A Joint Long Short-Term Memory and AdaBoost regression approach with application to remaining useful life estimation

Zhu, Xiaoyan; Zhang, Ping; Xie, Min

doi:10.1016/j.measurement.2020.108707

Cited by 40 publications

(19 citation statements)

References 46 publications

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“…AdaBoost is a statistical classification and regression algorithm that works by sequentially generating multiple regressors to finalize a weighted model [73]. The model can automatically adjust the weights based on estimation errors; therefore, it has great potential for addressing nonlinear, complicated regression problems [74]. AdaBoost develops numerical models by altering the distribution of the parent sample.…”

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confidence: 99%

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Prediction of Oil Palm Yield Using Machine Learning in the Perspective of Fluctuating Weather and Soil Moisture Conditions: Evaluation of a Generic Workflow

Khan

Kamaruddin

Sheikh

et al. 2022

Plants

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Current development in precision agriculture has underscored the role of machine learning in crop yield prediction. Machine learning algorithms are capable of learning linear and nonlinear patterns in complex agro-meteorological data. However, the application of machine learning methods for predictive analysis is lacking in the oil palm industry. This work evaluated a supervised machine learning approach to develop an explainable and reusable oil palm yield prediction workflow. The input data included 12 weather and three soil moisture parameters along with 420 months of actual yield records of the study site. Multisource data and conventional machine learning techniques were coupled with an automated model selection process. The performance of two top regression models, namely Extra Tree and AdaBoost was evaluated using six statistical evaluation metrics. The prediction was followed by data preprocessing and feature selection. Selected regression models were compared with Random Forest, Gradient Boosting, Decision Tree, and other non-tree algorithms to prove the R2 driven performance superiority of tree-based ensemble models. In addition, the learning process of the models was examined using model-based feature importance, learning curve, validation curve, residual analysis, and prediction error. Results indicated that rainfall frequency, root-zone soil moisture, and temperature could make a significant impact on oil palm yield. Most influential features that contributed to the prediction process are rainfall, cloud amount, number of rain days, wind speed, and root zone soil wetness. It is concluded that the means of machine learning have great potential for the application to predict oil palm yield using weather and soil moisture data.

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confidence: 99%

“…tomatically adjust the weights based on estimation errors; therefore, it has great potential for addressing nonlinear, complicated regression problems [74]. AdaBoost develops numerical models by altering the distribution of the parent sample.…”

mentioning

confidence: 99%

Prediction of Oil Palm Yield Using Machine Learning in the Perspective of Fluctuating Weather and Soil Moisture Conditions: Evaluation of a Generic Workflow

Khan

Kamaruddin

Sheikh

et al. 2022

Plants

View full text Add to dashboard Cite

show abstract

“…Kim et al [12] proposed a CNN-based (convolutional neural network, CNN) prediction model to reflect the correlation between RUL estimation and a health status detection process. Based on data trajectory expansion, a joint datadriven RUL prediction method using the AdaBoost regression model and the long-short term memory (LSTM) model was established by Zhu et al [13]. Liu et al [14] used stacked bidirectional LSTM to establish a data-based model to predict the RUL of supercapacitors.…”

Section: Introductionmentioning

confidence: 99%

Remaining Useful Life Prediction Method for Bearings Based on LSTM with Uncertainty Quantification

Yang

Peng

Xie

et al. 2022

Sensors

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To reduce the economic losses caused by bearing failures and prevent safety accidents, it is necessary to develop an effective method to predict the remaining useful life (RUL) of the rolling bearing. However, the degradation inside the bearing is difficult to monitor in real-time. Meanwhile, external uncertainties significantly impact bearing degradation. Therefore, this paper proposes a new bearing RUL prediction method based on long-short term memory (LSTM) with uncertainty quantification. First, a fusion metric related to runtime (or degradation) is proposed to reflect the latent degradation process. Then, an improved dropout method based on nonparametric kernel density is developed to improve estimation accuracy of RUL. The PHM2012 dataset is adopted to verify the proposed method, and comparison results illustrate that the proposed prediction model can accurately obtain the point estimation and probability distribution of the bearing RUL.

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“…Since hierarchical deep features are learned for raw input data progressively, deep learning is more suitable and powerful to discover intricate data patterns [ 18 ]. Deep belief network (DBN) [ 19 ], LSTM [ 20 ], Convolutional Neural Network (CNN) [ 21 ], and Stacked Autoencoder (SAE) [ 22 ] are some of the most popular deep learning models that have performed successfully in complex modeling tasks. In [ 23 ], two different models are conducted to soft sensing the industrial noxious gas, experiment demonstrates DBN based soft sensor method is more robust with less hyper-parameter than PCA based multilayer perceptron neural network.…”

Section: Introductionmentioning

confidence: 99%

Mechanism Analysis and Self-Adaptive RBFNN Based Hybrid Soft Sensor Model in Energy Production Process: A Case Study

Zhang

Yang

et al. 2022

Sensors

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Despite hard sensors can be easily used in various condition monitoring of energy production process, soft sensors are confined to some specific scenarios due to difficulty installation requirements and complex work conditions. However, industrial process may refer to complex control and operation, the extraction of relevant information from abundant sensors data may be challenging, and description of complicated process data patterns is also becoming a hot topic in soft-sensor development. In this paper, a hybrid soft sensor model based mechanism analysis and data-driven is proposed, and ventilation sensing of coal mill in a power plant is conducted as a case study. Firstly, mechanism model of ventilation is established via mass and energy conservation law, and object-relevant features are identified as the inputs of data-driven method. Secondly, radial basis function neural network (RBFNN) is used for soft sensor modeling, and genetic algorithm (GA) is adopted for quick and accurate determination of the RBFNN hyper-parameters, thus self-adaptive RBFNN (SA-RBFNN) is proposed to improve the soft sensor performance in energy production process. Finally, effectiveness of the proposed method is verified on a real-world power plant dataset, taking coal mill ventilation soft sensing as a case study.

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A Joint Long Short-Term Memory and AdaBoost regression approach with application to remaining useful life estimation

Cited by 40 publications

References 46 publications

Prediction of Oil Palm Yield Using Machine Learning in the Perspective of Fluctuating Weather and Soil Moisture Conditions: Evaluation of a Generic Workflow

Prediction of Oil Palm Yield Using Machine Learning in the Perspective of Fluctuating Weather and Soil Moisture Conditions: Evaluation of a Generic Workflow

Remaining Useful Life Prediction Method for Bearings Based on LSTM with Uncertainty Quantification

Mechanism Analysis and Self-Adaptive RBFNN Based Hybrid Soft Sensor Model in Energy Production Process: A Case Study

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