Remaining useful life prediction for equipment based on RF-BiLSTM

Wu, Zhiqiang; Wang, Zhenxi; Wei, Huihui; Ren, Jianji; Yuan, Yongliang; Wang, Taijie; Duan, Wenxian; Wei, Hefan; Wang, Shukai

doi:10.1063/5.0125885

Cited by 7 publications

(2 citation statements)

References 28 publications

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“…There are two main types of combined models: superposition-based and weight-based. In the former, the output of one model is used as the input of another model, for example, the CNN-BiLSTM model ( Ma et al, 2023 ), the CEEDM-BiLSTM model ( Zhang et al, 2023 ), and the RF-BiLSTM model ( Wu et al, 2022 ). In contrast, the latter use sophisticated algorithms to optimize the weight of the computational model, including the PSO-LSTM model ( Zheng & Li, 2023 ), the MBES-LSM model ( Tuerxun et al, 2022 ), etc .…”

Section: Introductionmentioning

confidence: 99%

Daily natural gas load prediction method based on APSO optimization and Attention-BiLSTM

Qi,

Wang,

et al. 2024

PeerJ Computer Science

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As the economy continues to develop and technology advances, there is an increasing societal need for an environmentally friendly ecosystem. Consequently, natural gas, known for its minimal greenhouse gas emissions, has been widely adopted as a clean energy alternative. The accurate prediction of short-term natural gas demand poses a significant challenge within this context, as precise forecasts have important implications for gas dispatch and pipeline safety. The incorporation of intelligent algorithms into prediction methodologies has resulted in notable progress in recent times. Nevertheless, certain limitations persist. However, there exist certain limitations, including the tendency to easily fall into local optimization and inadequate search capability. To address the challenge of accurately predicting daily natural gas loads, we propose a novel methodology that integrates the adaptive particle swarm optimization algorithm, attention mechanism, and bidirectional long short-term memory (BiLSTM) neural networks. The initial step involves utilizing the BiLSTM network to conduct bidirectional data learning. Following this, the attention mechanism is employed to calculate the weights of the hidden layer in the BiLSTM, with a specific focus on weight distribution. Lastly, the adaptive particle swarm optimization algorithm is utilized to comprehensively optimize and design the network structure, initial learning rate, and learning rounds of the BiLSTM network model, thereby enhancing the accuracy of the model. The findings revealed that the combined model achieved a mean absolute percentage error (MAPE) of 0.90% and a coefficient of determination (R2) of 0.99. These results surpassed those of the other comparative models, demonstrating superior prediction accuracy, as well as exhibiting favorable generalization and prediction stability.

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Section: Introductionmentioning

confidence: 99%

Daily natural gas load prediction method based on APSO optimization and Attention-BiLSTM

Qi,

Wang,

et al. 2024

PeerJ Computer Science

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“…The RUL refers to the time that an equipment can operate normally after a period of normal operation. 1 Currently, there are three main types of RUL prediction modeling methods commonly used: model-based approaches, 2 data-driven approaches, 3 and based on hybrid model. 4 The model-based approaches need to establish a physical model according to the engine operating law and degradation process for prediction.…”

Section: Introductionmentioning

confidence: 99%

Engine remaining useful life prediction based on PSO optimized multi-layer long short-term memory and multi-source information fusion

Yuan,

Li,

et al. 2023

Measurement and Control

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Engine as the core component of mechanical equipment, its operating state directly affects whether the equipment can operate normally. Predicting the engine remaining useful life (RUL) can monitor the health of the engine in real time and formulate a timely and reasonable maintenance plan. Aiming at the engine monitoring data with various and long time span, we propose a direct prediction method of engine RUL based on particle swarm optimization (PSO) optimized multi-layer Long Short-Term Memory (LSTM) in this paper. Firstly, the monitoring data that can well reflect the engine degradation trend is screened out, and the samples are constructed through a sliding time window. Then, a multi-layer LSTM model is constructed to mine the deep-seated features of the samples for predicting the engine RUL. Finally, the hyperparameters of the multi-layer LSTM model are optimized automatically by the PSO algorithm to optimize the performance of the model. The effectiveness of this method is verified by NASA data set. RMSE, MAE and the scoring function are used as evaluation indexes. RMSE and score of the prediction results are 12.35 and 284.1, respectively. It has higher prediction accuracy compared with traditional deep learning and machine learning methods.

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Combined improved tuna swarm optimization with graph convolutional neural network for remaining useful life of engine

Yuan,

Yang,

Wang

et al. 2024

Quality & Reliability Eng

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Accurate prediction of the engine's remaining useful life (RUL) is essential to ensure the safe operation of the aircraft because. However, traditional deep‐learning based methods for RUL prediction has been limited by interpretability and adjustment for hyperparameters in practical applications due to the intricate potential relations during the degradation process. To address these dilemmas, an improved multi‐strategy tuna swarm optimization‐assisted graph convolutional neural network (IMTSO‐GCN) is developed to achieve RUL prediction in this work. Specifically, mutual information is used to describe potential relationships among measured parameters so that they could be utilized in building edges for these parameters. Besides, considering that not all relational nodes will positively affect the RUL prediction and the inherent hyperparameters of the GCN are high‐dimensional. Inspired by “No Free Lunch (NFL)”, IMTSO is proposed to reduce the optimization cost of hyperparameters, in which cycle chaotic mapping is employed to achieve initialization of the population for improving the uniformity of the initial population distribution. Besides, a novel adaptive approach is proposed to enhance the exploration and exploitation of tuna swarm optimization (TSO). The CMAPSS dataset was used to validate the effectiveness and advancedness of IMTSO‐GCN, and the experimental results show that the R2 of the IMTSO‐GCN is greater than 0.99, RMSE is less than 3, the Score error is within 1.

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Remaining useful life prediction for equipment based on RF-BiLSTM

Cited by 7 publications

References 28 publications

Daily natural gas load prediction method based on APSO optimization and Attention-BiLSTM

Daily natural gas load prediction method based on APSO optimization and Attention-BiLSTM

Engine remaining useful life prediction based on PSO optimized multi-layer long short-term memory and multi-source information fusion

Combined improved tuna swarm optimization with graph convolutional neural network for remaining useful life of engine

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