Bearing Fault Diagnosis Method Based on EEMD and LSTM

The condition monitoring and fault detection of rolling bearing are of great significance to ensure the safe and reliable operation of rotating machinery system.In the past few years, deep neural network (DNN) has been recognized as an effective tool to detect rolling bearing faults. However, It is too complex to directly feed the original vibration signal to the DNN neural network, and the accuracy of fault identification is not high. By using the signal preprocessing technology, the original signal can be effectively removed and preprocessed without losing the key diagnosis information. In this paper, a new EEMD-LSTM bearing fault diagnosis method is proposed, which combines the signal preprocessing technology with the EEMD method that can get clear fault feature signals, and LSTM technology to extract fault features automatically that improves the efficiency of fault feature extraction. In the case of small sample size, this method can significantly improve the accuracy of fault diagnosis

Modeling and Optimal Operation of Hydraulic, Wind and Photovoltaic Power Generation Systems

The transition to 100% renewable energy in the future is one of the most important ways of achieving "carbon peaking and carbon neutrality" and of reducing the adverse effects of climate change. In this process, the safe, stable and economical operation of renewable energy generation systems, represented by hydro-, wind and solar power, is particularly important, and has naturally become a key concern for researchers and engineers. Therefore, this book focuses on the fundamental and applied research on the modeling, control, monitoring and diagnosis of renewable energy generation systems, especially hydropower energy systems, and aims to provide some theoretical reference for researchers, power generation departments or government agencies

Fault Diagnosis of Rotating Machinery Bearings Based on Improved DCNN and WOA-DELM

A bearing is a critical component in the transmission of rotating machinery. However, due to prolonged exposure to heavy loads and high-speed environments, rolling bearings are highly susceptible to faults, Hence, it is crucial to enhance bearing fault diagnosis to ensure safe and reliable operation of rotating machinery. In order to achieve this, a rotating machinery fault diagnosis method based on a deep convolutional neural network (DCNN) and Whale Optimization Algorithm (WOA) optimized Deep Extreme Learning Machine (DELM) is proposed in this paper. DCNN is a combination of the Efficient Channel Attention Net (ECA-Net) and Bi-directional Long Short-Term Memory (BiLSTM). In this method, firstly, a DCNN classification network is constructed. The ECA-Net and BiLSTM are brought into the deep convolutional neural network to extract critical features. Next, the WOA is used to optimize the weight of the initial input layer of DELM to build the WOA-DELM classifier model. Finally, the features extracted by the Improved DCNN (IDCNN) are sent to the WOA-DELM model for bearing fault diagnosis. The diagnostic capability of the proposed IDCNN-WOA-DELM method was evaluated through multiple-condition fault diagnosis experiments using the CWRU-bearing dataset with various settings, and comparative tests against other methods were conducted as well. The results indicate that the proposed method demonstrates good diagnostic performance

Fault severity assessment of rolling bearings method based on improved VMD and LSTM

In order to solve the problem of selection of appropriate wavelet basis function and clearly show the physical meaning of Empirical Mode Decomposition (EMD), an improved Variational Mode Decomposition (VMD) method with Long Short-Term Memory (LSTM) neural network is proposed. With the Cuckoo Search (CS) algorithm, the central frequency updating rules of VMD are optimized. And the low efficiency and local optimum problem is avoided. Meanwhile the decomposition layer number is found by the instantaneous frequency theory. For improving the prediction accuracy in traditional regression prediction methods, a LSTM neural network is designed for regression prediction of time sequence characteristics. The proposed method is implemented on actual bearings data which is derived from the bearing laboratory of Case West Reserve University in the United States and the University of Cincinnati Bearing Data Center. The experimental results showed that the improved VMD method was more robust and more accurate than the other traditional methods. And it has some practical value for real application and guiding significance for theory

Noise eliminated ensemble empirical mode decomposition scalogram analysis for rotating machinery fault diagnosis

Rotating machinery is one type of major industrial component that suffers from various faults and damage due to the constant workload to which it is subjected. Therefore, a fast and reliable fault diagnosis method is essential for machine condition monitoring. Artificial intelligence can be applied in fault feature extraction and classification. It is crucial to use an effective feature extraction method to obtain most of the fault information and a robust classifier to classify those features. In this study, an improved method, noise-eliminated ensemble empirical mode decomposition (NEEEMD), was proposed to reduce the white noise in the intrinsic functions and retain the optimum ensembles. A convolution neural network (CNN) classifier was applied for classification because of its feature-learning ability. A generalised CNN architecture was proposed to reduce the model training time. The classifier input used was 64×64 pixel RGB scalogram samples. However, CNN requires a large amount of training data to achieve high accuracy and robustness. Deep convolution generative adversarial network (DCGAN) was applied for data augmentation during the training phase. To evaluate the effectiveness of the proposed feature extraction method, scalograms from the related feature extraction methods such as ensemble empirical mode decomposition (EEMD), complementary EEMD (CEEMD) and continuous wavelet transform (CWT) were also classified. The effectiveness of the scalograms was also validated by comparing the classifier performance using greyscale samples from the raw vibration signals. The ability of CNN was compared with two traditional machine learning algorithms, k nearest neighbour (kNN) and the support vector machine (SVM), using statistical features from EEMD, CEEMD and NEEEMD. The proposed method was validated using bearing and blade datasets. The results show that the machine learning algorithms achieved comparatively lower accuracy than the proposed CNN model. All the outputs from the bearing and blade fault classifiers demonstrated that the scalogram samples from the proposed NEEEMD method obtained the highest accuracy, sensitivity and robustness using CNN. DCGAN was applied with the proposed NEEEMD scalograms to enhance the CNN classifier’s performance further and identify the optimal amount of training data. After training the classifier using the augmented samples, the results showed that the classifier obtained even higher validation and test accuracy with greater robustness. The test accuracies improved from 98%, 96.31% and 92.25% to 99.6%, 98.29% and 93.59%, respectively, for the different classifier models using NEEEMD. The proposed method can be used as a more generalised and robust method for rotating machinery fault diagnosis

Fault diagnosis method for energy storage mechanism of high voltage circuit breaker based on CNN characteristic matrix constructed by sound-vibration signal

Aiming at the problem that some traditional high voltage circuit breaker fault diagnosis methods were over-dependent on subjective experience, the accuracy was not very high and the generalization ability was poor, a fault diagnosis method for energy storage mechanism of high voltage circuit breaker, which based on Convolutional Neural Network (CNN) characteristic matrix constructed by sound-vibration signal ,was proposed. In this paper, firstly, the morphological filtering was used for background noise cancellation of sound signal, and the time scale alignment method based on kurtosis and envelope similarity were proposed to ensure the synchronism of the sound-vibration signal. Secondly, the Pearson correlation coefficient was used to construct two-dimensional image characteristic matrix for the expanded sound-vibration signal. Finally, the characteristic matrix was trained by utilizing CNN. Local Response Normalization (LRN) and core function decorrelation were utilized to improve the structure of CNN model, which reduced the bad impact of large data fluctuation of energy storage process on the diagnostic accuracy of circuit breaker energy storage mechanism. Compared with the traditional method, the proposed method has obvious advantages, whose total accurate rate up to 98.2 % and generalization performance is excellent

Offshore wind speed short-term forecasting based on a hybrid method: swarm decomposition and meta-extreme learning machine.

As the share of global offshore wind energy in the electricity generation portfolio is rapidly increasing, the grid integration of large-scale offshore wind farms is becoming of interest. Due to the intermittency of wind, the stability of power systems is challenging. Therefore, accurate and fast offshore short-term wind speed forecasting tools play important role in maintaining reliability and safe operation of the power system. This paper proposes a novel hybrid offshore wind forecasting model based on swarm decomposition (SWD) and meta-extreme learning machine (Meta-ELM). This approach combines the advantages of SWD which has proven efficiency for non-stationary signals, with Meta-ELM which provides faster calculation with a lower computational burden. In order to enhance accuracy and stability, the signal is decomposed by implementing a swarm-prey hunting algorithm in SWD. To validate the model, a comparison against four conventional and state-of-the-art hybrid models is performed. The implemented models are tested on two real wind datasets. The results demonstrate that the proposed model outperforms the counterparts for all performance metrics considered. The proposed hybrid approach can also improve the performance of the Meta-ELM model as a well-known and robust method

Constructive Incremental Learning for Fault Diagnosis of Rolling Bearings with Ensemble Domain Adaptation

Given the prevalence of rolling bearing fault diagnosis as a practical issue across various working conditions, the limited availability of samples compounds the challenge. Additionally, the complexity of the external environment and the structure of rolling bearings often manifests faults characterized by randomness and fuzziness, hindering the effective extraction of fault characteristics and restricting the accuracy of fault diagnosis. To overcome these problems, this paper presents a novel approach termed constructive Incremental learning-based ensemble domain adaptation (CIL-EDA) approach. Specifically, it is implemented on stochastic configuration networks (SCN) to constructively improve its adaptive performance in multi-domains. Concretely, a cloud feature extraction method is employed in conjunction with wavelet packet decomposition (WPD) to capture the uncertainty of fault information from multiple resolution aspects. Subsequently, constructive Incremental learning-based domain adaptation (CIL-DA) is firstly developed to enhance the cross-domain learning capability of each hidden node through domain matching and construct a robust fault classifier by leveraging limited labeled data from both target and source domains. Finally, fault diagnosis results are obtained by a majority voting of CIL-EDA which integrates CIL-DA and parallel ensemble learning. Experimental results demonstrate that our CIL-DA outperforms several domain adaptation methods and CIL-EDA consistently outperforms state-of-art fault diagnosis methods in few-shot scenarios

Friction, Vibration and Dynamic Properties of Transmission System under Wear Progression

This reprint focuses on wear and fatigue analysis, the dynamic properties of coating surfaces in transmission systems, and non-destructive condition monitoring for the health management of transmission systems. Transmission systems play a vital role in various types of industrial structure, including wind turbines, vehicles, mining and material-handling equipment, offshore vessels, and aircrafts. Surface wear is an inevitable phenomenon during the service life of transmission systems (such as on gearboxes, bearings, and shafts), and wear propagation can reduce the durability of the contact coating surface. As a result, the performance of the transmission system can degrade significantly, which can cause sudden shutdown of the whole system and lead to unexpected economic loss and accidents. Therefore, to ensure adequate health management of the transmission system, it is necessary to investigate the friction, vibration, and dynamic properties of its contact coating surface and monitor its operating conditions

Local Change Point Detection and Signal Cleaning using EEMD with applications to Acoustic Shockwaves

The Ensemble Empirical Mode Decomposition (EEMD) has become a preferred technique to decompose nonlinear and non-stationary signals due to its ability to create time-varying basis functions. However, current EEMD signal cleaning techniques are unable to deal with situations where a signal only occurs for a portion of the entire recording length. By combining change point detection and statistical hypothesis testing, we demonstrate how to clean a signal to emphasize unique local changes within each basis function. This not only allows us to observe which frequency bands are undergoing a change, but also leads to improved recovery of the underlying information. Using this technique, we demonstrate improved signal cleaning performance for acoustic shockwave signal detection. The technique is implemented in R via the LCDSC package