Remaining Useful Life Prediction for a Roller in a Hot Strip Mill Based on Deep Recurrent Neural Networks

Jiao, Ruihua; Peng, Kaixiang; Dong, Jie

doi:10.1109/jas.2021.1004051

Cited by 45 publications

(14 citation statements)

References 33 publications

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“…(5.) MHMM [11] Modified HMM for TWM --Online MoG-HMM [12] Learns RUL distributions with MoG-HMM --HMM and NF [13] Predicts degradation with Neuro-Fuzzy ----HSMM [14] Uses HSMM to predict RUL ---HMM for TWM [15] Uses HMM to predict RUL ---HMM ensemble [16] HMM ensemble to predict RUL --Neo Fuzzy [17] Predicts RMS with Neuro-Fuzzy -----FDFDA [18] FD analysis and regression updating ---ANN for TWM [19] Convolutional ANN for wear classification -----RNN with HI [20] RNN for HI and RUL estimation ----Fault effects [21] Uses fault effects to predict RUL ---LSTM-SVM [22] LSTM-SVM for RUL prediction ---LSTM with PF [23] Uses LSTM networks and PF to predict RUL ---BDNN-RF [24] BDNN and RF for ball-bearing fault prediction ----Regression [25] Regression models for RUL estimation --EKM for TWM [26] EKF and regression to predict RUL --WPD-HMM [27] Log-likelihood of HMM as HI --AHMM [28] Adaptive HMM for TWM -Trigger regression [29] Triggered regression for RUL estimation -APCMD [30] Local and global regressions to predict RUL -HSIC [31] Changes in dependencies as degradation --Random Forest [32] Uses random forest to detect anomalies ---Genetic HMMs [33] Learns HMMs with genetic algorithms --AMBi-GAN [34] Uses GAN to detect anomalies --…”

Section: Papermentioning

confidence: 99%

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Asymmetric HMMs for Online Ball-Bearing Health Assessments

Puerto-Santana¹,

Bielza

Díaz-Rozo³

et al. 2022

IEEE Internet Things J.

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The degradation of critical components inside large industrial assets, such as ball-bearings, has a negative impact on production facilities, reducing the availability of assets due to an unexpectedly high failure rate. Machine learningbased monitoring systems can estimate the remaining useful life (RUL) of ball-bearings, reducing the downtime by early failure detection. However, traditional approaches for predictive systems require run-to-failure (RTF) data as training data, which in real scenarios can be scarce and expensive to obtain as the expected useful life could be measured in years. Therefore, to overcome the need of RTF, we propose a new methodology based on online novelty detection and asymmetrical hidden Markov models (As-HMM) to work out the health assessment. This new methodology does not require previous RTF data and can adapt to natural degradation of mechanical components over time in data-stream and online environments. As the system is designed to work online within the electrical cabinet of machines it has to be deployed using embedded electronics. Therefore, a performance analysis of As-HMM is presented to detect the strengths and critical points of the algorithm. To validate our approach, we use real life ball-bearing data-sets and compare our methodology with other methodologies where no RTF data is needed and check the advantages in RUL prediction and health monitoring. As a result, we showcase a complete end-to-end solution from the sensor to actionable insights regarding RUL estimation towards maintenance application in real industrial environments.

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Section: Papermentioning

confidence: 99%

“…A copula was used to generate a joint RUL distribution. [23] used a LSTM network to extract features to determine a normalized health index that predicted the RUL for rollers in hot rolling production. For the RUL estimation a state space model was used with a particle filtering (PF) algorithm for inference and prediction.…”

Section: This Articlementioning

confidence: 99%

Asymmetric HMMs for Online Ball-Bearing Health Assessments

Puerto-Santana¹,

Bielza

Díaz-Rozo³

et al. 2022

IEEE Internet Things J.

View full text Add to dashboard Cite

show abstract

“…The deficiencies above limit the prediction accuracy of shallow learning model. Yet the deep learning models possess strong abilities of robustness, generalization, extraction of deep features and complex mapping relationship, which are more suitable for realizing accurate prediction of strip thickness ( Wang et al, 2021 ; Kuźnar & Augustyn, 2021 ; Jiao, Peng & Dong, 2021 ; Xu et al, 2020 ; Li et al, 2020a ).…”

Section: Introductionmentioning

confidence: 99%

Strip thickness prediction method based on improved border collie optimizing LSTM

Sun

Lin²,

Zhou

et al. 2022

PeerJ Computer Science

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Background The thickness accuracy of strip is an important indicator to measure the quality of strip, and the control of the thickness accuracy of strip is the key for the high-quality strip products in the rolling industry. Methods A thickness prediction method of strip based on Long Short-Term Memory (LSTM) optimized by improved border collie optimization (IBCO) algorithm is proposed. First, chaotic mapping and dynamic weighting strategy are introduced into IBCO to overcome the shortcomings of uneven initial population distribution and inaccurate optimization states of some individuals in Border Collie Optimization (BCO). Second, Long Short-Term Memory (LSTM) which can effectively deal with time series data and alleviate long-term dependencies is adopted. What’s more, IBCO is utilized to optimize parameters to mitigate the influence of hyperparameters such as the number of hidden neurons and learning rate on the prediction accuracy of LSTM, so IBCO-LSTM is established. Results The experiments are carried out on the measured strip data, which proves the excellent prediction performance of IBCO-LSTM. The experiments are carried out on the actual strip data, which prove that IBCO-LSTM has excellent capability of prediction.

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“…Recurrent neural RNN algorithms achieve great success for optimal online solutions over the recent years. For example, an RNN model with the aid of the saddle-point theorem is utilized to dispose of the non-convex optimization problem, which is applied to the identification problem of genetic regulatory networks [5]. A deep RNN model is constructed to predict the residual life of the roller by exploiting a comprehensive health indicator [6].…”

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confidence: 99%