At present, the large-scale railway maintenance equipment adopts a diesel engine as the main power plant. Therefore the diesel engine in the event of failure, will seriously affect the large-scale railway maintenance equipment of the normal work. Exploring advanced diesel engine condition monitoring and fault diagnosis technology and looking for practical and effective diesel engine fault diagnosis method, which has already become a research subject widely concerned by many experts at home and abroad. In this paper, genetic algorithm (GA) is used to optimize the parameters of radial basis function (RBF) neural network for diesel engine fault diagnosis, experimental results show the validity of this prediction method, and the accuracy of the proposed algorithm was verified by comparative.
College entrance examination and general education play important roles in university study. Yet, the mechanism of how they impact the students' graduation achievement is still not clear. This paper uses the students' test scores enrolled in a university in Yunnan Province, China, from 2012 to 2015 as the sample object. By establishing a multiple linear regression model, the paper explores the impact of college entrance examination scores and general education scores on graduation scores. The linear regression model established in this study shows that college entrance examinations and public courses have a significant impact on graduation achievements. Finally, through the case study of 13,607 sample data, the predicted results are basically the same as the actual results, which further illustrates this research's reliability and accuracy.
Conducting the remaining useful life (RUL) prediction for an aircraft engines is of significant importance in enhancing aircraft operation safety and formulating reasonable maintenance plans. Addressing the issue of low prediction model accuracy due to traditional neural networks’ inability to fully extract key features, this paper proposes an engine RUL prediction model based on the adaptive moment estimation (Adam) optimized self-attention mechanism–temporal convolutional network (SAM-TCN) neural network. Firstly, the raw data monitored by sensors are normalized, and RUL labels are set. A sliding window is utilized for overlapping sampling of the data, capturing more temporal features while eliminating data dimensionality. Secondly, the SAM-TCN neural network prediction model is constructed. The temporal convolutional network (TCN) neural network is used to capture the temporal dependency between data, solving the mapping relationship of engine degradation characteristics. A self-attention mechanism (SAM) is employed to adaptively assign different weight contributions to different input features. In the experiments, the root mean square error (RMSE) values on four datasets are 11.50, 16.45, 11.62, and 15.47 respectively. These values indicate further reduction in errors compared to methods reported in other literature. Finally, the SAM-TCN prediction model is optimized using the Adam optimizer to improve the training effectiveness and convergence speed of the model. Experimental results demonstrate that the proposed method can effectively learn feature data, with prediction accuracy superior to other models.
Extracting effective features from high-dimensional datasets is crucial for determining the accuracy of regression and classification models. Model predictions based on causality are known for their robustness. Thus, this paper introduces causality into feature selection and utilizes Feature Selection based on NOTEARS causal discovery (FSNT) for effective feature extraction. This method transforms the structural learning algorithm into a numerical optimization problem, enabling the rapid identification of the globally optimal causality diagram between features and the target variable. To assess the effectiveness of the FSNT algorithm, this paper evaluates its performance by employing 10 regression algorithms and 8 classification algorithms for regression and classification predictions on six real datasets from diverse fields. These results are then compared with three mainstream feature selection algorithms. The results indicate a significant average decline of 54.02% in regression prediction achieved by the FSNT algorithm. Furthermore, the algorithm exhibits exceptional performance in classification prediction, leading to an enhancement in the precision value. These findings highlight the effectiveness of FSNT in eliminating redundant features and significantly improving the accuracy of model predictions.
Aeroengines use numerous sensors to detect equipment health and ensure proper operation. Currently, filtering useful sensor data and removing useless data is challenging in predicting the remaining useful life (RUL) of an aeroengine using deep learning. To reduce computational costs and improve prediction performance, we use random forest to evaluate the feature importance of sensor data. Based on the size of the feature corresponding to the Gini index, we select the appropriate sensor. This helps us to determine which sensor to use and ensures that the computational resources are not wasted on unnecessary sensors. Considering that the RUL of equipment changes in a progressively more complex manner as the equipment is used over time, we propose an improved squeeze and excitation block (SSE) and combine it with a convolutional neural network (CNN). By enhancing the feature selection ability of CNN through segmented squeeze and excitation block, the model can focus on important information within features to effectively improve prediction performance. We compared our experiments with other RUL experiments on the CMAPSS aeroengine dataset and then conducted ablation experiments to verify the critical role of the methods we used.
Abstract Since it is easy to overfit due to the long training time of the fault diagnosis model for machinery. Introducing the idea of autoencoder (AE) into the wavelet extreme learning machine (WELM) and then stacking to form WELM-AE can convert the underlying fault features to more abstract and advanced ones. And then the adaptive boosting kernel extreme learning machine (Adaboost-KELM) is used as the top-level classifier for fault recognition. The experimental results verify the feasibility of the proposed algorithm in the fault diagnosis of tamping machine with the characteristics of the fast training speed of the extreme learning machine, and a higher accuracy rate than back propagation (BP), support vector machine (SVM), stacked autoencoder (SAE), and convolutional neural networks (CNN).
Accurately predicting the remaining useful life (RUL) of lithium-ion batteries holds significant importance for their health management. Due to the capacity regeneration phenomenon and random interference during the operation of lithium-ion batteries, a single model may exhibit poor prediction accuracy and generalization performance under a single scale signal. This paper proposes a method for predicting the RUL of lithium-ion batteries. The method is based on the improved sparrow search algorithm (ISSA), which optimizes the variational mode decomposition (VMD) and long- and short-term time-series network (LSTNet). First, this study utilized the ISSA-optimized VMD method to decompose the capacity degradation sequence of lithium-ion batteries, acquiring global degradation trend components and local capacity recovery components, then the ISSA–LSTNet–Attention model and ISSA–LSTNet–Skip model were employed to predict the trend component and capacity recovery component, respectively. Finally, the prediction results of these different models were integrated to accurately estimate the RUL of lithium-ion batteries. The proposed model was tested on two public lithium-ion battery datasets; the results indicate a root mean square error (RMSE) under 2%, a mean absolute error (MAE) under 1.5%, and an absolute correlation coefficient (R2) and Nash–Sutcliffe efficiency index (NSE) both above 92.9%, implying high prediction accuracy and superior performance compared to other models. Moreover, the model significantly reduces the complexity of the series.
Abstract To better handle temporal data and delve into learning the features of the data, a turbofan engine residual life prediction method is proposed, which integrates a dual-squeeze-excitation attention mechanism with a multi-scale temporal convolutional network. Firstly, utilizing a sliding window, the extracted multi-dimensional sensor features undergo overlapping sampling to enhance the model’s perception of temporal data. Secondly, a hybrid network prediction model based on DSE-MTCN is constructed, employing multi-scale convolutional kernels to expand the receptive field of convolution, assigning different weights to features, and adaptively allocating weights to hidden layer units. Lastly, the DSE-MTCN prediction model is globally optimized using the RAdam algorithm. The results demonstrate that this method effectively enhances the accuracy and generalization ability of the prediction model.
Lithium battery health state estimation can help optimize battery usage and management strategies. In response to the challenges faced by traditional battery management systems in accurately estimating the State of Health of lithium-ion batteries and addressing issues such as capacity recovery and noise interference, this paper proposes a method based on wavelet decomposition and an improved whale optimization algorithm optimized deep extreme learning machine for estimating the SOH of lithium-ion batteries. Firstly, the lithium-ion battery capacity degradation sequence is extracted, and the wavelet decomposition method is used to decompose the battery capacity into global and local degradation trends. Next, the non-linear convergence factor and the whale optimization algorithm with adaptive weights are employed to optimize the deep extreme learning machine for predicting each trend component. Finally, the prediction results are effectively integrated to obtain the lithium-ion battery SOH. This experimental method is validated using NASA and CALCE datasets, and the results indicate that the root mean square error and mean absolute percentage error are both below 0.95%, with relative accuracy and absolute correlation coefficients exceeding 98%. This demonstrates the method’s excellent accuracy and robustness.
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