Detection and isolation of single and mixed faults in a gearbox are very important to enhance the system reliability, lifetime, and service availability. This paper proposes a hybrid learning ...algorithm, consisting of multilayer perceptron (MLP)- and convolutional neural network (CNN)-based classifiers, for diagnosis of gearbox mixed faults. Domain knowledge features are required to train the MLP classifier, while the CNN classifier can learn features itself, allowing to reduce the required knowledge features for the counterpart. Vibration data from an experimental setup with gearbox mixed faults is used to validate the effectiveness of the algorithms and compare them with conventional methods. The comparative study shows that accuracies and robustness of the individual MLP and CNN algorithms are better than those of the compared methods and can be significantly improved using data fusion at the feature level. Furthermore, the robustness of the algorithm is secured under noises by combining the results of individual classifiers.
Accurate carbon price forecasting is of great significance for policy-makers and market participants. However, previous studies only focus on point-valued forecasting and ignore the importance of ...interval carbon price forecasting. In fact, interval-valued forecasting contains more information and can measure the uncertainty and variability of carbon price. Thus, to predict interval carbon price accurately, we propose a novel interval decomposition ensemble model based on multivariate variational mode decomposition (MVMD) and interval multilayer perceptron (iMLP) optimized by Jaya algorithm. Firstly, MVMD is used to decompose the original interval carbon price series into several sub-series. Secondly, iMLP optimized by Jaya algorithm is constructed to predict each sub-series of the above step. Finally, forecasting results of each sub-series are aggerated into the ultimate predictions of interval carbon price by linear addition. The interval carbon price data from two carbon markets in China are utilized to validate the effectiveness of the proposed model. Experimental results reveal that the proposed model outperforms all the benchmark models and the average values of the interval U of Theil statistics (UI) and the interval average relative variance (ARVI) in two datasets are 0.3003 and 0.0569, respectively. Overall, the proposed model can be used as an effective tool for future interval carbon price forecasting.
•Propose a novel model to predict interval carbon price: MVMD-Jaya-iMLP.•MVMD is applied to decompose the original interval carbon price.•Jaya algorithm is employed to optimize the initial weights and biases of iMLP.•MVMD improves the forecasting accuracy.•The proposed model outperforms other benchmark models.
Fault diagnosis of rotating machinery is essential for maintaining system performance and ensuring the operation safety. Deep learning (DL) has been recently developed rapidly and achieved remarkable ...results in fault diagnosis. However, the temporal information from time-series signals is ignored by convolutional neural networks (CNNs) based methods. Besides, the robustness against the noise is essential to methods for fault diagnosis. Therefore, a novel method based on recurrent neural networks (RNNs) is proposed to identify fault types in rotating machinery in this paper. One-dimensional time-series vibration signals are first converted into two-dimensional images. Then, Gated Recurrent Unit (GRU) is introduced to exploit temporal information of time-series data and learn representative features from constructed images. A multilayer perceptron (MLP) is finally employed to implement fault recognition. Experimental results show that the proposed method achieves the best performance on two public datasets compared with existing work and exhibits the robustness against the noise.
•A Gated Recurrent Unit (GRU) based method for fault diagnosis is proposed.•A linear layer, GRU and a multilayer perceptron are included to detect fault types.•Case studies show the effectiveness of our method and its robustness against noise.
This paper proposes a new hybrid stochastic training algorithm using the recently proposed grasshopper optimization algorithm (GOA) for multilayer perceptrons (MLPs) neural networks. The GOA ...algorithm is an emerging technique with a high potential in tackling optimization problems based on its flexible and adaptive searching mechanisms. It can demonstrate a satisfactory performance by escaping from local optima and balancing the exploration and exploitation trends. The proposed GOAMLP model is then applied to five important datasets: breast cancer, parkinson, diabetes, coronary heart disease, and orthopedic patients. The results are deeply validated in comparison with eight recent and well-regarded algorithms qualitatively and quantitatively. It is shown and proved that the proposed stochastic training algorithm GOAMLP is substantially beneficial in improving the classification rate of MLPs.
We present a machine learning based COVID-19 cough classifier which can discriminate COVID-19 positive coughs from both COVID-19 negative and healthy coughs recorded on a smartphone. This type of ...screening is non-contact, easy to apply, and can reduce the workload in testing centres as well as limit transmission by recommending early self-isolation to those who have a cough suggestive of COVID-19. The datasets used in this study include subjects from all six continents and contain both forced and natural coughs, indicating that the approach is widely applicable. The publicly available Coswara dataset contains 92 COVID-19 positive and 1079 healthy subjects, while the second smaller dataset was collected mostly in South Africa and contains 18 COVID-19 positive and 26 COVID-19 negative subjects who have undergone a SARS-CoV laboratory test. Both datasets indicate that COVID-19 positive coughs are 15%–20% shorter than non-COVID coughs. Dataset skew was addressed by applying the synthetic minority oversampling technique (SMOTE). A leave-p-out cross-validation scheme was used to train and evaluate seven machine learning classifiers: logistic regression (LR), k-nearest neighbour (KNN), support vector machine (SVM), multilayer perceptron (MLP), convolutional neural network (CNN), long short-term memory (LSTM) and a residual-based neural network architecture (Resnet50). Our results show that although all classifiers were able to identify COVID-19 coughs, the best performance was exhibited by the Resnet50 classifier, which was best able to discriminate between the COVID-19 positive and the healthy coughs with an area under the ROC curve (AUC) of 0.98. An LSTM classifier was best able to discriminate between the COVID-19 positive and COVID-19 negative coughs, with an AUC of 0.94 after selecting the best 13 features from a sequential forward selection (SFS). Since this type of cough audio classification is cost-effective and easy to deploy, it is potentially a useful and viable means of non-contact COVID-19 screening.
•A machine learning based COVID-19 cough classifier has been developed.•This classifier achieves the highest AUC of 0.98 from a residual based architecture.•Cough audio recordings are collected from all six continents of the globe.•COVID-19 positive coughs are 15% to 20% shorter than non-COVID coughs.•A special feature extraction technique preserves end-to-end time-domain patterns.
Recently, intelligent fault recognition means have been progressed rapidly and have attained marvelous achievement. Most of them have an assumption where those source and target domains have similar ...distributions. However, actual working conditions of bearing are variable, which makes the source domain and target domain data present large distribution discrepancy. In this paper, a new method named dilated convolution deep belief network-dynamic multilayer perceptron (DCDBN-DMLP) is proposed for bearing fault recognition under alterable running states. Firstly, dilated convolution deep belief network (DCDBN) is proposed to extract transferable characteristics from raw vibrational dataset of bearings under variable running conditions. The divergence between source domains and target domains is large because the dataset generally is gathered from multi-condition environments. Then, the multilayer domain adaptation and pseudo label technology are adopted to alleviate the cross and unequal quantity domains. Finally, dynamic multilayer perceptron (DMLP) is proposed to classify bearing faults, which is connected to DCDBN in a progressive manner. The performance of this proposed DCDBN-DMLP model is validated by three transfer tasks with bearing fault dataset. Experimental results present that the abundant transferable features can be learned by this proposed method, and its classification accuracies significantly outperform other methods by comparison.
•A novel method called DCDBN-DMLP is proposed for bearing fault diagnosis with transfer unsupervised learning under varying working conditions.•The dilated convolution deep belief network (DCDBN) is proposed to extract transferable characteristics from raw vibration dataset.•Dynamic multilayer perceptron (DMLP) is proposed to classify bearing faults, and three transfer tasks with bearing fault dataset are verified.
Estimation of absolute temperature distributions is crucial for many thermal processes in the nonlinear distributed parameter systems, such as predicting the curing temperature distribution of the ...chip, the temperature distribution of the catalytic rod, and so on. In this work, a spatiotemporal model based on the Karhunen-Loève (KL) decomposition, the multilayer perceptron (MLP), and the long short-term memory (LSTM) network, named KL-MLP-LSTM, is developed for estimating temperature distributions with a three-step procedure. Firstly, the infinite-dimensional model is transformed into a finite-dimensional model, where the KL decomposition method is used for dimension reduction and spatial basis functions extraction. Secondly, a novel MLP-LSTM hybrid time series model is constructed to deal with the two inherently coupled nonlinearities. Finally, the spatiotemporal temperature distribution model can be reconstructed through spatiotemporal synthesis. The effectiveness of the proposed model is validated by the data from a snap curing oven thermal process. Satisfactory agreement between the results of the current model and the other well-established model shows that the KL-MLP-LSTM model is reliable for estimating the temperature distributions during the thermal process.
•Liquid-gas flow was simulated in three different flow regimes by MCNP code.•The radiometric metering system consists of one 137Cs source and two NaI detectors.•Two methods of extracting different ...features from the registered data were proposed.•Two artificial neural network (ANN) models were implemented for each method.•Prediction the volume fractions with RMS error of less than 0.60 was obtained.
Determining the type of flow pattern and gas volumetric percentage with high precision is one of the vital topics for researchers in this field. For this, in this paper, three different types of liquid–gas two-phase flow regimes, namely annular, stratified, and homogenous were simulated in various gas volumetric percentages ranging from 5% to 90%. Simulations were performed by Monte Carlo N Particle (MCNP) code. Metering system includes one 137Cs sources, one Pyrex glass, and two NaI detectors to register the transmitted photons. Because the signals which are received from the MCNP simulations contain high-frequency noises, the Savitzky-Golay filter has been applied to solve this problem. Then, thirteen characteristics in time domain were extracted from the recorded data of both detectors. Since none of features were capable of completely separating the flow regimes, two methods as “extracting two different features from the recorded data of both detectors” and “extracting three features from the recorded data of both detectors” were proposed. Using these methods, many different separator cases were found and the best separator cases were distinguished via S parameter. Finally, two artificial neural network (ANN) models of multilayer perceptron (MLP) were implemented for each method to identify the flow regimes and approximate the gas volumetric percentages. The proposed methodology and networks could diagnose all flow patterns properly, and also predict the volumetric percentage with a root mean square error (RMSE) of less than 0.60. Increasing the precision of two-phase flow meter by extracting time-domain features and signal processing techniques is the most important advantage of this study.
In-situ remediation of total petroleum hydrocarbon (TPH) contaminated soils via Fenton oxidation is a promising approach. However, determining the proper injection amount of H2O2 and Fe source over ...the Fenton reaction in the complex geological conditions for in-situ TPH soil remediation remains a daunting challenge. Herein, we introduced a practical and novel approach using soft computational models, a multilayer perception artificial neural network (MPLNN), for predicting the TPH removal performance. In this study, we conducted 48 sets of TPH removal experiments using Fenton oxidation to determine the TPH removal performance of a wide range of different ground conditions and generated 336 data points. As a result, a negative Pearson correlation coefficient was obtained in the Fe injection mass and the natural presence of Fe mineral in the soil, indicating that the excess of Fe could significantly retarded the TPH removal performance in the Fenton reaction. In addition, the MPLNN model with 6-6-1 training using Scaled conjugate gradient backpropagation (SCG) with tangent sigmoid as the transfer function demonstrated a high accuracy for TPH removal prediction with the correlation determination of 0.974 and mean square error value of 0.0259. The optimized MPLNN model achieved less than 20% error for predicting TPH removal performance in actual TPH-contaminated soil via Fenton oxidation. Hence, the proposed MPLNN can be useful in improving the Fenton oxidation of TPH removal performance in-situ soil remediation.
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•48 sets of Fenton oxidation experiments were conducted for TPH removal, obtaining 336 data points.•TPH removal performance decreases as Fe concentration in soil increases.•A negative Pearson correlation was obtained for Fe mass and TPH removal performance.•Optimized MPLNN model achieved <20% error in predicting actual TPH removal performance.
Machine/Deep Learning (ML/DL) techniques have been applied to large data sets in order to extract relevant information and for making predictions. The performance and the outcomes of different ML/DL ...algorithms may vary depending upon the data sets being used, as well as on the suitability of algorithms to the data and the application domain under consideration. Hence, determining which ML/DL algorithm is most suitable for a specific application domain and its related data sets would be a key advantage. To respond to this need, a comparative analysis of well-known ML/DL techniques, including Multilayer Perceptron, K-Nearest Neighbors, Decision Tree, Random Forest, and Voting Classifier (or the Ensemble Learning Approach) for the prediction of parking space availability has been conducted. This comparison utilized Santander's parking data set, initiated while working on the H2020 WISE-IoT project. The data set was used in order to evaluate the considered algorithms and to determine the one offering the best prediction. The results of this analysis show that, regardless of the data set size, the less complex algorithms like Decision Tree, Random Forest, and KNN outperform complex algorithms such as Multilayer Perceptron, in terms of higher prediction accuracy, while providing comparable information for the prediction of parking space availability. In addition, in this paper, we are providing Top-K parking space recommendations on the basis of distance between current position of vehicles and free parking spots.
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