First, early warning model of the incidence trend of lung tuberculosis was constructed by using support vector machine (SVM) algorithm which includes two circumstances. One is that there is no extreme risk preference; the other is that there is extreme risk preference. And the searching was conducted in the optimum sorting process in training set based on SVM algorithm; Secondly, aimed at the problem that early warning of extreme risk preference of SVM model is easily out of order in the prediction of incidence trend of lung tuberculosis, data record of incidence trend of lung tuberculosis was preprocessed by using improved SVM model and SVM algorithm was improved by polynomial kernel SVM, which achieved improvement of prediction performance of autoregression model of sample data; finally, the effectiveness of algorithm is verified by positive analysis.

Aim: prediction of coronary disease using novel support vector machine and comparing its accuracy with logistic regression algorithm. Materials and methods: Two social affairs are proposed for predicting the accuracy (%) of coronary disease. To be explicit, the novel supports vector machine and logistic regression algorithms. Here we take 20 samples each for appraisal and compare. The sample size was calculated using G power with pretest power at 80% and the alpha of 0.05 value. Result: The logistic regression gives better precision (87.82%) than the novel support vector machine (SVM) accuracy (81.30%). Thus the real significance of logistic regression is better than novel support vector machine algorithms. Conclusion: From the result, it might be gathered that logistic regression helps in expecting the coronary sickness with more accuracy to appear differently in relation to novel support vector machine algorithms.