Relevant bibliographies by topics / Prediction of RUL

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Academic literature on the topic 'Prediction of RUL'

Author: Grafiati
Published: 28 June 2021
Last updated: 13 February 2022

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Journal articles on the topic "Prediction of RUL"

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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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Dissertations / Theses on the topic "Prediction of RUL"

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Martello, Rosanna. "Cloud storage and processing of automotive Lithium-ion batteries data for RUL prediction." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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Lithium-ion batteries are the ideal choice for electric and hybrid vehicles, but the high cost and the relatively short life represent an open issue for automotive industries. For this reason, the estimation of battery Remaining Useful Life (RUL) and the State of Health (SoH) are primary goals in the automotive sector. Cloud computing provides all the resources necessary to store, process and analyze all sensor data coming from connected vehicles in order to develop Predictive Maintenance tasks. This project describes the work done during my internship at FEV Italia s.r.l. The aims were designing an architecture for managing the data coming from a vehicle fleet and developing algorithms able to predict the SoH and the RUL of Lithium-ion batteries. The designed architecture is based on three Amazon Web Services: Amazon Elastic Compute Cloud, Amazon Simple Storage Service and Amazon Relational Database Service. After data processing and the feature extraction, the RUL and SoH estimations are performed by training two Neural Networks.
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Popara, Nikola. "Využití umělé inteligence k monitorování stavu obráběcího stroje." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2021. http://www.nusl.cz/ntk/nusl-444960.

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This thesis is focus on monitoring state of machine parts that are under the most stress. Type of artificial intelligence used in this work is recurrent neural network and its modifications. Chosen type of neural network was used because of the sequential character of used data. This thesis is solving three problems. In first problem algorithm is trying to determine state of mill tool wear using recurrent neural network. Used method for monitoring state is indirect. Second Problem was focused on detecting fault of a bearing and classifying it to specific category. In third problem RNN is used to predict RUL of monitored bearing.
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Mohammadisohrabi, Ali. "Design and implementation of a Recurrent Neural Network for Remaining Useful Life prediction." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

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A key idea underlying many Predictive Maintenance solutions is Remaining Useful Life (RUL) of machine parts, and it simply involves a prediction on the time remaining before a machine part is likely to require repair or replacement. Nowadays, with respect to fact that the systems are getting more complex, the innovative Machine Learning and Deep Learning algorithms can be deployed to study the more sophisticated correlations in complex systems. The exponential increase in both data accumulation and processing power make the Deep Learning algorithms more desirable that before. In this paper a Long Short-Term Memory (LSTM) which is a Recurrent Neural Network is designed to predict the Remaining Useful Life (RUL) of Turbofan Engines. The dataset is taken from NASA data repository. Finally, the performance obtained by RNN is compared to the best Machine Learning algorithm for the dataset.
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Jin, Wenjing. "Modeling of Machine Life Using Accelerated Prognostics and Health Management (APHM) and Enhanced Deep Learning Methodology." University of Cincinnati / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1479821186023747.

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Daher, Alaa. "Diagnostic et pronostic des défauts pour la maintenance préventive et prédictive. Application à une colonne de distillation." Thesis, Normandie, 2018. http://www.theses.fr/2018NORMR090/document.

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Le procédé de distillation est largement utilisé dans de nombreuses applications telles que la production pétrochimique, le traitement du gaz naturel, les raffineries de pétrole, etc. Généralement, la maintenance des réacteurs chimiques est très coûteuse et perturbe la production pendant de longues périodes. Tous ces facteurs démontrent réellement la nécessité de stratégies efficaces de diagnostic et de pronostic des défauts pour pouvoir réduire et éviter le plus grand nombre de ces problèmes catastrophiques. La première partie de nos travaux vise à proposer une méthode de diagnostic fiable pouvant être utilisée dans le régime permanent d’une procédure non linéaire. De plus, nous proposons une procédure modifiée de la méthode MFCM permettant de calculer la variation en pourcentage entre deux classes. L’utilisation de MFCM a pour objectif de réduire le temps de calcul et d’accroître les performances du classifieur. Les résultats de la méthode proposée confirment la capacité de classifier entre les différentes classes de défaillances considérées. Le calcul de la durée de vie du système est extrêmement important pour éviter les pannes catastrophiques. Notre deuxième objectif est de proposer une méthode fiable de pronostic permettant d’estimer le chemin de dégradation d’une colonne de distillation et de calculer le pourcentage de durée de vie de ce système. Le travail présente une approche basée sur le système d’inférence neuro-fuzzy adaptatif (ANFIS) combiné avec (FCM) pour prédire la trajectoire future et calculer le pourcentage de durée de vie du système. Les résultats obtenus démontrent la validité de la technique proposée pour atteindre les objectifs requis avec une précision de haut niveau. Pour améliorer les performances d’ANFIS, nous proposons la distribution de Parzen comme nouvelle fonction d’appartenance de l’algorithme ANFIS. Les résultats ont démontré l’importance de la technique proposée car elle s’est avérée efficace pour réduire le temps de calcul. En outre, la distribution de Parzen présentait la plus petite erreur quadratique moyenne (RMSE). La dernière partie de cette thèse se concentrait sur la proposition d’un nouvel algorithme pouvant être appliqué pour obtenir un système de surveillance en temps réel s’appuyant sur la prédiction de défauts ; cela signifie que cette méthode permet de prédire l’état futur du système, puis de diagnostiquer quelle est la source d’erreur probable. Elle permet d’évaluer la dégradation d’une colonne de distillation et de diagnostiquer par la suite les défauts ou accidents pouvant survenir à la suite de la dégradation estimée. Cette nouvelle approche combine les avantages d’ANFIS à ceux de RNA permettant d’atteindre un haut niveau de précision
The distillation process is largely used in many applications such a petrochemical production, natural gas processing, and petroleum refineries, etc. Usually, maintenance of the chemical reactors is very costly and it disrupts production for long periods of time. All these factors really demonstrate the fundamental need for effective fault diagnosis and prognostic strategies that they are able to reduce and avoid the greatest number of thes problems and disasters. The first part of our work aims to propose a reliable diagnostic method that can be used in the steady-state regime of a nonlinear procedure. Moreover, we propose a modified procedure of the fuzzy c-means clustering method (MFCM) where MFCM calculates the percentage variation between the two clustered classes. The purpose of using MFCM is to reduce the computing time and increase the performance of the classifier. The results of the proposed method confirm the ability to classify between normal mode and eight abnormal modes of faults. Our second goal aims to propose a prognosis reliable method used to estimate the degradation path of a distillation column and calculate the lifetime percentage of this system. The work presents an approach based on adaptive neuro-fuzzy inference system (ANFIS) combined with (FCM) to predict the future path and calculate the lifetime percentage of the system. The results obtained demonstrate the validity of the proposed technique to achieve the needed objectives with a high-level accuracy. To improve ANFIS performance we propose Parzen windows distribution as a new membership function for ANFIS algorithm. Results demonstrated the importance of the proposed technique since it proved to be highly successful in terms of reducing the time consumed. Additionally, Parzen windows had the smallest Root Mean Square Error (RMSE). The last part of this thesis was focusing on the proposing of new algorithm which can be applied to obtain real-time monitoring system which relies on the fault production module to reach the diagnosis module in contrast to the previous strategies ; this means this method predict the future state of the system then diagnosis what is the probable fault source. This proposed method has proven to be a reliable process that can evaluate the degradation of a distillation column and subsequently diagnose the possible faults or accidents that can emerge as a result of the estimated degradation. This new approach combines the benefits of ANFIS with the benefits of feedforward ANN. The results were demonstrated that the technique achieved with a high level of accuracy, the objective of prediction and diagnosis especially when applied to the data obtained from automated distillation process in the chemical industry
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Sanzani, Matteo. "La costruzione di un indicatore di salute per la manutenzione predittiva attraverso la programmazione genetica mono-obiettivo." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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La manutenzione predittiva, o Prognostic Health Management (PHM), è l’innovativa politica manutentiva basata monitoraggio continuo dello stato di salute dei componenti meccanici, grazie all’acquisizione dei dati tramite sensori applicati sui componenti stessi. Questi dati non sono facilmente analizzabili direttamente: è difatti necessaria un’attività di processing, volta ad estrarre delle caratteristiche significative e sintetiche del segnale, chiamate in letteratura features. Tipicamente, alla fase di estrazione delle features, segue una fase di selezione delle features e/o costruzione di un indicatore di salute, al fine di ridurre la dimensionalità dei dati ed aumentare la performance degli algoritmi futuri che riceveranno in input tali features per la diagnostica e/o prognostica. Questa tesi si focalizza proprio sulla costruzione di un indicatore di salute (HI) tramite programmazione genetica mono-obiettivo (algoritmo euristico basato sulla teoria della selezione naturale di Darwin, assai promettente rispetto alle tecniche tradizionali di selezione di features) a partire da un insieme di features estratte manualmente nel dominio del tempo. I segnali utilizzati provengono da un prototipo costruito all’interno del Laboratorio dell’Università di Bologna. In particolare, è stato analizzato il comportamento della cinghia, che rappresenta uno dei componenti chiave del prototipo, dalla messa in funzione in stato sano fino alla rottura (run-to-failure test). Il modello sarà costruito in ambiente MATLAB, attraverso lo sfruttamento del Genetic Programming Toolbox presente nel software stesso. Infine, per valutare il risultato ottenuto, l’HI costruito è stato dato in pasto ad un algoritmo di fitting e di previsione della vita utile residua (RUL), allo scopo di valutare l’errore medio di previsione rispetto a quanto realmente accaduto durante il test. I risultati ottenuti sembrano positivi, ma sono necessari sviluppi futuri per valutare la robustezza dell’indicatore.
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Sowan, Bilal I. "Enhancing Fuzzy Associative Rule Mining Approaches for Improving Prediction Accuracy. Integration of Fuzzy Clustering, Apriori and Multiple Support Approaches to Develop an Associative Classification Rule Base." Thesis, University of Bradford, 2011. http://hdl.handle.net/10454/5387.

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Building an accurate and reliable model for prediction for different application domains, is one of the most significant challenges in knowledge discovery and data mining. This thesis focuses on building and enhancing a generic predictive model for estimating a future value by extracting association rules (knowledge) from a quantitative database. This model is applied to several data sets obtained from different benchmark problems, and the results are evaluated through extensive experimental tests. The thesis presents an incremental development process for the prediction model with three stages. Firstly, a Knowledge Discovery (KD) model is proposed by integrating Fuzzy C-Means (FCM) with Apriori approach to extract Fuzzy Association Rules (FARs) from a database for building a Knowledge Base (KB) to predict a future value. The KD model has been tested with two road-traffic data sets. Secondly, the initial model has been further developed by including a diversification method in order to improve a reliable FARs to find out the best and representative rules. The resulting Diverse Fuzzy Rule Base (DFRB) maintains high quality and diverse FARs offering a more reliable and generic model. The model uses FCM to transform quantitative data into fuzzy ones, while a Multiple Support Apriori (MSapriori) algorithm is adapted to extract the FARs from fuzzy data. The correlation values for these FARs are calculated, and an efficient orientation for filtering FARs is performed as a post-processing method. The FARs diversity is maintained through the clustering of FARs, based on the concept of the sharing function technique used in multi-objectives optimization. The best and the most diverse FARs are obtained as the DFRB to utilise within the Fuzzy Inference System (FIS) for prediction. The third stage of development proposes a hybrid prediction model called Fuzzy Associative Classification Rule Mining (FACRM) model. This model integrates the ii improved Gustafson-Kessel (G-K) algorithm, the proposed Fuzzy Associative Classification Rules (FACR) algorithm and the proposed diversification method. The improved G-K algorithm transforms quantitative data into fuzzy data, while the FACR generate significant rules (Fuzzy Classification Association Rules (FCARs)) by employing the improved multiple support threshold, associative classification and vertical scanning format approaches. These FCARs are then filtered by calculating the correlation value and the distance between them. The advantage of the proposed FACRM model is to build a generalized prediction model, able to deal with different application domains. The validation of the FACRM model is conducted using different benchmark data sets from the University of California, Irvine (UCI) of machine learning and KEEL (Knowledge Extraction based on Evolutionary Learning) repositories, and the results of the proposed FACRM are also compared with other existing prediction models. The experimental results show that the error rate and generalization performance of the proposed model is better in the majority of data sets with respect to the commonly used models. A new method for feature selection entitled Weighting Feature Selection (WFS) is also proposed. The WFS method aims to improve the performance of FACRM model. The prediction performance is improved by minimizing the prediction error and reducing the number of generated rules. The prediction results of FACRM by employing WFS have been compared with that of FACRM and Stepwise Regression (SR) models for different data sets. The performance analysis and comparative study show that the proposed prediction model provides an effective approach that can be used within a decision support system.
Applied Science University (ASU) of Jordan
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Sowan, Bilal Ibrahim. "Enhancing fuzzy associative rule mining approaches for improving prediction accuracy : integration of fuzzy clustering, apriori and multiple support approaches to develop an associative classification rule base." Thesis, University of Bradford, 2011. http://hdl.handle.net/10454/5387.

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Building an accurate and reliable model for prediction for different application domains, is one of the most significant challenges in knowledge discovery and data mining. This thesis focuses on building and enhancing a generic predictive model for estimating a future value by extracting association rules (knowledge) from a quantitative database. This model is applied to several data sets obtained from different benchmark problems, and the results are evaluated through extensive experimental tests. The thesis presents an incremental development process for the prediction model with three stages. Firstly, a Knowledge Discovery (KD) model is proposed by integrating Fuzzy C-Means (FCM) with Apriori approach to extract Fuzzy Association Rules (FARs) from a database for building a Knowledge Base (KB) to predict a future value. The KD model has been tested with two road-traffic data sets. Secondly, the initial model has been further developed by including a diversification method in order to improve a reliable FARs to find out the best and representative rules. The resulting Diverse Fuzzy Rule Base (DFRB) maintains high quality and diverse FARs offering a more reliable and generic model. The model uses FCM to transform quantitative data into fuzzy ones, while a Multiple Support Apriori (MSapriori) algorithm is adapted to extract the FARs from fuzzy data. The correlation values for these FARs are calculated, and an efficient orientation for filtering FARs is performed as a post-processing method. The FARs diversity is maintained through the clustering of FARs, based on the concept of the sharing function technique used in multi-objectives optimization. The best and the most diverse FARs are obtained as the DFRB to utilise within the Fuzzy Inference System (FIS) for prediction. The third stage of development proposes a hybrid prediction model called Fuzzy Associative Classification Rule Mining (FACRM) model. This model integrates the ii improved Gustafson-Kessel (G-K) algorithm, the proposed Fuzzy Associative Classification Rules (FACR) algorithm and the proposed diversification method. The improved G-K algorithm transforms quantitative data into fuzzy data, while the FACR generate significant rules (Fuzzy Classification Association Rules (FCARs)) by employing the improved multiple support threshold, associative classification and vertical scanning format approaches. These FCARs are then filtered by calculating the correlation value and the distance between them. The advantage of the proposed FACRM model is to build a generalized prediction model, able to deal with different application domains. The validation of the FACRM model is conducted using different benchmark data sets from the University of California, Irvine (UCI) of machine learning and KEEL (Knowledge Extraction based on Evolutionary Learning) repositories, and the results of the proposed FACRM are also compared with other existing prediction models. The experimental results show that the error rate and generalization performance of the proposed model is better in the majority of data sets with respect to the commonly used models. A new method for feature selection entitled Weighting Feature Selection (WFS) is also proposed. The WFS method aims to improve the performance of FACRM model. The prediction performance is improved by minimizing the prediction error and reducing the number of generated rules. The prediction results of FACRM by employing WFS have been compared with that of FACRM and Stepwise Regression (SR) models for different data sets. The performance analysis and comparative study show that the proposed prediction model provides an effective approach that can be used within a decision support system.
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Lowy, Elliott. "The evolution of the golden rule /." Thesis, Connect to this title online; UW restricted, 1997. http://hdl.handle.net/1773/9017.

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Balla, Chaitanya Kumar. "Prediction of Remaining Service Life of Pavements." University of Toledo / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1279316853.

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Books on the topic "Prediction of RUL"

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Moṅʻ, Moṅʻ. Mranʻ māʹ rui r̋ā ʼāyu canʻ b̋edaṅʻ paññā. Ranʻ kunʻ: Tuiṅʻ L̋aṅʻ C̋ā pe, 2001.

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Gavrilov, O. A. Strategii͡a︡ pravotvorchestva i sot͡s︡ialʹnoe prognozirovanie. Moskva: In-t gosudarstva i prava Rossiĭskoĭ akademii nauk, 1993.

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V, Kehiaian H., Renon H, and International Symposium on Critical Evaluation and Prediction of Phase Equilibria in Multicomponent Systems (2nd : 1985 : Paris, France), eds. Measurement, evaluation, and prediction of phase equilibria: A collection of selected papers from the Second International IUPAC Workshop on Vapor-Liquid Equilibria in 1-Alkanol +n-Alkane Mixtures, Paris, France, 5-7 September 1985 and the Second International Symposium on Critical Evaluation and Prediction of Phase Equilibria in Multicomponent Systems, Paris France, 11-13 September 1985. Amsterdam: Elsevier, 1986.

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Nōrin Suisan Gijutsu Kaigi. Jimukyoku. Kankyō hendō ni tomonau kaiyō seibutsu daihassei no yosoku, seigyo gijutsu no kaihatsu: Kurage-rui no daihassei yosoku, seigyo gijutsu no kaihatsu = Study for the prediction and control of the population outbreak of the marine life in relation to environmental change : studies of prediction and control of jellyfish outbreaks (STOPJELLY). Tōkyō-to Chiyoda-ku: Nōrin Suisanshō Nōrin Suisan Gijutsu Kaigi Jimukyoku, 2014.

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Birch, Jonathan. The Rule under Attack. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198733058.003.0003.

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HRG has been criticized for being an ‘empty statement’ or tautology, for failing to yield predictions, and for failing to yield causal explanations of change. There is some justification for these charges, yet they do not undermine the value of HRG as an organizing framework. In response to the ‘tautology’ complaint, we should admit that HRG is tautology-like, in that it avoids detailed dynamical assumptions. But this is an advantage in an organizing framework, because it ensures its compatibility with a wide range of more detailed models. In response to the ‘prediction’ complaint, we should concede that HRG is not very useful for prediction, but the role of an organizing framework is not predictive. In response to the ‘causal explanation’ complaint, this chapter argues that HRG, by organizing our thinking about ultimate causes, generates understanding of those causes. It also compares favourably to other possible organizing frameworks.
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United States. Army Aviation Research and Technology Activity. and Langley Research Center, eds. A comparison of fatigue life prediction methodologies for rotor craft. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1990.

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Lee, Christoph I. Rule Out Subarachnoid Hemorrhage for Headache. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780190223700.003.0003.

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This chapter, found in the headache section of the book, provides a succinct synopsis of a key study examining the use of computed tomography (CT) to rule out a head bleed or subarachnoid hemorrhage among patients with acute headaches. This summary outlines the study methodology and design, major results, limitations and criticisms, related studies and additional information, and clinical implications. Researchers reported that the criteria had high sensitivity and high negative predictive value for identifying subarachnoid hemorrhage among patients presenting to the emergency department with acute nontraumatic headache that reached maximal intensity within 1 hour and with normal neurologic examinations. In addition to outlining the most salient features of the study, a clinical vignette and imaging example are included in order to provide relevant clinical context.
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Die Vermessung der Utopie: Mythen des Kapitalismus und die kommende Gesellschaft, Raul Zelik im Gespräch mit Elmar Altvater. Blumenbar, 2009.

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Hough, Catherine L. Chronic critical illness. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199600830.003.0377.

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Chronic critical illness (CCI) is common and describes a state of prolonged critical illness, in which patients have persisting organ failures requiring treatment in an intensive care setting. There are many different definitions of CCI, with most including prolonged (> 96 hours) mechanical ventilation. Advanced age, higher severity of illness, and poor functional status prior to critical illness are all important risk factors, but prediction of CCI is imperfect. Although requirement for mechanical ventilation is the hallmark, CCI encompasses much more than the respiratory system, with effects on metabolism, skin, brain, and neuromuscular function. During CCI, patients have a high burden of symptoms and impaired capacity to communicate their needs. Mortality and quality of life are generally poor, but highly variable, with 1-year mortality over 50% and most survivors suffering permanent cognitive impairment and functional dependence. Patients at highest and lowest risk for mortality can be identified using a simple prediction rule. Caring for the chronically critically ill is a substantial burden both to patients’ families and to the health care system as a whole. Further research is needed in order to improve care and outcomes for CCI patients and their families.
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van der Meer, Tom. Dissecting the Causal Chain from Quality of Government to Political Support. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198793717.003.0008.

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This chapter investigates to what extent cross-national differences in political support can be explained by the quality of government. The quality of government perspective implies that the executive ought to be bound by its own rules: impartiality and rule of law. The chapter formulates and tests hypotheses about the effects of governmental impartiality, rule of law, bureaucratic professionalism, and corruption on citizens’ political support using data from the ESS 2012. Of these indicators, it is the impartiality of policy implementation by the national bureaucracy that stands out as a consistently significant, robust, and strong predictor of political support.
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Book chapters on the topic "Prediction of RUL"

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Yan, Dong, and Xiukun Wei. "RUL Prediction for Bearings Based on Fault Diagnosis." In Lecture Notes in Electrical Engineering, 1013–20. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7986-3_102.

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Wu, Qianhui, Yu Feng, and Biqing Huang. "RUL Prediction of Bearings Based on Mixture of Gaussians Bayesian Belief Network and Support Vector Data Description." In Theory, Methodology, Tools and Applications for Modeling and Simulation of Complex Systems, 118–30. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2666-9_13.

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Wu, Qianhui, Yu Feng, and Biqing Huang. "RUL Prediction of Bearings Based on Mixture of Gaussians Bayesian Belief Network and Support Vector Data Description." In Challenges and Opportunity with Big Data, 139–51. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61994-1_14.

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Fürnkranz, Johannes. "Prediction Rule." In Encyclopedia of Systems Biology, 1733. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_837.

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Stemberger, Joseph P. "Rule ordering in Child phonology." In Principles and Prediction, 305. Amsterdam: John Benjamins Publishing Company, 1993. http://dx.doi.org/10.1075/cilt.98.25ste.

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Iverson, Gregory K. "Lexical versus postlexical rule application in Catalan." In Principles and Prediction, 339. Amsterdam: John Benjamins Publishing Company, 1993. http://dx.doi.org/10.1075/cilt.98.27ive.

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Kuhn, Max, and Kjell Johnson. "Classification Trees and Rule-Based Models." In Applied Predictive Modeling, 369–413. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6849-3_14.

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Kuhn, Max, and Kjell Johnson. "Regression Trees and Rule-Based Models." In Applied Predictive Modeling, 173–220. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6849-3_8.

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Tsukimoto, Hiroshi. "Rule Extraction from Prediction Models." In Methodologies for Knowledge Discovery and Data Mining, 34–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48912-6_6.

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Wanhill, Russell, Simon Barter, and Loris Molent. "Cubic Rule Life Prediction Examples." In SpringerBriefs in Applied Sciences and Technology, 67–70. Dordrecht: Springer Netherlands, 2019. http://dx.doi.org/10.1007/978-94-024-1675-6_8.

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Conference papers on the topic "Prediction of RUL"

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Hu, Chao, Byeng D. Youn, and Taejin Kim. "Semi-Supervised Learning With Co-Training for Data-Driven Prognostics." In ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-48302.

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Traditional data-driven prognostics often requires a large amount of failure data for the offline training in order to achieve good accuracy for the online prediction. However, in many engineered systems, failure data are fairly expensive and time-consuming to obtain while suspension data are readily available. In such cases, it becomes essentially critical to utilize suspension data, which may carry rich information regarding the degradation trend and help achieve more accurate remaining useful life (RUL) prediction. To this end, this paper proposes a co-training-based data-driven prognostic algorithm, denoted by Coprog, which uses two individual data-driven algorithms with each predicting RULs of suspension units for the other. The confidence of an individual data-driven algorithm in predicting the RUL of a suspension unit is quantified by the extent to which the inclusion of that unit in the training data set reduces the sum square error (SSE) in RUL prediction on the failure units. After a suspension unit is chosen and its RUL is predicted by an individual algorithm, it becomes a virtual failure unit that is added to the training data set. Results obtained from two case studies suggest that Coprog gives more accurate RUL predictions compared to any individual algorithm without the consideration of suspension data and that Coprog can effectively exploit suspension data to improve the accuracy in data-driven prognostics.
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Zhang, Yuxuan, Yuanxiang Li, Lei Jia, Xian Wei, and Yi Lu Murphey. "Sequential Information Bottleneck Network for RUL Prediction." In 2019 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2019. http://dx.doi.org/10.1109/ssci44817.2019.9002732.

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Galar, Diego, Uday Kumar, and Yuan Fuqing. "RUL prediction using moving trajectories between SVM hyper planes." In 2012 Annual Reliability and Maintainability Symposium (RAMS). IEEE, 2012. http://dx.doi.org/10.1109/rams.2012.6175481.

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Tang, Ting, Hui-Mei Yuan, and Jun Zhu. "RUL prediction of lithium batteries based on DLUKF algorithm." In 2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA). IEEE, 2020. http://dx.doi.org/10.1109/iciea48937.2020.9248133.

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Gao, Zehai, Cunbao Ma, and Yige Luo. "RUL prediction for IMA based on deep regression method." In 2017 IEEE 10th International Workshop on Computational Intelligence and Applications (IWCIA). IEEE, 2017. http://dx.doi.org/10.1109/iwcia.2017.8203556.

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Yan, Dong, Xiukun Wei, and Guorui Zhai. "RUL prediction for railway vehicle bearings based on fault diagnosis." In 2017 29th Chinese Control And Decision Conference (CCDC). IEEE, 2017. http://dx.doi.org/10.1109/ccdc.2017.7978862.

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Jia, Chao, and Hanwen Zhang. "RUL Prediction: Reducing Statistical Model Uncertainty Via Bayesian Model Aggregation." In 2019 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS). IEEE, 2019. http://dx.doi.org/10.1109/safeprocess45799.2019.9213433.

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Li, Huaxin, and Yanxue Wang. "A Sparse Coding Approach to RUL Prediction in Rolling Bearing." In 2017 International Conference on Sensing, Diagnostics, Prognostics and Control (SDPC). IEEE, 2017. http://dx.doi.org/10.1109/sdpc.2017.41.

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Jiang, Yuanyuan, Wenwen Zeng, Li Chen, and Yuanfang Xin. "Lithium-Ion Battery RUL Indirect Prediction Based on GAAA-ELM." In 2018 International Conference on Sensing,Diagnostics, Prognostics, and Control (SDPC). IEEE, 2018. http://dx.doi.org/10.1109/sdpc.2018.8664829.

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Khelif, Racha, Simon Malinowski, Brigitte Chebel-Morello, and Noureddine Zerhouni. "RUL prediction based on a new similarity-instance based approach." In 2014 IEEE 23rd International Symposium on Industrial Electronics (ISIE). IEEE, 2014. http://dx.doi.org/10.1109/isie.2014.6865006.

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Reports on the topic "Prediction of RUL"

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Mueller, Ulrich, and Mark Watson. Measuring Uncertainty about Long-Run Prediction. Cambridge, MA: National Bureau of Economic Research, March 2013. http://dx.doi.org/10.3386/w18870.

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Campshure, David A., and Eugene H. Drucker. Predicting First-Run Gunnery Performance on Tank Table VIII. Fort Belvoir, VA: Defense Technical Information Center, May 1990. http://dx.doi.org/10.21236/ada228201.

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Blanchflower, David, and Alex Bryson. The Sahm Rule and Predicting the Great Recession Across OECD Countries. Cambridge, MA: National Bureau of Economic Research, September 2021. http://dx.doi.org/10.3386/w29300.

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Townsend, Richard L., P. Westhagen, D. Yasuda, and J. R. Skalski. Evaluation of the 1994 Predictions of the Run-Timing of Wild Migrant Yearling Chinook in the Snake River Basin. Office of Scientific and Technical Information (OSTI), February 1995. http://dx.doi.org/10.2172/239306.

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Townsend, Richard L., Peter Westhagen, and Dean Yasuda. Evaluation of the 1995 Predictions of the Run-Timing of Wild Migrant Yearling Chinook in the Snake River Basin Using Program RealTime. Office of Scientific and Technical Information (OSTI), September 1996. http://dx.doi.org/10.2172/418436.

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Beer, W. Nicholas, Susannah Iltis, and James J. Anderson. Evaluation of the 2008 Predictions of Run-Timing and Survival of Wild Migrant Yearling Chinook and Steelhead on the Columbia and Snake Rivers. Office of Scientific and Technical Information (OSTI), January 2009. http://dx.doi.org/10.2172/947611.

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Townsend, Richard L., Dean Yasuda, and J. R. Skalski. Evaluation of the 1996 Predictions of the Run-Timing of Wild Migrant Spring/Summer Yearling Chinook in the Snake River Basin Using Program RealTime. Office of Scientific and Technical Information (OSTI), March 1997. http://dx.doi.org/10.2172/650231.

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Nishimura, Masatsugu, Yoshitaka Tezuka, Enrico Picotti, Mattia Bruschetta, Francesco Ambrogi, and Toru Yoshii. Study of Rider Model for Motorcycle Racing Simulation. SAE International, January 2020. http://dx.doi.org/10.4271/2019-32-0572.

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Various rider models have been proposed that provide control inputs for the simulation of motorcycle dynamics. However, those models are mostly used to simulate production motorcycles, so they assume that all motions are in the linear region such as those in a constant radius turn. As such, their performance is insufficient for simulating racing motorcycles that experience quick acceleration and braking. Therefore, this study proposes a new rider model for racing simulation that incorporates Nonlinear Model Predictive Control. In developing this model, it was built on the premise that it can cope with running conditions that lose contact with the front wheels or rear wheels so-called "endo" and "wheelie", which often occur during running with large acceleration or deceleration assuming a race. For the control inputs to the vehicle, we incorporated the lateral shift of the rider's center of gravity in addition to the normally used inputs such as the steering angle, throttle position, and braking force. We compared the performance of the new model with that of the conventional model under constant radius cornering and straight braking, as well as complex braking and acceleration in a single (hairpin) corner that represented a racing run. The results showed that the new rider model outperformed the conventional model, especially in the wider range of running speed usable for a simulation. In addition, we compared the simulation results for complex braking and acceleration in a single hairpin corner produced by the new model with data from an actual race and verified that the new model was able to accurately simulate the run of actual MotoGP riders.
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Farhi, Edward, and Hartmut Neven. Classification with Quantum Neural Networks on Near Term Processors. Web of Open Science, December 2020. http://dx.doi.org/10.37686/qrl.v1i2.80.

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We introduce a quantum neural network, QNN, that can represent labeled data, classical or quantum, and be trained by supervised learning. The quantum circuit consists of a sequence of parameter dependent unitary transformations which acts on an input quantum state. For binary classification a single Pauli operator is measured on a designated readout qubit. The measured output is the quantum neural network’s predictor of the binary label of the input state. We show through classical simulation that parameters can be found that allow the QNN to learn to correctly distinguish the two data sets. We then discuss presenting the data as quantum superpositions of computational basis states corresponding to different label values. Here we show through simulation that learning is possible. We consider using our QNN to learn the label of a general quantum state. By example we show that this can be done. Our work is exploratory and relies on the classical simulation of small quantum systems. The QNN proposed here was designed with near-term quantum processors in mind. Therefore it will be possible to run this QNN on a near term gate model quantum computer where its power can be explored beyond what can be explored with simulation.
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Beer, W. Nicholas, Joshua A. Hayes, and Pamela Shaw. Evaluation of the 1998 Predictions of the Run-Timing of Wild Migrant Yearling Chinook and Water Quality at Multiple Locations on the Snake and Columbia Rivers using CRiSP/RealTime, 1998 Technical Report. Office of Scientific and Technical Information (OSTI), July 1999. http://dx.doi.org/10.2172/14088.

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