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Journal articles on the topic 'Prediction of RUL'
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1
Peng, Cheng, Yufeng Chen, Qing Chen, Zhaohui Tang, Lingling Li, and Weihua Gui. "A Remaining Useful Life Prognosis of Turbofan Engine Using Temporal and Spatial Feature Fusion." Sensors 21, no. 2 (January 8, 2021): 418. http://dx.doi.org/10.3390/s21020418.
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The prognosis of the remaining useful life (RUL) of turbofan engine provides an important basis for predictive maintenance and remanufacturing, and plays a major role in reducing failure rate and maintenance costs. The main problem of traditional methods based on the single neural network of shallow machine learning is the RUL prognosis based on single feature extraction, and the prediction accuracy is generally not high, a method for predicting RUL based on the combination of one-dimensional convolutional neural networks with full convolutional layer (1-FCLCNN) and long short-term memory (LSTM) is proposed. In this method, LSTM and 1- FCLCNN are adopted to extract temporal and spatial features of FD001 andFD003 datasets generated by turbofan engine respectively. The fusion of these two kinds of features is for the input of the next convolutional neural networks (CNN) to obtain the target RUL. Compared with the currently popular RUL prediction models, the results show that the model proposed has higher prediction accuracy than other models in RUL prediction. The final evaluation index also shows the effectiveness and superiority of the model.
2
Peng, Cheng, Yufeng Chen, Qing Chen, Zhaohui Tang, Lingling Li, and Weihua Gui. "A Remaining Useful Life Prognosis of Turbofan Engine Using Temporal and Spatial Feature Fusion." Sensors 21, no. 2 (January 8, 2021): 418. http://dx.doi.org/10.3390/s21020418.
Full textAbstract:
The prognosis of the remaining useful life (RUL) of turbofan engine provides an important basis for predictive maintenance and remanufacturing, and plays a major role in reducing failure rate and maintenance costs. The main problem of traditional methods based on the single neural network of shallow machine learning is the RUL prognosis based on single feature extraction, and the prediction accuracy is generally not high, a method for predicting RUL based on the combination of one-dimensional convolutional neural networks with full convolutional layer (1-FCLCNN) and long short-term memory (LSTM) is proposed. In this method, LSTM and 1- FCLCNN are adopted to extract temporal and spatial features of FD001 andFD003 datasets generated by turbofan engine respectively. The fusion of these two kinds of features is for the input of the next convolutional neural networks (CNN) to obtain the target RUL. Compared with the currently popular RUL prediction models, the results show that the model proposed has higher prediction accuracy than other models in RUL prediction. The final evaluation index also shows the effectiveness and superiority of the model.
3
Gómez-Pau, Álvaro, Jordi-Roger Riba, and Manuel Moreno-Eguilaz. "Time Series RUL Estimation of Medium Voltage Connectors to Ease Predictive Maintenance Plans." Applied Sciences 10, no. 24 (December 17, 2020): 9041. http://dx.doi.org/10.3390/app10249041.
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The ageing process of medium voltage power connectors can lead to important power system faults. An on-line prediction of the remaining useful life (RUL) is a convenient strategy to prevent such failures, thus easing the application of predictive maintenance plans. The electrical resistance of the connector is the most widely used health indicator for condition monitoring and RUL prediction, even though its measurement is a challenging task because of its low value, which typically falls in the range of a few micro-ohms. At the present time, the RUL of power connectors is not estimated, since their electrical parameters are not monitored because medium voltage connectors are considered cheap and secondary devices in power systems, despite they play a critical role, so their failure can lead to important power flow interruptions with the consequent safety risks and economic losses. Therefore, there is an imperious need to develop on-line RUL prediction strategies. This paper develops an on-line method to solve this issue, by predicting the RUL of medium voltage connectors based on the degradation trajectory of the electrical resistance, which is characterized by analyzing the electrical resistance time series data by means of the autoregressive integrated moving average (ARIMA) method. The approach proposed in this paper allows applying predictive maintenance plans, since the RUL enables determining when the power connector must be replaced by a new one. Experimental results obtained from several connectors illustrate the feasibility and accuracy of the proposed approach for an on-line RUL prediction of power connectors.
4
Liu, Haiping, Jianjun Wu, Xiang Ye, Taijian Liao, and Minlin Chen. "A method based on Dempster-Shafer theory and support vector regression-particle filter for remaining useful life prediction of crusher roller sleeve." Mechanics & Industry 20, no. 1 (2019): 106. http://dx.doi.org/10.1051/meca/2018038.
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In order to solve the problem of accurately predicting the remaining useful life (RUL) of crusher roller sleeve under the partially observable and nonlinear nonstationary running state, a new method of RUL prediction based on Dempster-Shafer (D-S) data fusion and support vector regression-particle filter (SVR-PF) is proposed. First, it adopts the correlation analysis to select the features of temperature and vibration signal, and subsequently utilize wavelet to denoising the features. Lastly, comparing the prediction performance of the proposed method integrates temperature and vibration signal sources to predict the RUL with the prediction performance of single source and other prediction methods. The experiment results indicate that the proposed prediction method is capable of fusing different data sources to predict the RUL and the prediction accuracy of RUL can be improved when data are less available.
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Lu, Cun, Zheng Jian Gu, and Yuan Yan. "RUL Prediction of Lithium Ion Battery Based on ARIMA Time Series Algorithm." Materials Science Forum 999 (June 2020): 117–28. http://dx.doi.org/10.4028/www.scientific.net/msf.999.117.
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Lithium ion battery is a key component of energy storage system. Accurate and scientific prediction of its Remaining Useful Life (RUL) is an important factor to check the operation of energy storage system is whether reliable. ARIMA is an effective time series prediction processing method, which can be used to calculate battery RUL and its confidence interval. And the more predicted samples, the higher the prediction accuracy. Compared with the empirical model and support vector machine algorithm, the analysis results show that the support vector machine is over-fitting. For two sets of the experimental data, the absolute predictive error of ARIMA algorithm is approximately 1.2%, that of linear model is approximately 1.4%, and that of Verhulst model is approximately 7.5%, which verifies the accuracy of ARIMA time series model in predicting the RUL in long interval.
6
Pang, Xiaoqiong, Rui Huang, Jie Wen, Yuanhao Shi, Jianfang Jia, and Jianchao Zeng. "A Lithium-ion Battery RUL Prediction Method Considering the Capacity Regeneration Phenomenon." Energies 12, no. 12 (June 12, 2019): 2247. http://dx.doi.org/10.3390/en12122247.
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Prediction of Remaining Useful Life (RUL) of lithium-ion batteries plays a significant role in battery health management. Battery capacity is often chosen as the Health Indicator (HI) in research on lithium-ion battery RUL prediction. In the rest time of batteries, capacity will produce a certain degree of regeneration phenomenon, which exists in the use of each battery. Therefore, considering the capacity regeneration phenomenon in RUL prediction of lithium-ion batteries is helpful to improve the prediction performance of the model. In this paper, a novel method fusing the wavelet decomposition technology (WDT) and the Nonlinear Auto Regressive neural network (NARNN) model for predicting the RUL of a lithium-ion battery is proposed. Firstly, the multi-scale WDT is used to separate the global degradation and local regeneration of a battery capacity series. Then, the RUL prediction framework based on the NARNN model is constructed for the extracted global degradation and local regeneration. Finally, the two parts of the prediction results are combined to obtain the final RUL prediction result. Experiments show that the proposed method can not only effectively capture the capacity regeneration phenomenon, but also has high prediction accuracy and is less affected by different prediction starting points.
7
Qin, Aisong, Qinghua Zhang, Qin Hu, Guoxi Sun, Jun He, and Shuiquan Lin. "Remaining Useful Life Prediction for Rotating Machinery Based on Optimal Degradation Indicator." Shock and Vibration 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/6754968.
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Remaining useful life (RUL) prediction can provide early warnings of failure and has become a key component in the prognostics and health management of systems. Among the existing methods for RUL prediction, the Wiener-process-based method has attracted great attention owing to its favorable properties and flexibility in degradation modeling. However, shortcomings exist in methods of this type; for example, the degradation indicator and the first predicting time (FPT) are selected subjectively, which reduces the prediction accuracy. Toward this end, this paper proposes a new approach for predicting the RUL of rotating machinery based on an optimal degradation indictor. First, a genetic programming algorithm is proposed to construct an optimal degradation indicator using the concept of FPT. Then, a Wiener model based on the obtained optimal degradation indicator is proposed, in which the sensitivities of the dimensionless parameters are utilized to determine the FPT. Finally, the expectation of the predicted RUL is calculated based on the proposed model, and the estimated mean degradation path is explicitly derived. To demonstrate the validity of this model, several experiments on RUL prediction are conducted on rotating machinery. The experimental results indicate that the method can effectively improve the accuracy of RUL prediction.
8
Hao, Xiuhong, Shuqiang Wang, Mengfan Chen, and Deng Pan. "Remaining Useful Life Prediction of High-Frequency Swing Self-Lubricating Liner." Shock and Vibration 2021 (January 29, 2021): 1–12. http://dx.doi.org/10.1155/2021/8843374.
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The remaining useful life (RUL) prediction of self-lubricating spherical plain bearings is essential for replacement decision-making and the reliability of high-end equipment. The high-frequency swing self-lubricating liner (HSLL) is the key component of self-lubricating spherical plain bearings under high-frequency oscillation conditions. In this study, a RUL prediction method was proposed based on the Wiener process and grey system theory. First, the predictive processing of the wear depth was carried out using the grey model GM(1,1) to reduce the randomness and enhance the inherent regularity of the life test data. A degradation process model was established and the RUL was predicted online with the model parameter estimates based on the Bayesian updating strategy. Finally, examples were provided to elaborate the RUL prediction of the HSLL. The results show that the prediction accuracy of the proposed RUL prediction model is higher than that of the simple Wiener process during the entire residual life cycle of the HSLL. Based on the original wear data, the prediction accuracy of the RUL exhibited a strong dependence on prior samples and was relatively low owing to the larger deviation of the wear rate between the test sample and prior samples.
9
Kang, Ziqiu, Cagatay Catal, and Bedir Tekinerdogan. "Remaining Useful Life (RUL) Prediction of Equipment in Production Lines Using Artificial Neural Networks." Sensors 21, no. 3 (January 30, 2021): 932. http://dx.doi.org/10.3390/s21030932.
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Predictive maintenance of production lines is important to early detect possible defects and thus identify and apply the required maintenance activities to avoid possible breakdowns. An important concern in predictive maintenance is the prediction of remaining useful life (RUL), which is an estimate of the number of remaining years that a component in a production line is estimated to be able to function in accordance with its intended purpose before warranting replacement. In this study, we propose a novel machine learning-based approach for automating the prediction of the failure of equipment in continuous production lines. The proposed model applies normalization and principle component analysis during the pre-processing stage, utilizes interpolation, uses grid search for parameter optimization, and is built with multilayer perceptron neural network (MLP) machine learning algorithm. We have evaluated the approach using a case study research to predict the RUL of engines on NASA turbo engine datasets. Experimental results demonstrate that the performance of our proposed model is effective in predicting the RUL of turbo engines and likewise substantially enhances predictive maintenance results.
10
Mu, Zongyi, Yan Ran, Genbao Zhang, Hongwei Wang, and Xin Yang. "Remaining useful life prediction method for machine tools based on meta-action theory." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 235, no. 4 (March 11, 2021): 580–90. http://dx.doi.org/10.1177/1748006x211002544.
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Remaining useful life (RUL) is a crucial indictor to measure the performance degradation of machine tools. It directly affects the accuracy of maintenance decision-making, thus affecting operational reliability of machine tools. Currently, most RUL prediction methods are for the parts. However, due to the interaction among the parts, even RUL of all the parts cannot reflect the real RUL of the whole machine. Therefore, an RUL prediction method for the whole machine is needed. To predict RUL of the whole machine, this paper proposes an RUL prediction method with dynamic prediction objects based on meta-action theory. Firstly, machine tools are decomposed into the meta-action unit chains (MUCs) to obtain suitable prediction objects. Secondly, the machining precision unqualified rate (MPUR) control chart is used to conduct an out of control early warning for machine tools’ performance. At last, the Markov model is introduced to determine the prediction objects in next prediction and the Wiener degradation model is established to predict RUL of machine tools. According to the practical application, feasibility and effectiveness of the method is proved.
11
Anagiannis, Ioannis, Nikolaos Nikolakis, and Kosmas Alexopoulos. "Energy-Based Prognosis of the Remaining Useful Life of the Coating Segments in Hot Rolling Mill." Applied Sciences 10, no. 19 (September 29, 2020): 6827. http://dx.doi.org/10.3390/app10196827.
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The field of prognostic maintenance aims at predicting the remaining time for a system or component to continue being used under the desired performance. This time is usually named as Remaining Useful Life (RUL). The current study proposes a novel approach for the RUL estimation of coating segments placed on a hot rolling mill machine. A prediction method was developed, providing real-time updates of the RUL prediction during the rolling milling process. The proposed approach performs energy analysis on measurements of segment surface temperatures and hydraulic forces. It uses nonparametric statistical processes to update the predictions, within a prediction horizon/window, indicating the number of remaining products to be processed. To assess the probability of failure within the defined prediction window, Maximum Likelihood Estimation is used. The proposed methodology was implemented in a software prototype in the MATLAB environment and tested in an industrial use case coming from a steel parts manufacturer, facilitating testing and validation of the suggested approach. Real-world data were acquired from the operational machine, while the validation results support that the proposed methodology demonstrates reasonable performance and robustness against product type variations.
12
Guo, Haifeng, Aidong Xu, Kai Wang, Yue Sun, Xiaojia Han, Seung Ho Hong, and Mengmeng Yu. "Particle Filtering Based Remaining Useful Life Prediction for Electromagnetic Coil Insulation." Sensors 21, no. 2 (January 11, 2021): 473. http://dx.doi.org/10.3390/s21020473.
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Electromagnetic coils are one of the key components of many systems. Their insulation failure can have severe effects on the systems in which coils are used. This paper focuses on insulation degradation monitoring and remaining useful life (RUL) prediction of electromagnetic coils. First, insulation degradation characteristics are extracted from coil high-frequency electrical parameters. Second, health indicator is defined based on insulation degradation characteristics to indicate the health degree of coil insulation. Finally, an insulation degradation model is constructed, and coil insulation RUL prediction is performed by particle filtering. Thermal accelerated degradation experiments are performed to validate the RUL prediction performance. The proposed method presents opportunities for predictive maintenance of systems that incorporate coils.
13
Guo, Haifeng, Aidong Xu, Kai Wang, Yue Sun, Xiaojia Han, Seung Ho Hong, and Mengmeng Yu. "Particle Filtering Based Remaining Useful Life Prediction for Electromagnetic Coil Insulation." Sensors 21, no. 2 (January 11, 2021): 473. http://dx.doi.org/10.3390/s21020473.
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Electromagnetic coils are one of the key components of many systems. Their insulation failure can have severe effects on the systems in which coils are used. This paper focuses on insulation degradation monitoring and remaining useful life (RUL) prediction of electromagnetic coils. First, insulation degradation characteristics are extracted from coil high-frequency electrical parameters. Second, health indicator is defined based on insulation degradation characteristics to indicate the health degree of coil insulation. Finally, an insulation degradation model is constructed, and coil insulation RUL prediction is performed by particle filtering. Thermal accelerated degradation experiments are performed to validate the RUL prediction performance. The proposed method presents opportunities for predictive maintenance of systems that incorporate coils.
14
Chui, Kwok Tai, Brij B. Gupta, and Pandian Vasant. "A Genetic Algorithm Optimized RNN-LSTM Model for Remaining Useful Life Prediction of Turbofan Engine." Electronics 10, no. 3 (January 25, 2021): 285. http://dx.doi.org/10.3390/electronics10030285.
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Understanding the remaining useful life (RUL) of equipment is crucial for optimal predictive maintenance (PdM). This addresses the issues of equipment downtime and unnecessary maintenance checks in run-to-failure maintenance and preventive maintenance. Both feature extraction and prediction algorithm have played crucial roles on the performance of RUL prediction models. A benchmark dataset, namely Turbofan Engine Degradation Simulation Dataset, was selected for performance analysis and evaluation. The proposal of the combination of complete ensemble empirical mode decomposition and wavelet packet transform for feature extraction could reduce the average root-mean-square error (RMSE) by 5.14–27.15% compared with six approaches. When it comes to the prediction algorithm, the results of the RUL prediction model could be that the equipment needs to be repaired or replaced within a shorter or a longer period of time. Incorporating this characteristic could enhance the performance of the RUL prediction model. In this paper, we have proposed the RUL prediction algorithm in combination with recurrent neural network (RNN) and long short-term memory (LSTM). The former takes the advantages of short-term prediction whereas the latter manages better in long-term prediction. The weights to combine RNN and LSTM were designed by non-dominated sorting genetic algorithm II (NSGA-II). It achieved average RMSE of 17.2. It improved the RMSE by 6.07–14.72% compared with baseline models, stand-alone RNN, and stand-alone LSTM. Compared with existing works, the RMSE improvement by proposed work is 12.95–39.32%.
15
Zraibi, Brahim, Mohamed Mansouri, and Chafik Okar. "Comparing Single and Hybrid methods of Deep Learning for Remaining Useful Life Prediction of Lithium-ion Batteries." E3S Web of Conferences 297 (2021): 01043. http://dx.doi.org/10.1051/e3sconf/202129701043.
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The prediction lifetime of a Lithium-ion battery is able to be utilized as an early warning system to prevent the battery’s failure that makes it very significant for assuring safety and reliability. This paper represents a benchmark study that compares its RUL prediction results of single and hybrid methods with similar articles. We suggest a hybrid method, named the CNN-LSTM, which is a combination of Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), for predicting and improving the accuracy of the remaining useful life (RUL) of Lithium-ion battery. We selected three statistical indicators (MAE, R², and RMSE) to assess the results of performance prediction. Experimental validation is performed using the lithium-ion battery dataset from the NASA and results reveal that the effectiveness of the suggested hybrid method in reducing the prediction error and in achieving better RUL prediction performance compared to the other algorithms.
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Ge, Chenglong, Yuanchang Zhu, and Yanqiang Di. "Equipment remaining useful life prediction oriented symbiotic simulation driven by real-time degradation data." International Journal of Modeling, Simulation, and Scientific Computing 09, no. 02 (March 20, 2018): 1850009. http://dx.doi.org/10.1142/s1793962318500095.
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As an emerging simulation technology in the field of system modeling and simulation, the equipment symbiotic simulation has become research emphasis. In the field of equipment maintenance support, the outstanding problem of equipment remaining useful life (RUL) prediction is analyzed, i.e., the stable model parameters without self-evolution ability, which has become the primary factor that hinders self-adaptive prediction of equipment RUL. Combined with parallel systems theory, the equipment RUL prediction oriented symbiotic simulation framework is proposed on the basis of modeling analysis and Wiener state space model (SSM) is taken as the basic simulation model in the framework. Driven by the dynamic injected equipment degradation observation data, the model parameters are updated online by using expectation maximum (EM) algorithm and the data assimilation between simulation outputs and observation data is executed by using Kalman filter, so as to realize dynamic evolution of the simulation model. The simulation model evolution which makes the simulation outputs close to equipment real degradation state provides high fidelity model and data for predicting equipment RUL accurately. The framework is verified by the performance degradation data of a bearing. The simulation results show that the symbiotic simulation method can accurately simulate the equipment performance degradation process and the self-adaptive prediction of equipment RUL is realized on the basis of improving prediction accuracy, proving the feasibility and effectiveness of symbiotic simulation method.
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Hotait, Hassane, Xavier Chiementin, and Lanto Rasolofondraibe. "Intelligent Online Monitoring of Rolling Bearing: Diagnosis and Prognosis." Entropy 23, no. 7 (June 22, 2021): 791. http://dx.doi.org/10.3390/e23070791.
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This paper suggests a new method to predict the Remaining Useful Life (RUL) of rolling bearings based on Long Short Term Memory (LSTM), in order to obtain the degradation condition of the rolling bearings and realize the predictive maintenance. The approach is divided into three parts: the first part is the clustering to detect the damage state by the density-based spatial clustering of applications with noise. The second one is the health indicator construction which could give a better reflection of the bearing degradation tendency and is selected as the input for the prediction model. In the third part of the RUL prediction, the LSTM approach is employed to improve the accuracy of the prediction. The rationale of this work is to combine the two methods—the density-based spatial clustering of applications with noise and LSTM—to identify the abnormal state in rolling bearings, then estimate the RUL. The suggested method is confirmed by experimental data of bearing life cycle, and the RUL prediction results of the model LSTM are compared with the nonlinear au-regressive model with exogenous input model. In addition, the constructed health indicator is compared with the spectral kurtosis feature. The results demonstrated that the suggested method is more appropriate than the nonlinear au-regressive model with exogenous input model for the prediction of bearing RUL.
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Ahn, Gilseung, Hyungseok Yun, Sun Hur, and Siyeong Lim. "A Time-Series Data Generation Method to Predict Remaining Useful Life." Processes 9, no. 7 (June 26, 2021): 1115. http://dx.doi.org/10.3390/pr9071115.
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Accurate predictions of remaining useful life (RUL) of equipment using machine learning (ML) or deep learning (DL) models that collect data until the equipment fails are crucial for maintenance scheduling. Because the data are unavailable until the equipment fails, collecting sufficient data to train a model without overfitting can be challenging. Here, we propose a method of generating time-series data for RUL models to resolve the problems posed by insufficient data. The proposed method converts every training time series into a sequence of alphabetical strings by symbolic aggregate approximation and identifies occurrence patterns in the converted sequences. The method then generates a new sequence and inversely transforms it to a new time series. Experiments with various RUL prediction datasets and ML/DL models verified that the proposed data-generation model can help avoid overfitting in RUL prediction model.
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Guan, Qingluan, Xiukun Wei, Limin Jia, Ye He, and Haiqiang Zhang. "RUL Prediction of Railway PCCS Based on Wiener Process Model with Unequal Interval Wear Data." Applied Sciences 10, no. 5 (February 29, 2020): 1616. http://dx.doi.org/10.3390/app10051616.
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The railway pantograph carbon contact strip (PCCS) plays a critical role in collecting the electric current from the catenary to guarantee the steady power supply for the train. The catenary contacts with the PCCS and slides from one side to another side when the train runs on the track, which generates the wear on the surface of the PCCS. The thickness of the PCCS cannot be smaller than a lower limit for the sake of safety. Therefore, the remaining useful life (RUL) prediction of the PCCS is beneficial for the pantograph maintenance and inventory management. In this paper, the wear data from Guangzhou Metro are analyzed in the first place. After that, the challenge of predicting the RUL for PCCS from the unequal interval wear data is addressed. A Wiener-process-based wear model and the unequal interval weighted grey linear regression combined model (UIWGLRCM) are proposed for the RUL prediction of the PCCS. The case studies demonstrate the effectiveness of the proposed method via a comparison of RUL prediction with another available method.
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Zhiyong, Gao, Li Jiwu, and Wang Rongxi. "Prognostics uncertainty reduction by right-time prediction of remaining useful life based on hidden Markov model and proportional hazard model." Eksploatacja i Niezawodnosc - Maintenance and Reliability 23, no. 1 (January 2, 2021): 154–65. http://dx.doi.org/10.17531/ein.2021.1.16.
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Uncertainty is a key problem in remaining useful life (RUL) prediction, and measures to reduce uncertainty are necessary to make RUL prediction truly practical. In this paper, a right-time prediction method is proposed to reduce the prognostics uncertainty of mechanical systems under unobservable degradation. Correspondingly, the whole RUL prediction process is divided into three parts, including offline modelling, online state estimating and online life predicting. In the offline modelling part, hidden Markov model (HMM) and proportional hazard model (PHM) are built to map the whole degradation path. During operation, the degradation state of the object is estimated in real time. Once the last degradation state reached, the degradation characteristics are extracted, and the survival function is obtained with the fitted PHM. The proposed method is demonstrated on an engine dataset and shows higher accuracy than traditional method. By fusing the extracted degradation characteristics, the obtained survival function can be basis for optimal maintenance with lower uncertainty.
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Zhou, Funa, Jiayu Wang, and Yulin Gao. "DCA-Based Real-Time Residual Useful Life Prediction for Critical Faulty Component." Journal of Control Science and Engineering 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/8492139.
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Residual useful life (RUL) prediction is significant for condition-based maintenance. Traditional data-driven RUL prediction method can only predict fault trend of the system rather than RUL of a specific system component. Thus it cannot tell the operator which component should be maintained. The innovation of this paper is as follows: (1) Wavelet filtering based method is developed for early detection of slowly varying fault. (2) Designated component analysis is introduced as a feature extraction tool to define the fault precursor of a specific component. (3) Exponential life prediction model is established by nonlinear fitting of the historical RUL and the fault size characterized by the statistics used. Once online detection statistics is obtained, real-time RUL of the critical component can be predicted online. Simulation shows the effectiveness of this algorithm.
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Jia, Jianfang, Jianyu Liang, Yuanhao Shi, Jie Wen, Xiaoqiong Pang, and Jianchao Zeng. "SOH and RUL Prediction of Lithium-Ion Batteries Based on Gaussian Process Regression with Indirect Health Indicators." Energies 13, no. 2 (January 13, 2020): 375. http://dx.doi.org/10.3390/en13020375.
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The state of health (SOH) and remaining useful life (RUL) of lithium-ion batteries are two important factors which are normally predicted using the battery capacity. However, it is difficult to directly measure the capacity of lithium-ion batteries for online applications. In this paper, indirect health indicators (IHIs) are extracted from the curves of voltage, current, and temperature in the process of charging and discharging lithium-ion batteries, which respond to the battery capacity degradation process. A few reasonable indicators are selected as the inputs of SOH prediction by the grey relation analysis method. The short-term SOH prediction is carried out by combining the Gaussian process regression (GPR) method with probability predictions. Then, considering that there is a certain mapping relationship between SOH and RUL, three IHIs and the present SOH value are utilized to predict RUL of lithium-ion batteries through the GPR model. The results show that the proposed method has high prediction accuracy.
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Ali, Zafar, Nengroo, Hussain, Park, and Kim. "Online Remaining Useful Life Prediction for Lithium-Ion Batteries Using Partial Discharge Data Features." Energies 12, no. 22 (November 15, 2019): 4366. http://dx.doi.org/10.3390/en12224366.
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Online accurate estimation of remaining useful life (RUL) of lithium-ion batteries is a necessary feature of any smart battery management system (BMS). In this paper, a novel partial discharge data (PDD)-based support vector machine (SVM) model is proposed for RUL prediction. The proposed algorithm extracts the critical features from the voltage and temperature of PDD to train the SVM models. The classification and regression attributes of SVM are utilized to classify and predict accurate RUL. The different ranges of PDD were analyzed to find the optimal range for training the SVM model. The SVM model trained with optimal PDD features classifies the RUL into six different classes for gross estimation, and the support vector regression is used to estimate the accurate value of the last class. The classification and predictive performance of SVM model trained using the full discharge data and PDD are compared for publicly available data. Results show that the SVM classification and regression model trained with PDD features can accurately predict the RUL with low storage pressure on BMS. The PDD-based SVM model can be utilized for online RUL estimation in electric vehicles.
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Ma, Jian, Hua Su, Wan-lin Zhao, and Bin Liu. "Predicting the Remaining Useful Life of an Aircraft Engine Using a Stacked Sparse Autoencoder with Multilayer Self-Learning." Complexity 2018 (July 30, 2018): 1–13. http://dx.doi.org/10.1155/2018/3813029.
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Because they are key components of aircraft, improving the safety, reliability and economy of engines is crucial. To ensure flight safety and reduce the cost of maintenance during aircraft engine operation, a prognostics and health management system that focuses on fault diagnosis, health assessment, and life prediction is introduced to solve the problems. Predicting the remaining useful life (RUL) is the most important information for making decisions about aircraft engine operation and maintenance, and it relies largely on the selection of performance degradation features. The choice of such features is highly significant, but there are some weaknesses in the current algorithm for RUL prediction, notably, the inability to obtain tendencies from the data. Especially with aircraft engines, extracting useful degradation features from multisensor data with complex correlations is a key technical problem that has hindered the implementation of degradation assessment. To solve these problems, deep learning has been proposed in recent years to exploit multiple layers of nonlinear information processing for unsupervised self-learning of features. This paper presents a deep learning approach to predict the RUL of an aircraft engine based on a stacked sparse autoencoder and logistic regression. The stacked sparse autoencoder is used to automatically extract performance degradation features from multiple sensors on the aircraft engine and to fuse multiple features through multilayer self-learning. Logistic regression is used to predict the remaining useful life. However, the hyperparameters of the deep learning, which significantly impact the feature extraction and prediction performance, are determined based on expert experience in most cases. The grid search method is introduced in this paper to optimize the hyperparameters of the proposed aircraft engine RUL prediction model. An application of this method of predicting the RUL of an aircraft engine with a benchmark dataset is employed to demonstrate the effectiveness of the proposed approach.
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Mao, Ling, Jie Xu, Jiajun Chen, Jinbin Zhao, Yuebao Wu, and Fengjun Yao. "A LSTM-STW and GS-LM Fusion Method for Lithium-Ion Battery RUL Prediction Based on EEMD." Energies 13, no. 9 (May 9, 2020): 2380. http://dx.doi.org/10.3390/en13092380.
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To address inaccurate prediction in remaining useful life (RUL) in current Lithium-ion batteries, this paper develops a Long Short-Term Memory Network, Sliding Time Window (LSTM-STW) and Gaussian or Sine function, Levenberg-Marquardt algorithm (GS-LM) fusion batteries RUL prediction method based on ensemble empirical mode decomposition (EEMD). Firstly, EEMD is used to decompose the original data into high-frequency and low-frequency components. Secondly, LSTM-STW and GS-LM are used to predict the high-frequency and low-frequency components, respectively. Finally, the LSTM-STW and GS-LM prediction results are effectively integrated in order to obtain the final prediction of the lithium-ion battery RUL results. This article takes the lithium-ion battery data published by NASA as input. The experimental results show that the method has higher accuracy, including the phenomenon of sudden capacity increase, and is less affected by the prediction starting point. The performance of the proposed method is better than other typical battery RUL prediction methods.
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Cao, Xiangang, Pengfei Li, and Song Ming. "Remaining Useful Life Prediction-Based Maintenance Decision Model for Stochastic Deterioration Equipment under Data-Driven." Sustainability 13, no. 15 (July 31, 2021): 8548. http://dx.doi.org/10.3390/su13158548.
Full textAbstract:
Currently, the Remaining Useful Life (RUL) prediction accuracy of stochastic deterioration equipment is low. Existing researches did not consider the impact of imperfect maintenance on equipment degradation and maintenance decisions. Therefore, this paper proposed a remaining useful life prediction-based maintenance decision model under data-driven to extend equipment life, promoting sustainable development. The stochastic degradation model was established based on the nonlinear Wiener process. A combination of real-time update and offline estimation estimated the degradation model’s parameters and deduced the equipment’s RUL distribution. Based on the RUL prediction results, we established a maintenance decision model with the lowest long-term cost rate as the goal. Case analysis shows that the model proposed in this paper can improve the accuracy of RUL prediction and realize equipment sustainability.
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Lu, Yi-Wei, Chia-Yu Hsu, and Kuang-Chieh Huang. "An Autoencoder Gated Recurrent Unit for Remaining Useful Life Prediction." Processes 8, no. 9 (September 15, 2020): 1155. http://dx.doi.org/10.3390/pr8091155.
Full textAbstract:
With the development of smart manufacturing, in order to detect abnormal conditions of the equipment, a large number of sensors have been used to record the variables associated with production equipment. This study focuses on the prediction of Remaining Useful Life (RUL). RUL prediction is part of predictive maintenance, which uses the development trend of the machine to predict when the machine will malfunction. High accuracy of RUL prediction not only reduces the consumption of manpower and materials, but also reduces the need for future maintenance. This study focuses on detecting faults as early as possible, before the machine needs to be replaced or repaired, to ensure the reliability of the system. It is difficult to extract meaningful features from sensor data directly. This study proposes a model based on an Autoencoder Gated Recurrent Unit (AE-GRU), in which the Autoencoder (AE) extracts the important features from the raw data and the Gated Recurrent Unit (GRU) selects the information from the sequences to forecast RUL. To evaluate the performance of the proposed AE-GRU model, an aircraft turbofan engine degradation simulation dataset provided by NASA was used and a comparison made of different recurrent neural networks. The results demonstrate that the AE-GRU is better than other recurrent neural networks, such as Long Short-Term Memory (LSTM) and GRU.
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Wang, Ye, Zhixiong Chen, Yang Zhang, Xin Li, and Zhixiong Li. "Remaining useful life prediction of rolling bearings based on the three-parameter Weibull distribution proportional hazards model." Insight - Non-Destructive Testing and Condition Monitoring 62, no. 12 (December 1, 2020): 710–18. http://dx.doi.org/10.1784/insi.2020.62.12.710.
Full textAbstract:
In order to accurately predict the remaining useful life (RUL) of rolling bearings, a novel method based on the threeparameter Weibull distribution proportional hazards model (WPHM) is proposed in this paper. In this new method, degradation features of the bearing vibration signals were calculated in the time, frequency and time-frequency domains and treated as the input covariates of the predictive WPHM. Essential knowledge of the bearing degradation dynamics was learnt from the input features to build an effective three-parameter WPHM for bearing RUL prediction. Experimental data acquired from the run-to-failure bearing tests of the intelligent maintenance system (IMS) was used to evaluate the proposed method. The analysis results demonstrate that the proposed model is able to produce accurate RUL prediction for the tested bearings and outperforms the popular two-parameter WPHM.
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Shi, Hongmei, Jinsong Yang, and Jin Si. "Centralized Maintenance Time Prediction Algorithm for Freight Train Wheels Based on Remaining Useful Life Prediction." Mathematical Problems in Engineering 2020 (March 11, 2020): 1–12. http://dx.doi.org/10.1155/2020/9256312.
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Many freight trains for special lines have in common the characteristics of a fixed group. Centralized Condition-Based Maintenance (CCBM) of key components, on the same freight train, can reduce maintenance costs and enhance transportation efficiency. To this end, an optimization algorithm based on the nonlinear Wiener process is proposed, for the prediction of the train wheels Remaining Useful Life (RUL) and the centralized maintenance timing. First, Hodrick–Prescott (HP) filtering algorithm is employed to process the raw monitoring data of wheel tread wear, extracting its trend components. Then, a nonlinear Wiener process model is constructed. Model parameters are calculated with a maximum likelihood estimation and the general deterioration parameters of wheel tread wear are obtained. Then, the updating algorithm for the drift coefficient is deduced using Bayesian formula. The online updating of the model is realized, based on individual wheel monitoring data, while a probability density function of individual wheel RUL is obtained. A prediction method of RUL for centralized maintenance is proposed, based on two set thresholds: “maintenance limit” and “the ratio of limit-arriving.” Meanwhile, a CCBM timing prediction algorithm is proposed, based on the expectation distribution of individual wheel RUL. Finally, the model is validated using a 500-day online monitoring data on a fixed group, consisting of 54 freight train cars. The validation result shows that the model can predict the wheels RUL of the train for CCBM. The proposed method can be used to predict the maintenance timing when there is a large number of components under the same working conditions and following the same path of degradation.
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Yan, Ji Hong, Chao Zhong Guo, Xing Wang, and De Bin Zhao. "A Data-Driven Neural Network Approach for Remaining Useful Life Prediction." Key Engineering Materials 450 (November 2010): 544–47. http://dx.doi.org/10.4028/www.scientific.net/kem.450.544.
Full textAbstract:
This paper proposed a neural network (NN) based remaining useful life (RUL) prediction approach. A new performance degradation index is designed using multi-feature fusion techniques to represent deterioration severities of facilities. Based on this indicator, back propagation neural networks are trained for RUL prediction, and average of the networks’ outputs is considered as the final RUL in order to overcome prediction errors caused by random initiations of NNs. Finally, an experiment is set up based on a Bently-RK4 rotor unbalance test bed to validate the neural network based life prediction models, experimental results illustrate the effectiveness of the methodology.
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Zhou, Shenghan, Xingxing Xu, Yiyong Xiao, Wenbing Chang, Silin Qian, and Xing Pan. "Remaining Useful Life Prediction with Similarity Fusion of Multi-Parameter and Multi-Sample Based on the Vibration Signals of Diesel Generator Gearbox." Entropy 21, no. 9 (September 3, 2019): 861. http://dx.doi.org/10.3390/e21090861.
Full textAbstract:
The prediction of electrical machines’ Remaining Useful Life (RUL) can facilitate making electrical machine maintenance policies, which is important for improving their security and extending their life span. This paper proposes an RUL prediction model with similarity fusion of multi-parameter and multi-sample. Firstly, based on the time domain and frequency domain extraction of vibration signals, the performance damage indicator system of a gearbox is established to select the optimal damage indicators for RUL prediction. Low-pass filtering based on approximate entropy variance (Aev) is introduced in this process because of its stability. Secondly, this paper constructs Dynamic Time Warping Distance (DTWD) as a similarity measurement function, which belongs to the nonlinear dynamic programming algorithm. It performed better than the traditional Euclidean distance. Thirdly, based on DTWD, similarity fusion of multi-parameter and multi-sample methods is proposed here to achieve RUL prediction. Next, the performance evaluation indicator Q is adopted to evaluate the RUL prediction accuracy of different methods. Finally, the proposed method is verified by experiments, and the Multivariable Support Vector Machine (MSVM) and Principal Component Analysis (PCA) are introduced for comparative studies. The results show that the Mean Absolute Percentage Error (MAPE) of the similarity fusion of multi-parameter and multi-sample methods proposed here is below 14%, which is lower than MSVM’s and PCA’s. Additionally, the RUL prediction based on the DTWD function in multi-sample similarity fusion exhibits the best accuracy.
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Mahdaoui, Rafik, Leila Hayet Mouss, Amar Haboussi, Ouahiba Chouhal, Hichem Haouassi, and Toufik Messoud Maarouk. "A Temporal Neuro-Fuzzy System for Estimating Remaining Useful Life in Preheater Cement Cyclones." International Journal of Reliability, Quality and Safety Engineering 26, no. 03 (May 7, 2019): 1950012. http://dx.doi.org/10.1142/s0218539319500128.
Full textAbstract:
Fault prognosis in industrial plants is a complex problem, and time is an important factor for the resolution of this problem. The main indicator for the task of fault prognosis is the estimate of remaining useful life (RUL), which essentially depends on the predicted time to failure. This paper introduces a temporal neuro-fuzzy system (TNFS) for performing the fault prognosis task and exactly estimating the RUL of preheater cyclones in a cement plant. The main component of the TNFS is a set of temporal fuzzy rules that have been chosen for their ability to explain the behavior of the entire system, the components’ degradation, and the RUL estimation. The benefit of introducing time in the structure of fuzzy rules is that a local memory of the TNFS is created to capture the dynamics of the prognostic task. More precisely, the paper emphasizes improving the performance of TNFSs for prediction. The RUL estimation process is broken down into four generic processes: building a predictive model, selecting the most critical parameters, training the TNFS, and predicting RUL through the generated temporal fuzzy rules. Finally, the performance of the proposed TNFS is evaluated using a real preheater cement cyclone dataset. The results show that our TNFS produces better results than classical neuro-fuzzy systems and neural networks.
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LEZANSKI, Pawel. "A data-driven predictive model of the grinding wheel wear using the neural network approach." Journal of Machine Engineering 4, no. 17 (December 12, 2017): 69–82. http://dx.doi.org/10.5604/01.3001.0010.7006.
Full textAbstract:
Advanced manufacturing depends on the timely acquisition, distribution, and utilization of information from machines and processes. These activities can improve accuracy and reliability in predicting resource needs and allocation, maintenance scheduling, and remaining service life of equipment. Thus, to model the state of tool wear and next to predict its remaining useful life (RUL) significantly increases the sustainability of manufacturing processes. there are many approaches, methods and theories applied to predictive model building. the proposed paper investigates an artificial neural network (ANN) model to predict the wear propagation process of grinding wheel and to estimate the RUL of the wheel when the extrapolated data reaches a predefined final failure value. The model building framework is based on data collected during external cylindrical plunge grinding. Firstly, usefulness of selected features of the measured process variables to be symptoms of grinding wheel state is experimentally verified. Next, issues related to development of an effective MLP model and its use in prediction of the grinding wheel RUL is discussed.
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Li, Lin, Alfredo Alan Flores Saldivar, Yun Bai, and Yun Li. "Battery Remaining Useful Life Prediction with Inheritance Particle Filtering." Energies 12, no. 14 (July 19, 2019): 2784. http://dx.doi.org/10.3390/en12142784.
Full textAbstract:
Accurately forecasting a battery’s remaining useful life (RUL) plays an important role in the prognostics and health management of rechargeable batteries. An effective forecast is reported using a particle filter (PF), but it currently suffers from particle degeneracy and impoverishment deficiencies in RUL evaluations. In this paper, an inheritance PF is developed to predict lithium-ion battery RUL for the first time. A battery degradation model is first mapped onto a PF problem using the genetic algorithm (GA) framework. Then, a Lamarckian inheritance operator is designed to improve the light-weight particles by heavy-weight ones and thus to tackle particle degeneracy. In addition, the inheritance mechanism retains certain existing information to tackle particle impoverishment. The performance of the inheritance PF is compared with an elitism GA-based PF. The former has fewer tuning parameters than the latter and is less sensitive to tuning parameters. Both PFs are applied to the prediction of lithium-ion battery RUL, which is validated using capacity degradation data from the NASA Ames Research Center. The experimental results show that the inheritance PF method offers improved RUL prediction and wider applications. Further improvement is obtained with one-step ahead prediction when the charging and discharging cycles move along.
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Ma, Tiancai, Jianmiao Xu, Ruitao Li, Naiyuan Yao, and Yanbo Yang. "Online Short-Term Remaining Useful Life Prediction of Fuel Cell Vehicles Based on Cloud System." Energies 14, no. 10 (May 13, 2021): 2806. http://dx.doi.org/10.3390/en14102806.
Full textAbstract:
The durability of automotive fuel cells is one of the main factors restricting their commercial application. Therefore, establishing a remaining useful life (RUL) prediction model and developing an online operational method to apply it to the RUL optimization of fuel cell vehicles is an urgent academic problem. In this work, a short-term RUL prediction model and an online operation scheme for fuel cell vehicles are proposed. Firstly, based on historical data of a fuel cell bus under multiple conditions, the daily mode of stack voltage under a 75 A operation condition was selected as a health indicator that could better reflect the health status of a fuel cell stack. Then, an adaptive locally weighted scatterplot smoothing (LOWESS) algorithm was developed to adjust the most appropriate step size to smooth the original data automatically. Furthermore, for better prediction accuracy and stronger adaptability, a short-term RUL prediction model consisting of the adaptive LOWESS and bi-directional long short-term memory was established. Finally, an online operation scheme of the RUL prediction model based on a cloud system gave the model a strong powerful practicability. After validation, this work demonstrated good application prospects in the prognostic and health management of automotive fuel cells.
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Zhao, Chengying, Xianzhen Huang, Yuxiong Li, and Muhammad Yousaf Iqbal. "A Double-Channel Hybrid Deep Neural Network Based on CNN and BiLSTM for Remaining Useful Life Prediction." Sensors 20, no. 24 (December 11, 2020): 7109. http://dx.doi.org/10.3390/s20247109.
Full textAbstract:
In recent years, prognostic and health management (PHM) has played an important role in industrial engineering. Efficient remaining useful life (RUL) prediction can ensure the development of maintenance strategies and reduce industrial losses. Recently, data-driven based deep learning RUL prediction methods have attracted more attention. The convolution neural network (CNN) is a kind of deep neural network widely used in RUL prediction. It shows great potential for application in RUL prediction. A CNN is used to extract the features of time-series data according to the spatial feature method. This way of processing features without considering the time dimension will affect the prediction accuracy of the model. On the contrary, the commonly used long short-term memory (LSTM) network considers the timing of the data. However, compared with CNN, it lacks spatial data extraction capabilities. This paper proposes a double-channel hybrid prediction model based on the CNN and a bidirectional LSTM network to avoid those drawbacks. The sliding time window is used for data preprocessing, and an improved piece-wise linear function is used for model validating. The prediction model is evaluated using the C-MAPSS dataset provided by NASA. The predicted results show the proposed prediction model to have a better prediction performance compared with other state-of-the-art models.
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Al Masry, Zeina, Patrick Schaible, Noureddine Zerhouni, and Christophe Varnier. "Remaining useful life prediction for ball bearings based on health indicators." MATEC Web of Conferences 261 (2019): 02003. http://dx.doi.org/10.1051/matecconf/201926102003.
Full textAbstract:
Uncertainty in remaining useful life (RUL) prediction is nowadays a scientific problem that occupies industrials. Many prognostic models have been developed to respond to this issue from probabilistic to non-probabilistic approaches. In this paper, we deal with a non- probabilistic model for RUL prediction. For this purpose, we propose a model, which is based on health indicators information, that allows to estimate the RUL of ball bearings. The method is applied to simulated data provided by the PRONOSTIA platform designed and realized at AS2M department of FEMTO- ST Institute.
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Wang, Zhuqing, Yangming Guo, and Cong Xu. "An HI Extraction Framework for Lithium-Ion Battery Prognostics Based on SAE-VMD." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 38, no. 4 (August 2020): 814–21. http://dx.doi.org/10.1051/jnwpu/20203840814.
Full textAbstract:
The signals of lithium-ion battery degradation are non-stationary and nonlinear. To adaptively extract the health indicator(HI) that can accurately represent the battery degradation characters and improve the prediction precision of battery remaining useful life (RUL), a stacked auto encoder-variational mode decomposition(SAE-VMD) based HI construction framework is proposed. Firstly, the stacked auto encoder(SAE) is used to reduce the noises of battery parameters and lower the data dimensionality and construct a syncretic HI that contains the battery degradation characters. Then the variational mode decomposition(VMD) is employed for effectively separating the syncretic HI into three modalities: the global attenuation, the local regeneration and the noises. The three modalities are selected as HIs to eliminate the HI noises and improve the RUL prediction precision. The RUL prediction results of lithium-ion battery indicate that the HI extracted by using the present method can obtain a better RUL prediction precision and verify the high quality of the extracted HI.
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Liu, Min, Xifan Yao, Jianming Zhang, Wocheng Chen, Xuan Jing, and Kesai Wang. "Multi-Sensor Data Fusion for Remaining Useful Life Prediction of Machining Tools by IABC-BPNN in Dry Milling Operations." Sensors 20, no. 17 (August 19, 2020): 4657. http://dx.doi.org/10.3390/s20174657.
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Inefficient remaining useful life (RUL) estimation may cause unpredictable failures and unscheduled maintenance of machining tools. Multi-sensor data fusion will improve the RUL prediction reliability by fusing more sensor information related to the machining process of tools. In this paper, a multi-sensor data fusion system for online RUL prediction of machining tools is proposed. The system integrates multi-sensor signal collection, signal preprocess by a complementary ensemble empirical mode decomposition, feature extraction in time domain, frequency domain and time-frequency domain by such methods as statistical analysis, power spectrum density analysis and Hilbert-Huang transform, feature selection by a Light Gradient Boosting Machine method, feature fusion by a tool wear prediction model based on back propagation neural network optimized by improved artificial bee colony (IABC-BPNN) algorithm, and the online RUL prediction model by a polynomial curve fitting method. An example is used to verify whether if the prediction performance of the proposed system is stable and reliable, and the results show that it is superior to its rivals.
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Zhao, Lin, Yipeng Wang, and Jianhua Cheng. "A Hybrid Method for Remaining Useful Life Estimation of Lithium-Ion Battery with Regeneration Phenomena." Applied Sciences 9, no. 9 (May 8, 2019): 1890. http://dx.doi.org/10.3390/app9091890.
Full textAbstract:
The lithium-ion battery has become the primary energy source of many electronic devices. Accurately forecasting the remaining useful life (RUL) of a battery plays an essential role in ensuring reliable operatioin of an electronic system. This paper investigates the lithium-ion battery RUL prediction problem with capacity regeneration phenomena. We aim to reduce the accumulation of the prediction error by integrating different capacity degradation models and thereby improve the prediction accuracy of the long-term RUL. To describe the degradation process more accurately, we decoupled the degradation process into two types: capacity regeneration and normal degradation. Then, we modelled two kinds of degradation processes separately. In the prediction phase, we predicted the battery state of health (SOH) by using the relevance vector machine (RVM) and the gray model (GM) alternately, updated the training dataset according to the prediction results, and then updated the RVM and GM. The RVM and GM correct each other’s prediction results constantly, which reduces the cumulative error of prediction and improves the prediction accuracy of the battery SOH. Experimental results with the National Aeronautics and Space Administration (NASA) battery dataset demonstrated that the proposed method can accurately establish the degradation model and achieve better performance for the RUL estimation as compared with the single RVM or GM methods.
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Wen, Juan, Hongli Gao, and Jiangquan Zhang. "Bearing Remaining Useful Life Prediction Based on a Nonlinear Wiener Process Model." Shock and Vibration 2018 (June 26, 2018): 1–13. http://dx.doi.org/10.1155/2018/4068431.
Full textAbstract:
Prognostic is an essential part of condition-based maintenance, which can be employed to enhance the reliability and availability and reduce the maintenance cost of mechanical systems. This paper develops an improved remaining useful life (RUL) prediction method for bearings based on a nonlinear Wiener process model. First, the service life of bearings is divided into two stages in terms of the working condition. Then a new prognostic model is constructed to reflect the relationship between time and bearing health status. Besides, a variety of factors that cause uncertainties toward the degradation path are considered and appropriately managed to obtain reliable RUL prediction results. The particle filtering is utilized to estimate the degradation state, qualify the uncertainties, and predict the RUL. The experimental studies show that the proposed method has a better performance in RUL prediction and uncertainty management than the exponential model and the linear model.
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Zhang, Nannan, Lifeng Wu, Zhonghua Wang, and Yong Guan. "Bearing Remaining Useful Life Prediction Based on Naive Bayes and Weibull Distributions." Entropy 20, no. 12 (December 8, 2018): 944. http://dx.doi.org/10.3390/e20120944.
Full textAbstract:
Bearing plays an important role in mechanical equipment, and its remaining useful life (RUL) prediction is an important research topic of mechanical equipment. To accurately predict the RUL of bearing, this paper proposes a data-driven RUL prediction method. First, the statistical method is used to extract the features of the signal, and the root mean square (RMS) is regarded as the main performance degradation index. Second, the correlation coefficient is used to select the statistical characteristics that have high correlation with the RMS. Then, In order to avoid the fluctuation of the statistical feature, the improved Weibull distributions (WD) algorithm is used to fit the fluctuation feature of bearing at different recession stages, which is used as input of Naive Bayes (NB) training stage. During the testing stage, the true fluctuation feature of the bearings are used as the input of NB. After the NB testing, five classes are obtained: health states and four states for bearing degradation. Finally, the exponential smoothing algorithm is used to smooth the five classes, and to predict the RUL of bearing. The experimental results show that the proposed method is effective for RUL prediction of bearing.
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Liu, Zhiliang, Ming J. Zuo, and Yong Qin. "Remaining useful life prediction of rolling element bearings based on health state assessment." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 230, no. 2 (June 3, 2015): 314–30. http://dx.doi.org/10.1177/0954406215590167.
Full textAbstract:
Instead of looking for an overall regression model for remaining useful life (RUL) prediction, this paper proposes a RUL prediction framework based on multiple health state assessment that divides the entire bearing life into several health states where a local regression model can be built individually. A hybrid approach consisting of both unsupervised learning and supervised learning is proposed to automatically estimate the real-time health state of a bearing in cases with no prior knowledge available. Support vector machine is the main technology adopted to implement health state assessment and RUL prediction. Experimental results on accelerated degradation tests of rolling element bearings demonstrate the effectiveness of the proposed framework.
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Susilo, Didik Djoko, Achmad Widodo, Toni Prahasto, and Muhammad Nizam. "State of Health Estimation of Lithium-Ion Batteries Based on Combination of Gaussian Distribution Data and Least Squares Support Vector Machines Regression." Materials Science Forum 929 (August 2018): 93–102. http://dx.doi.org/10.4028/www.scientific.net/msf.929.93.
Full textAbstract:
Lithium-ion batteries play a critical role in the reliability and safety of a system. Battery health monitoring and remaining useful life (RUL) prediction are needed to prevent catastrophic failure of the battery. The aim of this research is to develop a data-driven method to monitor the batteries state of health and predict their RUL by using the battery capacity degradation data. This paper also investigated the effect of prediction starting point to the RUL prediction error. One of the data-driven method drawbacks is the need of a large amount of data to obtain accurate prediction. This paper proposed a method to generate a series of degradation data that follow the Gaussian distribution based on limited battery capacity degradation data. The prognostic model was constructed from the new data using least square support vector machine (LSSVM) regression. The remaining useful life prediction was carried out by extrapolating the model until reach the end of life threshold. The method was applied to three differences lithium-ion batteries capacity data. The results showed that the proposed method has good performance. The method can predict the lithium-ion batteries RUL with a small error, and the optimal RUL starting point was found at the point where the battery has experienced the highest capacity recovery due to the self-recharge phenomenon.
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He, Qin, Yabing Zha, Quan Sun, Zhengqiang Pan, and Tianyu Liu. "Capacity Fast Prediction and Residual Useful Life Estimation of Valve Regulated Lead Acid Battery." Mathematical Problems in Engineering 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/7835049.
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The usable capacity of acid lead batteries is often used as the degradation feature for online RUL (residual useful life) estimation. In engineering applications, the “standard” fully discharging method for capacity measure is quite time-consuming and harmful for the high-capacity batteries. In this paper, a data-driven framework providing capacity fast prediction and RUL estimation for high-capacity VRLA (valve regulated lead acid) batteries is presented. These batteries are used as backup power sources on the ships. The relationship between fully discharging time and partially discharging voltage curve is established for usable capacity extrapolation. Based on the predicted capacity, the particle filtering approach is utilized to obtain battery RUL distribution. A case study is conducted with the experimental data of GFM-200 battery. Results confirm that our method not only reduces the prediction time greatly but also performs quite well in prediction accuracy of battery capacity and RUL.
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Wu, Chenchen, Hongchun Sun, Senmiao Lin, and Sheng Gao. "Remaining useful life prediction of bearings with different failure types based on multi-feature and deep convolution transfer learning." Eksploatacja i Niezawodnosc - Maintenance and Reliability 23, no. 4 (September 12, 2021): 684–94. http://dx.doi.org/10.17531/ein.2021.4.11.
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The accurate prediction of the remaining useful life (RUL) of rolling bearings is of immense importance in ensuring the safe and smooth operation of machinery and equipment. Although the prediction accuracy has been improved by a predictive model based on deep learning, it is still limited in engineering because lots of models use single-scale features to predict and assume that the degradation data of each bearing has a consistent distribution. In this paper, A deep convolutional migration network based on spatial pyramid pooling (SPP-CNNTL) is proposed to obtain higher prediction accuracy with self-extraction of multi-feature from the original vibrating signal. And to consider the differences of the data distribution in different failure types, transfer learning (TL) added with maximum mean difference (MMD) measurement function is used in the RUL prediction part. Finally, the data of IEEE PHM 2012 Challenge is used for verification, and the results show that the method in this paper has high prediction accuracy.
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Lin, Zhibin, Hongli Gao, Erqing Zhang, Weiqing Cao, and Kesi Li. "Diamond-Coated Mechanical Seal Remaining Useful Life Prediction Based on Convolution Neural Network." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 05 (August 28, 2019): 2051007. http://dx.doi.org/10.1142/s0218001420510076.
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Reliable remaining useful life (RUL) prediction of industrial equipment key components is of considerable importance in condition-based maintenance to avoid catastrophic failure, promote reliability and reduce cost during the production. Diamond-coated mechanical seal is one of the most critical wearing components in petroleum chemical, nuclear power and other process industries. Estimating the RUL is of critical importance. We consider the data-driven approaches for diamond-coated mechanical seal RUL estimation based on AE sensor data, since it is difficult to construct an explicit mathematical degradation model of seal. The challenges of this work are dealing with the noisy AE sensor data and modeling the degradation process with fluctuation. Faced with these challenges, we propose a pipeline method CDF-CNN to estimate the RUL for mechanical seal: WPD-KLD to raise the signal-to-noise ratio, novel CDF-based statistics to represent seal degradation process and CNN structure to estimate RUL. To acquire AE sensor data, several diamond-coated seals are tested from new to failure in three working conditions. Experimental results demonstrate that the proposed method can accurately predict the RUL of diamond-coated mechanical seal based on AE signals. The proposed prediction method can be generalized to other various mechanical assets.
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Peng, Weiwen, Zhi-Sheng Ye, and Nan Chen. "Joint Online RUL Prediction for Multivariate Deteriorating Systems." IEEE Transactions on Industrial Informatics 15, no. 5 (May 2019): 2870–78. http://dx.doi.org/10.1109/tii.2018.2869429.
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Yu, He, Zaike Tian, Hongru Li, Baohua Xu, and Guoqing An. "A Novel Deep Belief Network Model Constructed by Improved Conditional RBMs and its Application in RUL Prediction for Hydraulic Pumps." International Journal of Acoustics and Vibration 25, no. 3 (September 30, 2020): 373–82. http://dx.doi.org/10.20855/ijav.2020.25.31669.
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Residual Useful Life (RUL) prediction is a key step of Condition-Based Maintenance (CBM). Deep learning-based techniques have shown wonderful prospects on RUL prediction, although their performances depend on heavy structures and parameter tuning strategies of these deep-learning models. In this paper, we propose a novel Deep Belief Network (DBN) model constructed by improved conditional Restrict Boltzmann Machines (RBMs) and apply it in RUL prediction for hydraulic pumps. DBN is a deep probabilistic digraph neural network that consists of multiple layers of RBMs. Since RBM is an undirected graph model and there is no communication among the nodes of the same layer, the deep feature extraction capability of the original DBN model can hardly ensure the accuracy of modeling continuous data. To address this issue, the DBN model is improved by replacing RBM with the Improved Conditional RBM (ICRBM) that adds timing linkage factors and constraint variables among the nodes of the same layers on the basis of RBM. The proposed model is applied to RUL prediction of hydraulic pumps, and the results show that the prediction model proposed in this paper has higher prediction accuracy compared with traditional DBNs, BP networks, support vector machines and modified DBNs such as DEBN and GC-DBN.
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Wang, Ze Wen, Wei Li, Gong Bo Zhou, and Bo Wu. "Application of Random Average Method in Remain Useful Life Prediction of Rolling Bearing." Applied Mechanics and Materials 615 (August 2014): 335–40. http://dx.doi.org/10.4028/www.scientific.net/amm.615.335.
Full textAbstract:
Remain useful life (RUL) prediction technology which is significant in the condition based maintenance (CBM) is a hot research topic nowadays. Rolling bearing is a basic component widely used in the mechanical industry, and its reliability affects the operation of rotating machinery. On the basis of traditional RUL technology for rolling bearing, a method named random average method (RAM) is introduced into RUL prediction and the implementation of it is instructed in detail via the processing of vibration data in full life of rolling bearing. Compared to traditional method, the proposed method based on RAM is better in both accuracy and timeliness.