Achieving Predictive and Proactive Maintenance for High-Speed Railway Power Equipment With LSTM-RNN

Wang, Qi; Bu, Siqi; He, Zhengyou

doi:10.1109/tii.2020.2966033

Cited by 83 publications

(30 citation statements)

References 27 publications

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“…These neural networks are tailored for a variety of different tasks, to name a few, recurrent neural networks (RNN) [ 34 ] and long short-term memory (LSTM) [ 35 , 36 ] are suitable for modeling sequential data and sequence recognition and prediction, region-based convolutional neural network (R-CNN) [ 37 ] as well as its variants and you only look once (YOLO) [ 38 ] are capable of tackling object detection problems, SegNet [ 39 ] is tailored for semantic segmentation tasks. Such models have more complex structures and modules, such as memory blocks in LSTM, to cope with more complicated problems [ 40 , 41 ]. Complex structures correspond to considerable computing resources consumptions.…”

Section: Methodsmentioning

confidence: 99%

Multilabel Image Classification Based Fresh Concrete Mix Proportion Monitoring Using Improved Convolutional Neural Network

Han

Jiao

Yin

2020

Sensors

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Proper and accurate mix proportion is deemed to be crucial for the concrete in service to implement its structural functions in a specific environment and structure. Neither existing testing methods nor previous studies have, to date, addressed the problem of real-time and full-scale monitoring of fresh concrete mix proportion during manufacturing. Green manufacturing and safety construction are hindered by such defects. In this study, a state-of-the-art method based on improved convolutional neural network multilabel image classification is presented for mix proportion monitoring. Elaborately planned, uniformly distributed, widely covered and high-quality images of concrete mixtures were collected as dataset during experiments. Four convolutional neural networks were improved or fine-tuned based on two solutions for multilabel image classification problems, since original networks are tailored for single-label multiclassification tasks, but mix proportions are determined by multiple parameters. Various metrices for effectiveness evaluation of training and testing all indicated that four improved network models showed outstanding learning and generalization ability during training and testing. The best-performing one was embedded into executable application and equipped with hardware facilities to establish fresh concrete mix proportion monitoring system. Such system was deployed to terminals and united with mechanical and weighing sensors to establish integrated intelligent sensing system. Fresh concrete mix proportion real-time and full-scale monitoring and inaccurate mix proportion sensing and warning could be achieved simply by taking pictures and feeding pictures into such sensing system instead of conducting experiments in laboratory after specimen retention.

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Section: Methodsmentioning

confidence: 99%

Multilabel Image Classification Based Fresh Concrete Mix Proportion Monitoring Using Improved Convolutional Neural Network

Han

Jiao

Yin

2020

Sensors

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“…The results obtained with the LSTM were superior when compared to the other models mentioned in the study. The same approach was used in predictive and proactive maintenance for high-speed rail power equipment [43]. Some architectures have good ability in predicting univariate or multivariate temporal series with LSTM and GRU networks [44][45][46].…”

Section: Mlp and Recurrent Networkmentioning

confidence: 99%

Comparing LSTM and GRU Models to Predict the Condition of a Pulp Paper Press

et al. 2021

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The accuracy of a predictive system is critical for predictive maintenance and to support the right decisions at the right times. Statistical models, such as ARIMA and SARIMA, are unable to describe the stochastic nature of the data. Neural networks, such as long short-term memory (LSTM) and the gated recurrent unit (GRU), are good predictors for univariate and multivariate data. The present paper describes a case study where the performances of long short-term memory and gated recurrent units are compared, based on different hyperparameters. In general, gated recurrent units exhibit better performance, based on a case study on pulp paper presses. The final result demonstrates that, to maximize the equipment availability, gated recurrent units, as demonstrated in the paper, are the best options.

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“…About LSTM and GRU layers, if too many network layers are selected, the calculation of the entire network will be large and more training time will be needed. According to [70], when both the accuracy of the prediction model and the training time are considered, the two LSTM network layers are suitable, so two network layers in LSTM are selected. Similarly, two network layers are selected in GRU.…”

Section: Relu(x) �mentioning

confidence: 99%

Short-Term Traffic Flow Prediction: A Method of Combined Deep Learnings

Ren

Chai

Yin

et al. 2021

Journal of Advanced Transportation

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Short-term traffic flow prediction can provide a basis for traffic management and support for travelers to make decisions. Accurate short-term traffic flow prediction also provides necessary conditions for the sustainable development of the traffic environment. Although the application of deep learning methods for traffic flow prediction has achieved good accuracy, the problem of combining multiple deep learning methods to improve the prediction accuracy of a single method still has a margin for in-depth research. In this article, a combined deep learning prediction (CDLP) model including two paralleled single deep learning models, CNN-LSTM-attention model and CNN-GRU-attention model, is established. In the model, a one-dimensional convolutional neural network (1DCNN) is used to extract traffic flow local trend features and RNN variants (LSTM and GRU) with attention mechanism are used to extract long temporal dependencies trend features. Moreover, a dynamic optimal weighted coefficient algorithm (DOWCA) is proposed to calculate the dynamic weights of CNN-LSTM-attention and CNN-GRU-attention with the goal of minimizing the sum of squared errors of the CDLP model. Then, the neuron number, loss function, optimization algorithm, and other parameters of the CDLP model are discussed and set through experiments. Finally, the training set and test set for the CDLP model are established through the processing of traffic flow data collected from the field. The CDLP model is trained and tested, and the prediction results of traffic flow are obtained and analyzed. It indicates that the CDLP model can fit the change trend of traffic flow very well and has better performance. Furthermore, under the same dataset, the results from the CDLP model are compared with baseline models. It is found that the CDLP model has higher prediction accuracy than baseline models.

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Achieving Predictive and Proactive Maintenance for High-Speed Railway Power Equipment With LSTM-RNN

Cited by 83 publications

References 27 publications

Multilabel Image Classification Based Fresh Concrete Mix Proportion Monitoring Using Improved Convolutional Neural Network

Multilabel Image Classification Based Fresh Concrete Mix Proportion Monitoring Using Improved Convolutional Neural Network

Comparing LSTM and GRU Models to Predict the Condition of a Pulp Paper Press

Short-Term Traffic Flow Prediction: A Method of Combined Deep Learnings

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