Just Another Attention Network for Remaining Useful Life Prediction of Rolling Element Bearings

Huang, Gangjin; Hua, Shungang; Zhou, Qiang; Li, Hongkun; Zhang, Yuanliang

doi:10.1109/access.2020.3036726

Cited by 9 publications

(6 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…O artigo de [12], utilizou o algoritmo k-NN para classificar sinais de corrente elétrica de um motor elétrico cujos rolamentos apresentaram diferentes condic ¸ões de falha. Para a realizac ¸ão dos experimentos, os autores utilizaram o conjunto de dados da SKF [13], que é constituído por sinais de corrente referentes a quatro condic ¸ões de falha em rolamentos: falha na pista externa, falha na pista interna, falha na esfera e falha no rolo. Segundo os autores, o algoritmo k-NN foi apresentou uma acurácia de 99,7%.…”

Section: Trabalhos Correlatosunclassified

Diagnóstico de Falhas em Rolamentos de Motores Elétricos com Base na Análise da Assinatura da Corrente do Motor

Oliveira,

Pinto,

Resende

2024

Anais Do XVI Congresso Brasileiro De Inteligência Computacional

View full text Add to dashboard Cite

This article presents a comparative analysis of the performance of classification algorithms applied to fault diagnosis in electric motor bearings. To represent the faults, 13 statistical descriptors of the electric current signals available in the Paderborn dataset were extracted. The classification problem consisted of nine failure classes in addition to the normal operating condition class. The models were built both in the time domain and in the time scale domain, using wavelet transform filters, Coiflet 5, Daubechies 4 (Daub 4) and Symlet 8, at predefined resolution levels. The k-NN, SVM and Decision Tree algorithms were used to build the models. The performance of the models was evaluated based on the metrics of accuracy, sensitivity and F1 score. The best accuracy results were obtained using the SVM algorithm, with Symlet and Coiflet 5 filters, reaching a value of 0.9967%. The articles main contribution, in addition to the use of Wavelets and SVM combined, was that it demonstrated results that were comparable when using electric current signals as entry to vibration signals that are already widely used in the industry and that the use of the Wavelet Transform, along which Machine Learning algorithms, when using good descriptors, can be a viable solution to implement diagnostic systems automatic detection of more accurate faults in electric motor bearings.

show abstract

Section: Trabalhos Correlatosunclassified

Diagnóstico de Falhas em Rolamentos de Motores Elétricos com Base na Análise da Assinatura da Corrente do Motor

Oliveira,

Pinto,

Resende

2024

Anais Do XVI Congresso Brasileiro De Inteligência Computacional

View full text Add to dashboard Cite

show abstract

“…First, the impact of different LSTM hidden layers on the prediction results is explored. Each hidden layer contains 80 hidden units [44], and the number of particles is selected as 300. The prediction results are evaluated using the above two prediction results evaluation indicators.…”

Section: ) Health Indicator Predicting Using Hpo-pf-lstmmentioning

confidence: 99%

Remaining Useful Life Prediction for Rolling Bearings With a Novel Entropy-Based Health Indicator and Improved Particle Filter Algorithm

et al. 2023

View full text Add to dashboard Cite

Due to the importance of bearings in modern machinery, the prediction of the remaining useful life (RUL) of rolling bearings has been widely studied. When predicting the RUL of rolling bearings in engineering practice, the RUL is usually predicted based on historical data, and as the historical data increases, the prediction results should be more accurate. However, the existing methods usually have the shortcomings of low prediction accuracy, large cumulative error and failure to dynamically give prediction results with the increase of historical data, which are not suitable for engineering practice.To address the above problems, a novel RUL prediction method is proposed. The proposed method consists of 3 parts: First, the multi-scale entropy-based feature -namely "average multi-scale morphological gradient power spectral information entropy (AMMGPSIE)"from the rolling bearings as the Health indicator (HI) is extracted to ensure all the fault-related information is well-included; Then, the HI is processed with the enhanced Hodrick Prescott trend-filtering with boundary lines (HPTF-BL) to ensure good performance and small fluctuation on the HI; Finally, the deterioration curve is predicted using an LSTM neural network and the improved Particle Filter algorithm that we proposed. The proposed method is validated using the experimental bearing degradation dataset and the casing data of a centrifugal pump bearing from an actual industrial site. Comparing the results with other recent RUL prediction methods, the proposed method achieved state-ofthe-art feasibility and effectiveness, conform to the needs of practical application of the project.

show abstract

“…To date, the LSTM algorithm has been successfully applied in various felds. Huang et al [16] selected the PRONOSTIA dataset as the database for RUL research and calculated the root mean square (RMS) of the full life-cycle bearing data as a degradation index. Meanwhile, the LSTM's attention mechanism was used to optimize the prognosis model.…”

Section: Introductionmentioning

confidence: 99%

Prediction on the Remaining Useful Life of Rolling Bearings Using Ensemble DLSTM

Jiang

Xiang

2023

Shock and Vibration

View full text Add to dashboard Cite

As a significant component of rotation machinery, bearing plays a role in supporting and transmitting power. However, bearings are subject to complex operating conditions and are prone to failure. To avoid ineffectiveness and improve the reliability of bearings, a data-driven method is used to predict the remaining useful life (RUL). However, this method is less stable and can only forecast the RUL of bearings under training sample conditions. An ensemble deep, long-term, and short-term memory (EDLSTM) method is proposed to solve this problem. First, the feature of the forecast-bearing RUL was extracted including time-domain features, frequency-domain energy features, and Shannon entropy. Then, a deep long- and short-term memory network prediction model of the bearing RUL was constructed. To resolve the instability of DLSTM predictions, multiple DLSTMs were ensembled using the maximum information component (MIC) criterion. The model i trained using bearing data with different failure modes under difficult operating conditions to improve the predictive stability of the model. Finally, an EDLSTM was constructed to achieve the bearing RUL prediction. In the prediction result of the training set, the cumulative relative accuracy (CRA) was above 0.9 for most of the bearings. According to the experimental results in the test set, the mean CRA was over 0.80. For some of the bearing’s RUL, the CRA was more than 0.90. The above results show that the proposed approach can effectively predict the RUL of a bearing and has a more stable prediction ability than the bagging integration method.

show abstract

Just Another Attention Network for Remaining Useful Life Prediction of Rolling Element Bearings

Cited by 9 publications

References 34 publications

Diagnóstico de Falhas em Rolamentos de Motores Elétricos com Base na Análise da Assinatura da Corrente do Motor

Diagnóstico de Falhas em Rolamentos de Motores Elétricos com Base na Análise da Assinatura da Corrente do Motor

Remaining Useful Life Prediction for Rolling Bearings With a Novel Entropy-Based Health Indicator and Improved Particle Filter Algorithm

Prediction on the Remaining Useful Life of Rolling Bearings Using Ensemble DLSTM

Contact Info

Product

Resources

About