A Novel Deep Learning Framework Based RNN-SAE for Fault Detection of Electrical Gas Generator

Few-Shot Learning-Based Light-Weight WDCNN Model for Bearing Fault Diagnosis in Siamese Network

In this study, bearing fault diagnosis is performed with a small amount of data through few-shot learning. Recently, a fault diagnosis method based on deep learning has achieved promising results. Most studies required numerous training samples for fault diagnosis. However, at manufacturing sites, it is impossible to have enough training samples to represent all fault types under all operating conditions. In addition, most studies consider only accuracy, and models are complex and computationally expensive. Research that only considers accuracy is inefficient since manufacturing sites change rapidly. Therefore, in this study, we propose a few-shot learning model that can effectively learn with small data. In addition, a Depthwise Separable Convolution layer that can effectively reduce parameters is used together. In order to find an efficient model, the optimal hyperparameters were found by adjusting the number of blocks and hyperparameters, and by using a Depthwise Separable Convolution layer for the optimal hyperparameters, it showed higher accuracy and fewer parameters than the existing model.

Machine Learning for Industry 4.0: A Systematic Review Using Deep Learning-Based Topic Modelling

Machine learning (ML) has a well-established reputation for successfully enabling automation through its scalable predictive power. Industry 4.0 encapsulates a new stage of industrial processes and value chains driven by smart connection and automation. Large-scale problems within these industrial settings are a prime example of an environment that can benefit from ML. However, a clear view of how ML currently intersects with industry 4.0 is difficult to grasp without reading an infeasible number of papers. This systematic review strives to provide such a view by gathering a collection of 45,783 relevant papers from Scopus and Web of Science and analysing it with BERTopic. We analyse the key topics to understand what industry applications receive the most attention and which ML methods are used the most. Moreover, we manually reviewed 17 white papers of consulting firms to compare the academic landscape to an industry perspective. We found that security and predictive maintenance were the most common topics, CNNs were the most used ML method and industry companies, at the moment, generally focus more on enabling successful adoption rather than building better ML models. The academic topics are meaningful and relevant but technology focused on making ML adoption easier deserves more attention.

Neural Component Analysis for Key Performance Indicator Monitoring

The partial least squares (PLS) algorithm is a commonly used key performance indicator (KPI)-related performance monitoring method. To address nonlinear features in the process, this paper proposes neural component analysis (NCA)-PLS, which combines PLS with NCA. (NCA)-PLS realizes all the principles of PLS by introducing a new loss function and a new principal component selection mechanism to NCA. Then, the gradient descent formulas for network training are rederived. NCA-PLS can extract components with large correlations with KPI variables and adopt them for data reconstruction. Simulation tests using a mathematical model and the Tennessee Eastman process show that NCA-PLS can successfully handle nonlinear relationships in process data and that it performs much better than PLS, KPLS, and NCA.

Well Performance Classification and Prediction: Deep Learning and Machine Learning Long Term Regression Experiments on Oil, Gas, and Water Production

In the oil and gas industries, predicting and classifying oil and gas production for hydrocarbon wells is difficult. Most oil and gas companies use reservoir simulation software to predict future oil and gas production and devise optimum field development plans. However, this process costs an immense number of resources and is time consuming. Each reservoir prediction experiment needs tens or hundreds of simulation runs, taking several hours or days to finish. In this paper, we attempt to overcome these issues by creating machine learning and deep learning models to expedite the process of forecasting oil and gas production. The dataset was provided by the leading oil producer, Saudi Aramco. Our approach reduced the time costs to a worst-case of a few minutes. Our study covered eight different ML and DL experiments and achieved its most outstanding R2 scores of 0.96 for XGBoost, 0.97 for ANN, and 0.98 for RNN over the other experiments.

Data-Driven Forecasting and Modeling of Runoff Flow to Reduce Flood Risk Using a Novel Hybrid Wavelet-Neural Network Based on Feature Extraction

The reliable forecasting of river flow plays a key role in reducing the risk of floods. Regarding nonlinear and variable characteristics of hydraulic processes, the use of data-driven and hybrid methods has become more noticeable. Thus, this paper proposes a novel hybrid wavelet-neural network (WNN) method with feature extraction to forecast river flow. To do this, initially, the collected data are analyzed by the wavelet method. Then, the number of inputs to the ANN is determined using feature extraction, which is based on energy, standard deviation, and maximum values of the analyzed data. The proposed method has been analyzed by different input and various structures for daily, weekly, and monthly flow forecasting at Ellen Brook river station, western Australia. Furthermore, the mean squares error (MSE), root mean square error (RMSE), and the Nash-Sutcliffe efficiency (NSE) is used to evaluate the performance of the suggested method. Furthermore, the obtained findings were compared to those of other models and methods in order to examine the performance and efficiency of the feature extraction process. It was discovered that the proposed feature extraction model outperformed their counterparts, especially when it came to long-term forecasting.