Academic literature on the topic 'Prognostics prediction model'
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Journal articles on the topic "Prognostics prediction model"
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Long, Bing, Xiangnan Li, Xiaoyu Gao, and Zhen Liu. "Prognostics Comparison of Lithium-Ion Battery Based on the Shallow and Deep Neural Networks Model." Energies 12, no. 17 (August 25, 2019): 3271. http://dx.doi.org/10.3390/en12173271.
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Prognostics of the remaining useful life (RUL) of lithium-ion batteries is a crucial role in the battery management systems (BMS). An artificial neural network (ANN) does not require much knowledge from the lithium-ion battery systems, thus it is a prospective data-driven prognostic method of lithium-ion batteries. Though the ANN has been applied in prognostics of lithium-ion batteries in some references, no one has compared the prognostics of the lithium-ion batteries based on different ANN. The ANN generally can be classified to two categories: the shallow ANN, such as the back propagation (BP) ANN and the nonlinear autoregressive (NAR) ANN, and the deep ANN, such as the long short-term memory (LSTM) NN. An improved LSTM NN is proposed in order to achieve higher prediction accuracy and make the construction of the model simpler. According to the lithium-ion data from the NASA Ames, the prognostics comparison of lithium-ion battery based on the BP ANN, the NAR ANN, and the LSTM ANN was studied in detail. The experimental results show: (1) The improved LSTM ANN has the best prognostic accuracy and is more suitable for the prediction of the RUL of lithium-ion batteries compared to the BP ANN and the NAR ANN; (2) the NAR ANN has better prognostic accuracy compared to the BP ANN.
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Li, Xiaochuan, Xiaoyu Yang, Yingjie Yang, Ian Bennett, and David Mba. "An intelligent diagnostic and prognostic framework for large-scale rotating machinery in the presence of scarce failure data." Structural Health Monitoring 19, no. 5 (October 29, 2019): 1375–90. http://dx.doi.org/10.1177/1475921719884019.
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In this work, a novel diagnostic and prognostic framework is proposed to detect faults and predict remaining service life of large-scale rotating machinery in the presence of scarce failure data. In the proposed framework, a canonical variate residuals–based diagnostic method is developed to facilitate remaining service life prediction by continuously implementing detection of the prediction start time. A novel two-step prognostic feature exploring approach that involves fault identification, feature extraction, feature selection and multi-feature fusion is put forward. Most existing prognostic methods lack a fault-identification module to automatically identify the fault root-cause variables required in the subsequent prognostic analysis and decision-making process. The proposed prognostic feature exploring method overcomes this challenge by introducing a canonical variate residuals–based fault-identification method. With this method, the most representative degradation features are extracted from only the fault root-cause variables, thereby facilitating machinery prognostics by ensuring accurate estimates. Its effectiveness is demonstrated for slow involving faults in two case studies of an operational industrial centrifugal pump. Moreover, an enhanced grey model approach is developed for remaining useful life prediction. In particular, the empirical Bayesian algorithm is employed to improve the traditional grey forecasting model in terms of quantifying the uncertainty of remaining service life in a probabilistic form and improving its prediction accuracy. To demonstrate the superiority of empirical Bayesian–grey model, existing prognostic methods such as grey model, particle filter–grey model and empirical Bayesian–exponential regression are also utilized to realize machinery remaining service life prediction, and the results are compared with that of the proposed method. The achieved predictive accuracy shows that the proposed approach outperforms its counterparts and is highly applicable in fault prognostics of industrial rotating machinery. The use of in-service data in a practical scenario shows that the proposed prognostic approach is a promising tool for online health monitoring.
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Tong, Guoqiang, Xinbo Qian, and Yilai Liu. "Prognostics and Predictive Maintenance Optimization Based on Combination BP-RBF-GRNN Neural Network Model and Proportional Hazard Model." Journal of Sensors 2022 (April 29, 2022): 1–17. http://dx.doi.org/10.1155/2022/8655669.
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Owning to the advantage of keeping the operating environment safe, high reliability, and low production cost, predictive maintenance has been widely used in industry and academia. Predictive maintenance based on degeneration state mainly studies the degeneration prediction. However, on account of the error of the sensor and human, condition monitoring data may not directly reflect the true degeneration. The degeneration model with dynamic explanatory covariates which is named as proportional hazard model is proposed to deal with the semi-observed monitoring condition. And the degeneration prediction mainly adopts a single prediction model, which leads to low prediction accuracy. A combination forecasting model can effectively solve the above problem. Compared to the traditional prediction method, the neural network model can use the “black box” characteristic to indirectly construct the degeneration model without complex mathematical derivation. Therefore, we propose a combination BP-RBF-GRNN neural network model which is applied to improve the degeneration prediction with dynamic covariate. Based on the above two aspects, a predictive maintenance optimization framework based on the proportional hazard model and BP-RBF-GRNN neural network model is proposed to improve maintenance efficiency and reduce maintenance costs. The simulation results of thrust ball bearing show that the proposed method can effectively improve the degeneration prediction accuracy and reduce the maintenance cost rate to a certain extent.
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Won, Dong-Yeon, Hyun Su Sim, and Yong Soo Kim. "Prediction of Remaining Useful Lifetime of Membrane Using Machine Learning." Science of Advanced Materials 12, no. 10 (October 1, 2020): 1485–91. http://dx.doi.org/10.1166/sam.2020.3788.
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We present a novel analytical procedure estimating the remaining useful life (RUL) of complex systems or facilities based on degradation data obtained over time; we consider the maintenance characteristics of units that are incompletely repaired. We develop an extended prognostic model that accurately predicts the RUL; we use machine-learning featuring smoothing, logging, variable transformation and clustering to this end. The performance of a general model was more predictable than that of an extended model. A linear regression (LR) method was superior in terms of root mean square error prediction and an artificial neural network (ANN) was superior in terms of prognostics and health management (PHM) scoring. The procedure is both practical and efficient, and can be deployed in various industries, yielding low-cost prognostics even in low-expertise domains. The procedure can be applied to high-risk industries, aiding management decision-making in terms of the establishment of optimal, preventative maintenance policies.
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Wang, Yiwei, Christian Gogu, Nicolas Binaud, Christian Bes, Raphael T. Haftka, and Nam-Ho Kim. "Predictive airframe maintenance strategies using model-based prognostics." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 232, no. 6 (March 1, 2018): 690–709. http://dx.doi.org/10.1177/1748006x18757084.
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Aircraft panel maintenance is typically based on scheduled inspections during which the panel damage size is compared to a repair threshold value, set to ensure a desirable reliability for the entire fleet. This policy is very conservative since it does not consider that damage size evolution can be very different on different panels, due to material variability and other factors. With the progress of sensor technology, data acquisition and storage techniques, and data processing algorithms, structural health monitoring systems are increasingly being considered by the aviation industry. Aiming at reducing the conservativeness of the current maintenance approaches, and, thus, at reducing the maintenance cost, we employ a model-based prognostics method developed in a previous work to predict the future damage growth of each aircraft panel. This allows deciding whether a given panel should be repaired considering the prediction of the future evolution of its damage, rather than its current health state. Two predictive maintenance strategies based on the developed prognostic model are proposed in this work and applied to fatigue damage propagation in fuselage panels. The parameters of the damage growth model are assumed to be unknown and the information on damage evolution is provided by noisy structural health monitoring measurements. We propose a numerical case study where the maintenance process of an entire fleet of aircraft is simulated, considering the variability of damage model parameters among the panel population as well as the uncertainty of pressure differential during the damage propagation process. The proposed predictive maintenance strategies are compared to other maintenance strategies using a cost model. The results show that the proposed predictive maintenance strategies significantly reduce the unnecessary repair interventions, and, thus, they lead to major cost savings.
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Wang, Xin, Yi Li, Yaxi Xu, Xiaodong Liu, Tao Zheng, and Bo Zheng. "Remaining Useful Life Prediction for Aero-Engines Using a Time-Enhanced Multi-Head Self-Attention Model." Aerospace 10, no. 1 (January 13, 2023): 80. http://dx.doi.org/10.3390/aerospace10010080.
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Data-driven Remaining Useful Life (RUL) prediction is one of the core technologies of Prognostics and Health Management (PHM). Committed to improving the accuracy of RUL prediction for aero-engines, this paper proposes a model that is entirely based on the attention mechanism. The attention model is divided into the multi-head self-attention and timing feature enhancement attention models. The multi-head self-attention model employs scaled dot-product attention to extract dependencies between time series; the timing feature enhancement attention model is used to accelerate and enhance the feature selection process. This paper utilises Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) turbofan engine simulation data obtained from NASA Ames’ Prognostics Center of Excellence and compares the proposed algorithm to other models. The experiments conducted validate the superiority of our model’s approach.
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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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Chen, Xuefeng, Zhongjie Shen, Zhengjia He, Chuang Sun, and Zhiwen Liu. "Remaining life prognostics of rolling bearing based on relative features and multivariable support vector machine." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 227, no. 12 (January 11, 2013): 2849–60. http://dx.doi.org/10.1177/0954406212474395.
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Life prognostics are an important way to reduce production loss, save maintenance cost and avoid fatal machine breakdowns. Predicting the remaining life of rolling bearing with small samples is a challenge due to lack of enough condition monitoring data. This study proposes a novel prognostics model based on relative features and multivariable support vector machine to meet the challenge. Support vector machine is an effective prediction method for the small samples. However, it only focuses on the univariate time series prognosis and fails to predict the remaining life directly. So multivariable support vector machine is constructed for the life prognostics with many relative features, which are closely linked to the remaining life. Unlike the univariate support vector machine, multivariable support vector machine considers the influences among various variables and excavates the potential information of small samples as much as possible. Besides, relative root mean square with ineffectiveness of the individual difference is used to assess the bearing performance degradation and divided the stages of the whole bearing life. The simulation and run-to-failure experiments are carried out to validate the novel prognostics model. And the results demonstrate that multivariable support vector machine utilizes many kinds of useful information for the precise prediction with practical values.
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Xie, Zhiyuan, Shichang Du, Jun Lv, Yafei Deng, and Shiyao Jia. "A Hybrid Prognostics Deep Learning Model for Remaining Useful Life Prediction." Electronics 10, no. 1 (December 29, 2020): 39. http://dx.doi.org/10.3390/electronics10010039.
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Remaining Useful Life (RUL) prediction is significant in indicating the health status of the sophisticated equipment, and it requires historical data because of its complexity. The number and complexity of such environmental parameters as vibration and temperature can cause non-linear states of data, making prediction tremendously difficult. Conventional machine learning models such as support vector machine (SVM), random forest, and back propagation neural network (BPNN), however, have limited capacity to predict accurately. In this paper, a two-phase deep-learning-model attention-convolutional forget-gate recurrent network (AM-ConvFGRNET) for RUL prediction is proposed. The first phase, forget-gate convolutional recurrent network (ConvFGRNET) is proposed based on a one-dimensional analog long short-term memory (LSTM), which removes all the gates except the forget gate and uses chrono-initialized biases. The second phase is the attention mechanism, which ensures the model to extract more specific features for generating an output, compensating the drawbacks of the FGRNET that it is a black box model and improving the interpretability. The performance and effectiveness of AM-ConvFGRNET for RUL prediction is validated by comparing it with other machine learning methods and deep learning methods on the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset and a dataset of ball screw experiment.
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Muneer, Amgad, Shakirah Mohd Taib, Sheraz Naseer, Rao Faizan Ali, and Izzatdin Abdul Aziz. "Data-Driven Deep Learning-Based Attention Mechanism for Remaining Useful Life Prediction: Case Study Application to Turbofan Engine Analysis." Electronics 10, no. 20 (October 9, 2021): 2453. http://dx.doi.org/10.3390/electronics10202453.
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Accurately predicting the remaining useful life (RUL) of the turbofan engine is of great significance for improving the reliability and safety of the engine system. Due to the high dimension and complex features of sensor data in RUL prediction, this paper proposes four data-driven prognostic models based on deep neural networks (DNNs) with an attention mechanism. To improve DNN feature extraction, data are prepared using a sliding time window technique. The raw data collected after normalizing is simply fed into the suggested network, requiring no prior knowledge of prognostics or signal processing and simplifying the proposed method’s applicability. In order to verify the RUL prediction ability of the proposed DNN techniques, the C-MAPSS benchmark dataset of the turbofan engine system is validated. The experimental results showed that the developed long short-term memory (LSTM) model with attention mechanism achieved accurate RUL prediction in both scenarios with a high degree of robustness and generalization ability. Furthermore, the proposed model performance outperforms several state-of-the-art prognosis methods, where the LSTM-based model with attention mechanism achieved an RMSE of 12.87 and 11.23 for FD002 and FD003 subset of data, respectively.
Dissertations / Theses on the topic "Prognostics prediction model"
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Mishra, Madhav. "Model-based Prognostics for Prediction of Remaining Useful Life." Licentiate thesis, Luleå tekniska universitet, Drift, underhåll och akustik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-17263.
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Prognostics and healthmanagement (PHM) is an engineering discipline that aims to maintain the systembehaviour and function, and assure the mission success, safety andeffectiveness. Health management using a proper condition-based maintenance (CBM)deployment is a worldwide accepted technique and has grown very popular in manyindustries over the past decades. These techniques are relevant in environmentswhere the prediction of a failure and the prevention and mitigation of itsconsequences increase the profit and safety of the facilities concerned.Prognosis is the most critical part of this process and is nowadays recognizedas a key feature in maintenance strategies, since estimation of the remaininguseful life (RUL) is essential.PHM can provide a stateassessment of the future health of systems or components, e.g. when a degradedstate has been found. Using this technology, one can estimate how long it willtake before the equipment will reach a failure threshold, in future operatingconditions and future environmental conditions. This thesis focuses especiallyon physics-based prognostic approaches, which depend on a fundamentalunderstanding of the physical system in order to develop condition monitoringtechniques and to predict the RUL.The overall research objective of thework performed for this thesis has been to improve the accuracy and precisionof RUL predictions. The research hypothesis is that fusing the output of morethan one method will improve the accuracy and precision of the RUL estimation,by developing a new approach to prognostics that combines different remaininglife estimators and physics-based and data-driven methods. There are two waysof acquiring data for data-driven models, namely measurements of real systemsand syntactic data generation from simulations. The thesis deals with two casestudies, the first of which concerns the generation of synthetic data andindirect measurement of dynamic bearing loads and was performed atBillerudKorsäs paper mill at Karlsborg in Sweden. In this study the behaviourof a roller in a paper machine was analysed using the finite element method(FEM). The FEM model is a step towards the possibility of generating syntheticdata on different failure modes, and the possibility of estimating crucialparameters like dynamic bearing forces by combining real vibration measurementswith the FEM model. The second case study deals with the development ofprognostic methods for battery discharge estimation for Mars-based rovers. Herephysical models and measurement data were used in the prognostic development insuch a way that the degradation behaviour of the battery could be modelled andsimulated in order to predict the life-length. A particle filter turned out tobe the method of choice in performing the state assessment and predicting thefuture degradation. The method was then applied to a case study of batteriesthat provide power to the rover.Godkänd; 2015; 20151116 (madmis); Nedanstående person kommer att hålla licentiatseminarium för avläggande av teknologie licentiatexamen. Namn: Madhav Mishra Ämne: Drift och underhållsteknik/Operation and Maintenance Engineering Uppsats: Model-based Prognostics for Prediction of Remaining Useful Life Examinator: Professor Uday Kumar Institutionen för samhällsbyggnad och naturresurser Avdelning Drift, underhåll och akustik Luleå tekniska universitet Diskutant: Accos. Professor Jyoti Kumar Sinha University of Manchester, Aerospace and Civil Engineering, Manchester Tid: Torsdag 17 december 2015 kl 10.00 Plats: F1031, Luleå tekniska universitet
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Nguyen, Hoang-Phuong. "Model-based and data-driven prediction methods for prognostics." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASC021.
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La dégradation est un phénomène inévitable qui affecte les composants et les systèmes d'ingénierie, et qui peut entraîner leurs défaillances avec des conséquences potentiellement catastrophiques selon l'application. La motivation de cette Thèse est d'essayer de modéliser, d'analyser et de prédire les défaillances par des méthodes pronostiques qui peuvent permettre une gestion prédictive de la maintenance des actifs. Cela permettrait aux décideurs d'améliorer la planification de la maintenance, augmentant ainsi la disponibilité et la sûreté du système en minimisant les arrêts imprévus. Dans cet objectif, la recherche au cours de la thèse a été consacrée à l'adaptation et à l'utilisation d'approches basées sur des modèles et d'approches pilotées par les données pour traiter les processus de dégradation qui peuvent conduire à différents modes de défaillance dans les composants industriels, en utilisant différentes sources d'informations et de données pour effectuer des prédictions sur l'évolution de la dégradation et estimer la durée de vie utile restante (RUL).Les travaux de thèse ont porté sur deux applications pronostiques spécifiques: les pronostics basés sur des modèles pour la prédiction de la croissance des fissures par fatigue et les pronostics pilotées par les données pour les prédictions à pas multiples des données de séries chronologiques des composants des Centrales Nucléaires.Les pronostics basé sur des modèles compter sur le choix des modèles adoptés de Physics-of-Failure (PoF). Cependant, chaque modèle de dégradation ne convient qu'à certains processus de dégradation dans certaines conditions de fonctionnement, qui souvent ne sont pas connues avec précision. Pour généraliser, des ensembles de multiples modèles de dégradation ont été intégrés dans la méthode pronostique basée sur les modèles afin de tirer profit des différentes précisions des modèles spécifiques aux différentes dégradations et conditions. Les principales contributions des approches pronostiques proposées basées sur l'ensemble des modèles sont l'intégration d'approches de filtrage, y compris le filtrage Bayésien récursif et le Particle Filtering (PF), et de nouvelles stratégies d'ensemble pondérées tenant compte des précisions des modèles individuels dans l'ensemble aux étapes de prédiction précédentes. Les méthodes proposées ont été validées par des études de cas de croissance par fissures de fatigue simulées dans des conditions de fonctionnement variables dans le temps.Quant à la prédictions à pas multiples, elle reste une tâche difficile pour le Prognostics and Health Management (PHM) car l'incertitude de prédiction a tendance à augmenter avec l'horizon temporel de la prédiction. La grande incertitude de prédiction a limité le développement de pronostics à pas multiples dans les applications. Pour résoudre le problème, de nouveaux modèles de prédiction à pas multiples basés sur la Long Short-Term Memory (LSTM), un réseau de neurones profond développé pour traiter les dépendances à long terme dans les données de séries chronologiques, ont été développés dans cette Thèse. Pour des applications pratiques réalistes, les méthodes proposées abordent également les problèmes supplémentaires de détection d'anomalie, d'optimisation automatique des hyper-paramètres et de quantification de l'incertitude de prédiction. Des études de cas pratiques ont été envisagées, concernant les données de séries chronologiques collectées auprès des Générateurs de Vapeur et de Pompes de Refroidissement de Réacteurs de Centrales NucléairesDegradation is an unavoidable phenomenon that affects engineering components and systems, and which may lead to their failures with potentially catastrophic consequences depending on the application. The motivation of this Thesis is trying to model, analyze and predict failures with prognostic methods that can enable a predictive management of asset maintenance. This would allow decision makers to improve maintenance planning, thus increasing system availability and safety by minimizing unexpected shutdowns. To this aim, research during the Thesis has been devoted to the tailoring and use of both model-based and data-driven approaches to treat the degradation processes that can lead to different failure modes in industrial components, making use of different information and data sources for performing predictions on the degradation evolution and estimating the Remaining Useful Life (RUL).The Ph.D. work has addressed two specific prognostic applications: model-based prognostics for fatigue crack growth prediction and data-driven prognostics for multi-step ahead predictions of time series data of Nuclear Power Plant (NPP) components.Model-based prognostics relies on the choice of the adopted Physics-of-Failure (PoF) models. However, each degradation model is appropriate only to certain degradation process under certain operating conditions, which are often not precisely known. To generalize this, ensembles of multiple degradation models have been embedded in the model-based prognostic method in order to take advantage of the different accuracies of the models specific to different degradations and conditions. The main contributions of the proposed ensemble of models-based prognostic approaches are the integration of filtering approaches, including recursive Bayesian filtering and Particle Filtering (PF), and novel weighted ensemble strategies considering the accuracies of the individual models in the ensemble at the previous time steps of prediction. The proposed methods have been validated by case studies of fatigue crack growth simulated with time-varying operating conditions.As for multi-step ahead prediction, it remains a difficult task of Prognostics and Health Management (PHM) because prediction uncertainty tends to increase with the time horizon of the prediction. Large prediction uncertainty has limited the development of multi-step ahead prognostics in applications. To address the problem, novel multi-step ahead prediction models based on Long Short- Term Memory (LSTM), a deep neural network developed for dealing with the long-term dependencies in the time series data have been developed in this Thesis. For realistic practical applications, the proposed methods also address the additional issues of anomaly detection, automatic hyperparameter optimization and prediction uncertainty quantification. Practical case studies have been considered, concerning time series data collected from Steam Generators (SGs) and Reactor Coolant Pumps (RCPs) of NPPs
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Alrabady, Linda Antoun Yousef. "An online-integrated condition monitoring and prognostics framework for rotating equipment." Thesis, Cranfield University, 2014. http://dspace.lib.cranfield.ac.uk/handle/1826/9204.
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Detecting abnormal operating conditions, which will lead to faults developing
later, has important economic implications for industries trying to meet their
performance and production goals. It is unacceptable to wait for failures that
have potential safety, environmental and financial consequences. Moving from
a “reactive” strategy to a “proactive” strategy can improve critical equipment
reliability and availability while constraining maintenance costs, reducing
production deferrals, decreasing the need for spare parts. Once the fault
initiates, predicting its progression and deterioration can enable timely
interventions without risk to personnel safety or to equipment integrity.
This work presents an online-integrated condition monitoring and prognostics
framework that addresses the above issues holistically. The proposed
framework aligns fully with ISO 17359:2011 and derives from the I-P and P-F
curve. Depending upon the running state of machine with respect to its I-P and
P-F curve an algorithm will do one of the following:
(1) Predict the ideal behaviour and any departure from the normal operating
envelope using a combination of Evolving Clustering Method (ECM), a
normalised fuzzy weighted distance and tracking signal method.
(2) Identify the cause of the departure through an automated diagnostics
system using a modified version of ECM for classification.
(3) Predict the short-term progression of fault using a modified version of the
Dynamic Evolving Neuro-Fuzzy Inference System (DENFIS), called here
MDENFIS and a tracking signal method.
(4) Predict the long term progression of fault (Prognostics) using a combination
of Autoregressive Integrated Moving Average (ARIMA)- Empirical Mode
Decomposition (EMD) for predicting the future input values and MDENFIS for
predicting the long term progression of fault (output).
The proposed model was tested and compared against other models in the
literature using benchmarks and field data. This work demonstrates four
noticeable improvements over previous methods:
(1) Enhanced testing prediction accuracy, (2) comparable processing time if not
better, (3) the ability to detect sudden changes in the process and finally (4) the
ability to identify and isolate the problem source with high accuracy.
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Gorjian, Nima. "Asset health prediction using the explicit hazard model." Thesis, Queensland University of Technology, 2012. https://eprints.qut.edu.au/57314/1/Nima_Gorjian_Jolfaei_Thesis.pdf.
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The ability to estimate the asset reliability and the probability of failure is critical to reducing maintenance costs, operation downtime, and safety hazards. Predicting the survival time and the probability of failure in future time is an indispensable requirement in prognostics and asset health management. In traditional reliability models, the lifetime of an asset is estimated using failure event data, alone; however, statistically sufficient failure event data are often difficult to attain in real-life situations due to poor data management, effective preventive maintenance, and the small population of identical assets in use. Condition indicators and operating environment indicators are two types of covariate data that are normally obtained in addition to failure event and suspended data. These data contain significant information about the state and health of an asset. Condition indicators reflect the level of degradation of assets while operating environment indicators accelerate or decelerate the lifetime of assets. When these data are available, an alternative approach to the traditional reliability analysis is the modelling of condition indicators and operating environment indicators and their failure-generating mechanisms using a covariate-based hazard model.
The literature review indicates that a number of covariate-based hazard models have been developed. All of these existing covariate-based hazard models were developed based on the principle theory of the Proportional Hazard Model (PHM). However, most of these models have not attracted much attention in the field of machinery prognostics. Moreover, due to the prominence of PHM, attempts at developing alternative models, to some extent, have been stifled, although a number of alternative models to PHM have been suggested. The existing covariate-based hazard models neglect to fully utilise three types of asset health information (including failure event data (i.e. observed and/or suspended), condition data, and operating environment data) into a model to have more effective hazard and reliability predictions. In addition, current research shows that condition indicators and operating environment indicators have different characteristics and they are non-homogeneous covariate data. Condition indicators act as response variables (or dependent variables) whereas operating environment indicators act as explanatory variables (or independent variables). However, these non-homogenous covariate data were modelled in the same way for hazard prediction in the existing covariate-based hazard models. The related and yet more imperative question is how both of these indicators should be effectively modelled and integrated into the covariate-based hazard model.
This work presents a new approach for addressing the aforementioned challenges. The new covariate-based hazard model, which termed as Explicit Hazard Model (EHM), explicitly and effectively incorporates all three available asset health information into the modelling of hazard and reliability predictions and also drives the relationship between actual asset health and condition measurements as well as operating environment measurements. The theoretical development of the model and its parameter estimation method are demonstrated in this work. EHM assumes that the baseline hazard is a function of the both time and condition indicators. Condition indicators provide information about the health condition of an asset; therefore they update and reform the baseline hazard of EHM according to the health state of asset at given time t. Some examples of condition indicators are the vibration of rotating machinery, the level of metal particles in engine oil analysis, and wear in a component, to name but a few. Operating environment indicators in this model are failure accelerators and/or decelerators that are included in the covariate function of EHM and may increase or decrease the value of the hazard from the baseline hazard. These indicators caused by the environment in which an asset operates, and that have not been explicitly identified by the condition indicators (e.g. Loads, environmental stresses, and other dynamically changing environment factors). While the effects of operating environment indicators could be nought in EHM; condition indicators could emerge because these indicators are observed and measured as long as an asset is operational and survived.
EHM has several advantages over the existing covariate-based hazard models. One is this model utilises three different sources of asset health data (i.e. population characteristics, condition indicators, and operating environment indicators) to effectively predict hazard and reliability. Another is that EHM explicitly investigates the relationship between condition and operating environment indicators associated with the hazard of an asset. Furthermore, the proportionality assumption, which most of the covariate-based hazard models suffer from it, does not exist in EHM. According to the sample size of failure/suspension times, EHM is extended into two forms: semi-parametric and non-parametric. The semi-parametric EHM assumes a specified lifetime distribution (i.e. Weibull distribution) in the form of the baseline hazard. However, for more industry applications, due to sparse failure event data of assets, the analysis of such data often involves complex distributional shapes about which little is known. Therefore, to avoid the restrictive assumption of the semi-parametric EHM about assuming a specified lifetime distribution for failure event histories, the non-parametric EHM, which is a distribution free model, has been developed. The development of EHM into two forms is another merit of the model.
A case study was conducted using laboratory experiment data to validate the practicality of the both semi-parametric and non-parametric EHMs. The performance of the newly-developed models is appraised using the comparison amongst the estimated results of these models and the other existing covariate-based hazard models. The comparison results demonstrated that both the semi-parametric and non-parametric EHMs outperform the existing covariate-based hazard models. Future research directions regarding to the new parameter estimation method in the case of time-dependent effects of covariates and missing data, application of EHM in both repairable and non-repairable systems using field data, and a decision support model in which linked to the estimated reliability results, are also identified.
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Tamssaouet, Ferhat. "Towards system-level prognostics : modeling, uncertainty propagation and system remaining useful life prediction." Thesis, Toulouse, INPT, 2020. http://www.theses.fr/2020INPT0079.
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Le pronostic est le processus de prédiction de la durée de vie résiduelle utile (RUL) des composants, sous-systèmes ou systèmes. Cependant, jusqu'à présent, le pronostic a souvent été abordé au niveau composant sans tenir compte des interactions entre les composants et l'impact de l'environnement, ce qui peut conduire à une mauvaise prédiction du temps de défaillance dans des systèmes complexes. Dans ce travail, une approche de pronostic au niveau du système est proposée. Cette approche est basée sur un nouveau cadre de modélisation : le modèle d'inopérabilité entrée-sortie (IIM), qui permet de prendre en compte les interactions entre les composants et les effets du profil de mission et peut être appliqué pour des systèmes hétérogènes. Ensuite, une nouvelle méthodologie en ligne pour l'estimation des paramètres (basée sur l'algorithme de la descente du gradient) et la prédiction du RUL au niveau système (SRUL) en utilisant les filtres particulaires (PF), a été proposée. En détail, l'état de santé des composants du système est estimé et prédit d'une manière probabiliste en utilisant les PF. En cas de divergence consécutive entre les estimations a priori et a posteriori de l'état de santé du système, la méthode d'estimation proposée est utilisée pour corriger et adapter les paramètres de l'IIM. Finalement, la méthodologie développée, a été appliquée sur un système industriel réaliste : le Tennessee Eastman Process, et a permis une prédiction du SRUL dans un temps de calcul raisonnablePrognostics is the process of predicting the remaining useful life (RUL) of components, subsystems, or systems. However, until now, the prognostics has often been approached from a component view without considering interactions between components and effects of the environment, leading to a misprediction of the complex systems failure time. In this work, a prognostics approach to system-level is proposed. This approach is based on a new modeling framework: the inoperability input-output model (IIM), which allows tackling the issue related to the interactions between components and the mission profile effects and can be applied for heterogeneous systems. Then, a new methodology for online joint system RUL (SRUL) prediction and model parameter estimation is developed based on particle filtering (PF) and gradient descent (GD). In detail, the state of health of system components is estimated and predicted in a probabilistic manner using PF. In the case of consecutive discrepancy between the prior and posterior estimates of the system health state, the proposed estimation method is used to correct and to adapt the IIM parameters. Finally, the developed methodology is verified on a realistic industrial system: The Tennessee Eastman Process. The obtained results highlighted its effectiveness in predicting the SRUL in reasonable computing time
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Sánchez, Sardi Héctor Eloy. "Prognostics and health aware model predictive control of wind turbines." Doctoral thesis, Universitat Politècnica de Catalunya, 2017. http://hdl.handle.net/10803/463321.
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Wind turbines components are subject to considerable stresses and fatigue due to extreme environmental conditions to which they are exposed, especially those located offshore. Also, the most common faults present in wind turbine components have been investigated for years by the research community and that has led to propose a fault diagnosis and fault tolerant control wind turbine benchmark which include a set of faults that affect the sensors and actuators of several wind turbine components.
This thesis presents some contributions to the fields of fault diagnosis, fault-tolerant control, prognostics and its integration with wind turbine control which leads to proposing a control approach called health-aware model predictive control (HAMPC). The contributions are summarized below:
- Model-based fault diagnosis: to perform fault detection and isolation interval-based observers together with a set of analytical redundant relations (ARRs) are obtained based on a structural analysis and the fault signature matrix that relates the ARRs with the faults. - Fault tolerant control: it is proposed a fault tolerant control scheme that integrates fault detection and an algorithm for fault accommodation. The scheme has the objective to avoid the increment of blades and tower loads when a fault in the rotor azimuth angle sensor occurs using the individual pitch control technique (IPC).
- Wind turbine blades fatigue prognostics and degradation: fatigue is assessed using the rainflow counting algorithm which is used to estimate the accumulated damage and for degradation, it is used a stiffness degradation model of blades material which is used to make predictions of remaining useful life (RUL).
- Wind turbines health control: the module for the health of the system based on fatigue damage estimation and RUL predictions is integrated with model predictive control (MPC) leading to the proposed control approach (HAMPC).
The contributions presented in this thesis have been validated on a wind turbine study case that uses a 5MW wind turbine reference model implemented in a high fidelity wind turbine simulator (FAST).Els components dels aerogeneradors estan sotmesos a considerable estrès i fatiga, degut a les condicions ambientals extremes a les quals estan exposats, especialment els localitzats en alta mar. Per aquest motiu, al comunitat científica durant els últims anys ha investigat les averies més comunes presents en els aerogeneradors, fet que ha portat a proposar un cas d'estudi de diagnosi i control tolerant de fallades que inclou un conjunt de fallades que afecten a diversos components dels aerogeneradors. Aquesta tesi presenta algunes contribucions en els camps de la diagnosi de fallades, el control tolerant de fallades i la prognosi, així com la seva integració amb el control d'aerogeneradors, fet que ha portat a proposar una tècnica de control anomenada control predictiu basada en models conscients de la salut del sistema (HAMPC). Concretament les aportacions es poden resumir en: - Diagnosi de fallades basada en models: per a la detecció s'utilitzen observadors intervalars i l'aïllament de la fallada es fa en base el conjunt d'ARRs obtinguts de l'anàlisi estructural i de la matriu de signatures de fallades que relaciona les ARRs amb les fallades. - Control tolerant de fallades: es proposa un esquema de control tolerant a fallades que integra la detecció de fallades i algoritme d'acomodació de fallades, i té per objectiu evitar l'augment de càrregues en la pala i la torre quan es produeix una fallada en el sensor azimuth quan es fa un control individual de la inclinació de les pales (IPC). - Prognosi de la fatiga i la degradació de les pales: la fatiga s'avalua amb un algorisme denominat "rainflow counting" amb el qual es fa estimació del dany acumulat i per a la degradació es fa servir un model de degradació de la rigidesa del material amb el qual es fan prediccions de la vida útil restant (RUL). - Control de la salut d'aerogeneradors: s'ha integrat la gestió de la salut del sistema basat en danys per fatiga o prediccions de RUL amb control predictiu basat en models (MPC) donant lloc al control que anomenem HAMPC. Les contribucions presentades en aquesta tesi han sigut validades en un cas d'estudi d'aerogeneradors basat en un aerogenerador de referència de 5MW de potència implementat en el simulador d'aerogeneradors d'alta fidelitat conegut amb el nom de FAST.
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Moskowitz, Chaya S. "Quantifying and comparing the predictive accuracy of prognostic factors /." Thesis, Connect to this title online; UW restricted, 2002. http://hdl.handle.net/1773/9610.
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Iwakami, Naotsugu. "Optimal Sampling in Derivation Studies was Associated with Improved Discrimination in External Validation for Heart Failure Prognostic Models." Kyoto University, 2020. http://hdl.handle.net/2433/259731.
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Gwilliam, Bridget. "The development of prognostic models for predicting survival in patients with advanced cancer." Thesis, St George's, University of London, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.546796.
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Marques, Maria João Pereira Vicente Dias. "Análise retrospetiva de 92 casos de cólica em equinos admitidos em segunda opinião para tratamento hospitalar." Master's thesis, Universidade de Lisboa, Faculdade de Medicina Veterinária, 2018. http://hdl.handle.net/10400.5/15833.
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Dissertação de Mestrado Integrado em Medicina VeterináriaA cólica é uma patologia de importância preeminente em equinos, cuja identificação da causa nem sempre é fácil, fazendo com que a determinação precoce de um prognóstico seja essencial. Assim, foi realizado um estudo retrospetivo em 92 casos de cólica recebidos pelo Serviço de Cirurgia e Urgência em Equinos da FMV-ULisboa. Os objetivos do presente estudo foram: 1) caracterizar os casos de cólica referenciados para o SCUE FMV-ULisboa, avaliando o tipo de intervenção clínica, a causa de cólica e a taxa de alta hospitalar; 2) avaliar o valor prognóstico de cada um dos indicadores recolhidos na admissão; 3) comparar o valor destes indicadores entre os dois tipos de intervenção clínica, médica e cirúrgica; e 4) elaborar um modelo multivariado de predição de prognóstico. Estimou-se que 82% dos animais submetidos a intervenção cirúrgica e 75% dos animais tratados medicamente tiveram alta hospitalar, e que 25% dos animais submetidos a laparotomia sofreram íleo pós-cirúrgico. Foram recolhidos na admissão os seguintes dados: idade, tempo entre sinalização e admissão hospitalar, refluxo gastrointestinal, frequência cardíaca, hematócrito, proteínas totais séricas, proteínas totais do líquido peritoneal, lactato peritoneal e lactato sanguíneo. Nas cólicas médicas, os indicadores hematócrito, frequência cardíaca e lactato peritoneal foram considerados estatisticamente significativos (p<0,05), o lactato sanguíneo marginalmente significativo (p=0,053) e as proteínas do líquido peritoneal tendencialmente significativas (p<0,10). Foram elaborados dois modelos de predição multivariável. O modelo de 3 preditores (lactato sanguíneo, frequência cardíaca e hematócrito) com especificidade de 42,9% e sensibilidade de 96,0%. O modelo de 5 preditores (lactato sanguíneo, frequência cardíaca, hematócrito, idade e proteínas totais séricas) com especificidade de 71,4% e sensibilidade de 95,7%. Nas cólicas cirúrgicas, não foi possível determinar preditores significativos nem elaborar modelos de predição. Foi, ainda, criada uma aplicação informática de cálculo de probabilidade de alta hospitalar com base nos modelos descritos. Finalmente, conclui-se que a recolha de líquido peritoneal deverá ser feita com mais frequência pois os seus indicadores parecem transmitir informação valiosa. O modelo de 3 preditores, apesar de ter uma especificidade menor para a amostra em estudo, será provavelmente mais fiável do ponto de vista clínico, para utilização futura. Para além disso, é espectado que com o aumento da dimensão da amostra, estes modelos se tornem mais robustos.
ABSTRACT - A RETROSPECTIVE REVIEW OF 92 EQUINE COLIC CASES REFERRED FOR HOSPITAL TREATMENT - Colic is a really important syndrome in the equine species. To identify a diagnosis can be a true challenge, so the early determination of a prognosis is essential. Therefore, a retrospective study was performed in 92 colic cases admitted at the “Equine Surgery and Emergency Services” (Lisbon University). The objectives of this study were: 1) describe the colic cases and evaluate the clinical approach (medical or surgical), the origin of the problem and rate of survival; 2) estimate the prognostic value of each one of the collected predictors at the admission process; 3) compare the predictors according to the clinical approach; and 4) elaborate a multivariable prognostic prediction model. In this study, the survival rate was 82% for the horses submitted to surgical intervention and 75% for the horses treated medically; and, 25% of the horses in which laparotomy was performed developed post-operative ileus. The following data were collected during admission at the hospital: age, time between the onset of clinical signs and referral, gastrointestinal reflux, cardiac frequency, haematocrit, blood total protein, peritoneal fluid total protein, peritoneal fluid lactate, blood lactate. In medical colics, haematocrit, cardiac frequency and peritoneal fluid lactate were statistically significant (p<0,05), blood lactate was marginally significant (p=0,053) and peritoneal fluid total protein was tendentially significant (p<0,10). Two multivariable prognostic prediction models were elaborated. The three predictors model (blood lactate, cardiac frequency and haematocrit) had a specificity of 42,9% and a sensibility of 96,0%. The five predictors model (blood lactate, cardiac frequency, haematocrit, blood total protein and age) had a specificity of 71,4% and a sensibility of 95,7%. In surgical colics, it wasn’t possible to determine statistically significant predictors neither to elaborate prediction models. Based on the previously descript models, a computerized application to calculate the survival probability was created. It was concluded that peritoneal fluid should be collected more often, since peritoneal lactate and peritoneal fluid total protein seem to be providers of valuable information. Even though, the three predictors model has a reduced specificity for the study sample, it will be probably more reliable from the clinical point of view for further applications. Furthermore, it’s expected that with the increasing of the sample size, these models will get more robust.
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Books on the topic "Prognostics prediction model"
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Len'kov, Roman. Social forecasting and planning. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1058988.
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The tutorial describes the preconditions of sociopragmatics research in Russia on the background of evolutionary processes of social prognostics of the twentieth century. Considered the essential characteristics of social forecasting, its subject and range of issues. Based on analysis of classification schemes methods of scientific forecasting offers the author's approach to classification of methods of social forecasting. Special attention is paid to the description of the characteristics, the specific application and selection procedure of the ways of making social predictions.
Theoretical and applied analysis of the foundations of social design, the direction of its implementation and research methods used for it. The conceptual basis of design in education on the example of the educational process in the University. Given the model structure, rationale and testing of design solutions.
The third edition of the book is dedicated to the 100th anniversary of the State University of management.
Meets the current requirements of the Federal state educational standard of higher education.
For students of higher educational institutions, students of humanitarian directions and specialities.
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Wagner, Carolin. Process-Centric View on Predictive Maintenance and Fleet Prognostics: Development of a Process Reference Model and a Development Method for Fleet Prognostics to Guide Predictive Maintenance Projects. Logos Verlag Berlin, 2022.
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Steinhauser, Karen E., and James A. Tulsky. Defining a ‘good’ death. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199656097.003.0008.
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Although any outcome of an advanced illness can be predicted, in palliative care settings the word ‘prognosis’ usually means the estimated time to death. Prognosis is an important but challenging set of clinical skills for palliative medicine clinicians to master. It is important because patients and families want to know what to expect, it influences clinical decision-making, and it may determine eligibility for services. It is challenging because of the inherent uncertainty of making predictions and because dying is not an easy topic to discuss. Advances in statistical computing have allowed the development of mathematical models and predictive tools that are now more accurate than clinical estimates. A large section of this chapter is devoted to presenting and evaluating several of these models, although prognostic uncertainty remains a significant issue even with them, and survival estimates should never drive clinical decision-making alone.
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Riley, Richard D., Danielle van der Windt, Peter Croft, and Karel G. M. Moons, eds. Prognosis Research in Health Care. Oxford University Press, 2019. http://dx.doi.org/10.1093/med/9780198796619.001.0001.
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What is going to happen to me, doctor?’ ‘What outcomes am I likely to experience?’ ‘Will this treatment work for me?’ Prognosis—forecasting the future—has always been a part of medical practice and caring for the sick. In modern healthcare it now has a new importance, with large financial investments being made to personalize clinical decisions and tailor treatment strategies to improve individual health outcomes based on prognostic information. Prognosis research—the study of future outcomes in people with a particular health condition—provides the critical evidence for obtaining, evaluating, and implementing prognostic information within modern healthcare. This new book, written and edited by experts in the field, including clinicians, epidemiologists, statisticians, and other healthcare professionals, is a comprehensive and unified account of prognosis research in the broadest sense. It explains the concepts behind prognosis in medical practice and prognosis research, and provides a practical foundation for those developing, conducting, interpreting, synthesizing, and appraising prognosis studies. It recommends a framework of four basic prognosis research types, pioneered by the PROGRESS group, and provides explicit guidance on the conduct, analysis, and reporting of prognosis studies for each type. Key topics are overall prognosis in clinically relevant populations; prognostic factors associated with changes in prognosis across individuals; prognostic models for individual outcome risk prediction; and predictors of treatment effects. Examples are given of the impact of prognosis research across a broad range of healthcare topics, and the book also signals the latest developments in prognosis research, including systematic reviews and meta-analysis of prognosis studies, and the use of electronic health records and machine learning in prognosis research.
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Rubia, Katya. ADHD brain function. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198739258.003.0007.
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ADHD patients appear to have complex multisystem impairments in several cognitive-domain dissociated inferior, dorsolateral, and medial fronto-striato-parietal and frontocerebellar neural networks during inhibition, attention, working memory, and timing functions. There is emerging evidence for abnormalities in motivation and affect control regions, most prominently in ventral striatum, but also orbital/ventromedial frontolimbic areas. Furthermore, there is an immature interrelationship between hypoengaged task-positive cognitive control networks and a poorly ‘switched off’ default mode network, both of which impact performance. Stimulant medication enhances the activation of inferior frontostriatal systems, while atomoxetine appears to have more pronounced effects on the dorsal attention network. More studies are needed to understand the neurofunctional correlates of the effects of age, gender, ADHD subtypes, and comorbidities with other psychiatric conditions. The use of pattern recognition analyses applied to imaging to make individual diagnostic or prognostic predictions are promising and will be the challenge over the next decade.
Book chapters on the topic "Prognostics prediction model"
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Tinga, Tiedo, and Richard Loendersloot. "Physical Model-Based Prognostics and Health Monitoring to Enable Predictive Maintenance." In Predictive Maintenance in Dynamic Systems, 313–53. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05645-2_11.
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Harrell, Frank E., Kerry L. Lee, and Daniel B. Mark. "Prognostic/Clinical Prediction Models: Multivariable Prognostic Models: Issues in Developing Models, Evaluating Assumptions and Adequacy, and Measuring and Reducing Errors." In Tutorials in Biostatistics, 223–49. Chichester, UK: John Wiley & Sons, Ltd, 2005. http://dx.doi.org/10.1002/0470023678.ch2b(i).
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Staibano, Stefania. "Molecular Markers for Patient Selection and Stratification: Personalized Prognostic Predictive Models." In Prostate Cancer: Shifting from Morphology to Biology, 213–19. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-7149-9_13.
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Prathan, Sorada, and Siew Hock Ow. "A Model for Predicting and Determining the Best-Fit Programmers Using Prognostic Attributes." In Lecture Notes in Electrical Engineering, 294–301. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8276-4_28.
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ArunKumar, K., and S. Vasundra. "Prognostic Outcome Prediction on Patient Treatment Trajectory Data Using PSO Optimization on LTSM-RNN Model." In Advances in Intelligent Systems and Computing, 1045–61. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7330-6_78.
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Wang, Dong, and Kwok-Leung Tsui. "State Space Models Based Prognostic Methods for Remaining Useful Life Prediction of Rechargeable Batteries." In Statistical Modeling for Degradation Data, 307–34. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5194-4_16.
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Aria, Massimo, Corrado Cuccurullo, and Agostino Gnasso. "Supporting decision-makers in healthcare domain. A comparative study of two interpretative proposals for Random Forests." In Proceedings e report, 179–84. Florence: Firenze University Press, 2021. http://dx.doi.org/10.36253/978-88-5518-461-8.34.
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The growing success of Machine Learning (ML) is making significant improvements to predictive models, facilitating their integration in various application fields, especially the healthcare context. However, it still has limitations and drawbacks, such as the lack of interpretability which does not allow users to understand how certain decisions are made. This drawback is identified with the term "Black-Box", as well as models that do not allow to interpret the internal work of certain ML techniques, thus discouraging their use. In a highly regulated and risk-averse context such as healthcare, although "trust" is not synonymous with decision and adoption, trusting an ML model is essential for its adoption. Many clinicians and health researchers feel uncomfortable with black box ML models, even if they achieve high degrees of diagnostic or prognostic accuracy. Therefore more and more research is being conducted on the functioning of these models. Our study focuses on the Random Forest (RF) model. It is one of the most performing and used methodologies in the context of ML approaches, in all fields of research from hard sciences to humanities. In the health context and in the evaluation of health policies, their use is limited by the impossibility of obtaining an interpretation of the causal links between predictors and response. This explains why we need to develop new techniques, tools, and approaches for reconstructing the causal relationships and interactions between predictors and response used in a RF model. Our research aims to perform a machine learning experiment on several medical datasets through a comparison between two methodologies, which are inTrees and NodeHarvest. They are the main approaches in the rules extraction framework. The contribution of our study is to identify, among the approaches to rule extraction, the best proposal for suggesting the appropriate choice to decision-makers in the health domain.
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Marano, Giuseppe, Patrizia Boracchi, and Elia M. Biganzoli. "Estimation of a Piecewise Exponential Model by Bayesian P-splines Techniques for Prognostic Assessment and Prediction." In Computational Intelligence Methods for Bioinformatics and Biostatistics, 183–98. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24462-4_16.
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Di Maso, Matteo, Monica Ferraroni, Pasquale Ferrante, Serena Delbue, and Federico Ambrogi. "Longitudinal profile of a set of biomarkers in predicting Covid-19 mortality using joint models." In Proceedings e report, 191–96. Florence: Firenze University Press, 2021. http://dx.doi.org/10.36253/978-88-5518-461-8.36.
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In survival analysis, time-varying covariates are endogenous when their measurements are directly related to the event status and incomplete information occur at random points during the follow-up. Consequently, the time-dependent Cox model leads to biased estimates. Joint models (JM) allow to correctly estimate these associations combining a survival and longitudinal sub-models by means of a shared parameter (i.e., random effects of the longitudinal sub-model are inserted in the survival one). This study aims at showing the use of JM to evaluate the association between a set of inflammatory biomarkers and Covid-19 mortality. During Covid-19 pandemic, physicians at Istituto Clinico di Città Studi in Milan collected biomarkers (endogenous time-varying covariates) to understand what might be used as prognostic factors for mortality. Furthermore, in the first epidemic outbreak, physicians did not have standard clinical protocols for management of Covid-19 disease and measurements of biomarkers were highly incomplete especially at the baseline. Between February and March 2020, a total of 403 COVID-19 patients were admitted. Baseline characteristics included sex and age, whereas biomarkers measurements, during hospital stay, included log-ferritin, log-lymphocytes, log-neutrophil granulocytes, log-C-reactive protein, glucose and LDH. A Bayesian approach using Markov chain Monte Carlo algorithm were used for fitting JM. Independent and non-informative priors for the fixed effects (age and sex) and for shared parameters were used. Hazard ratios (HR) from a (biased) time-dependent Cox and joint models for log-ferritin levels were 2.10 (1.67-2.64) and 1.73 (1.38-2.20), respectively. In multivariable JM, doubling of biomarker levels resulted in a significantly increase of mortality risk for log-neutrophil granulocytes, HR=1.78 (1.16-2.69); for log-C-reactive protein, HR=1.44 (1.13-1.83); and for LDH, HR=1.28 (1.09-1.49). Increasing of 100 mg/dl of glucose resulted in a HR=2.44 (1.28-4.26). Age, however, showed the strongest effect with mortality risk starting to rise from 60 years.
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D'Agostino, Ralph B., Albert J. Belanger, Elizabeth W. Markson, Maggie Kelly-Hayes, and Philip A. Wolf. "Prognostic/Clinical Prediction Models: Development of Health Risk Appraisal Functions in the Presence of Multiple Indicators: The Framingham Study Nursing Home Institutionalization Model." In Tutorials in Biostatistics, 209–22. Chichester, UK: John Wiley & Sons, Ltd, 2005. http://dx.doi.org/10.1002/0470023678.ch2b.
Full textConference papers on the topic "Prognostics prediction model"
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Li, Zhixiong, Dazhong Wu, Chao Hu, Janis Terpenny, and Sheng Shen. "Ensemble Prognostics With Degradation-Dependent Weights: Prediction of Remaining Useful Life for Aircraft Engines." In ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-68315.
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The objective of this research is to introduce a new ensemble prognostics method with degradation-dependent weights. Specifically, this method assigns an optimized, degradation-dependent weight to each learner (i.e., learning algorithm) such that the weighted sum of the prediction results from all the learners predicts the RUL of mechanical components with better accuracy. The ensemble prognostic algorithm is demonstrated using a data set collected from an engine simulator. Analysis results show that the predictive model trained by the ensemble learning algorithm outperform the existing methods.
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Yuan, Yuchen, and Wanqing Song. "Degradation Prediction Of Tool Based On Fractional Levy Prediction Model." In 2022 Global Reliability and Prognostics and Health Management (PHM-Yantai). IEEE, 2022. http://dx.doi.org/10.1109/phm-yantai55411.2022.9942217.
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Xi, Zhimin, and Pingfeng Wang. "A Copula Based Sampling Method for Residual Life Prediction of Engineering Systems Under Uncertainty." In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-71105.
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The success of health prognostics of engineering systems will allow engineers to shift the traditional breakdown and time based maintenance to the state-of-art predictive and condition-based maintenance. Performing the right type of maintenance activity at the right time will minimize maintenance costs and the downtime of engineering systems. However, techniques and methodologies for health prognostics are typically application-specific. This paper aims at developing a generic real time sensor-based prognostic methodology for predicting residual life of engineering systems by modeling explicit relationship between the failure time and the time realizations at different degradation levels. Specifically, a Copula based sampling method is proposed with four technical components for off-line training and on-line life prediction. First of all, degradation signals are pre-processed to have non-decreasing degradation data sets. Next, degradation data sets are dicretized into a certain number of degradation levels with associated time realizations. Then, explicit statistical dependence modeling between the failure time and the time realizations at different degradation levels is conducted using the Bayesian Copula approach and the semi-Copula model. Finally, probability density function of the failure time and the residual life are efficiently predicted using the sampling method provided that we know some true time realizations at a certain number of degradation levels. Residual life predictions of electric cooling fans are employed to demonstrate the proposed method.
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Islam, Mohammad Rubyet, and Peter Sandborn. "Application of Prognostics and Health Management (PHM) to Software System Fault and Remaining Useful Life (RUL) Prediction." In ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/detc2021-70508.
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Abstract Prognostics and Health Management (PHM) is an engineering discipline focused on predicting the point at which systems or components will no longer perform as intended. The prediction is often articulated as a Remaining Useful Life (RUL). RUL is an important decision-making tool for contingency mitigation, i.e., the prediction of an RUL (and its associated confidence) enables decisions to be made about how and when to maintain the system. PHM is generally applied to hardware systems in the electronics and non-electronics application domains. The application of PHM (and RUL) concepts has not been explored for application to software. Today, software (SW) health management is confined to diagnostic assessments that identify problems, whereas prognostic assessment potentially indicates when in the future a problem will become detrimental to the operation of the system. Relevant areas such as SW defect prediction, SW reliability prediction, predictive maintenance of SW, SW degradation, and SW performance prediction, exist, but all represent static models, built upon historical data — none of which can calculate an RUL. This paper addresses the application of PHM concepts to software systems for fault predictions and RUL estimation. Specifically, we wish to address how PHM can be used to make decisions for SW systems such as version update, module changes, rejuvenation, maintenance scheduling and abandonment. This paper presents a method to prognostically and continuously predict the RUL of a SW system based on usage parameters (e.g., numbers and categories of releases) and multiple performance parameters (e.g., response time). The model is validated based on actual data (on performance parameters), generated by the test beds versus predicted data, generated by a predictive model. Statistical validation (regression validation) has been carried out as well. The test beds replicate and validate faults, collected from a real application, in a controlled and standard test (staging) environment. A case study based on publicly available data on faults and enhancement requests for the open-source Bugzilla application is presented. This case study demonstrates that PHM concepts can be applied to SW systems and RUL can be calculated to make decisions on software version update or upgrade, module changes, rejuvenation, maintenance schedule and total abandonment.
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Pang, Zhenan, Changhua Hu, Xiaosheng Si, Jianxun Zhang, and Hong Pei. "Life Prediction Approach by Integrating Nonlinear Accelerated Degradation Model and Hazard Rate Model." In 2018 Prognostics and System Health Management Conference (PHM-Chongqing). IEEE, 2018. http://dx.doi.org/10.1109/phm-chongqing.2018.00073.
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Wang, Wenbin, and Matthew Carr. "An adapted Brownion motion model for plant residual life prediction." In 2010 Prognostics and System Health Management Conference (PHM). IEEE, 2010. http://dx.doi.org/10.1109/phm.2010.5413487.
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Xiao, Bei, Peng-Cheng Luo, Zhi-Jun Cheng, Xiao-Nan Zhang, and Xin-Wu Hu. "Dam Deformation Prediction Model Based on Combined Gaussian Process." In 2019 Prognostics and System Health Management Conference (PHM-Qingdao). IEEE, 2019. http://dx.doi.org/10.1109/phm-qingdao46334.2019.8942944.
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Tang, Liang, Jonathan DeCastro, Greg Kacprzynski, Kai Goebel, and George Vachtsevanos. "Filtering and prediction techniques for model-based prognosis and uncertainty management." In 2010 Prognostics and System Health Management Conference (PHM). IEEE, 2010. http://dx.doi.org/10.1109/phm.2010.5413490.
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Wenjia Xu and Wenbin Wang. "An adaptive gamma process based model for residual useful life prediction." In 2012 Prognostics and System Health Management Conference (PHM). IEEE, 2012. http://dx.doi.org/10.1109/phm.2012.6228785.
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Lall, Pradeep, Junchao Wei, and Peter Sakalaukus. "Bayesian probabilistic model for life prediction and fault mode classification of solid state luminaires." In 2014 IEEE Conference on Prognostics and Health Management (PHM). IEEE, 2014. http://dx.doi.org/10.1109/icphm.2014.7036401.
Full textReports on the topic "Prognostics prediction model"
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Seale, Maria, Natàlia Garcia-Reyero, R. Salter, and Alicia Ruvinsky. An epigenetic modeling approach for adaptive prognostics of engineered systems. Engineer Research and Development Center (U.S.), July 2021. http://dx.doi.org/10.21079/11681/41282.
Full textAbstract:
Prognostics and health management (PHM) frameworks are widely used in engineered systems, such as manufacturing equipment, aircraft, and vehicles, to improve reliability, maintainability, and safety. Prognostic information for impending failures and remaining useful life is essential to inform decision-making by enabling cost versus risk estimates of maintenance actions. These estimates are generally provided by physics-based or data-driven models developed on historical information. Although current models provide some predictive capabilities, the ability to represent individualized dynamic factors that affect system health is limited. To address these shortcomings, we examine the biological phenomenon of epigenetics. Epigenetics provides insight into how environmental factors affect genetic expression in an organism, providing system health information that can be useful for predictions of future state. The means by which environmental factors influence epigenetic modifications leading to observable traits can be correlated to circumstances affecting system health. In this paper, we investigate the general parallels between the biological effects of epigenetic changes on cellular DNA to the influences leading to either system degradation and compromise, or improved system health. We also review a variety of epigenetic computational models and concepts, and present a general modeling framework to support adaptive system prognostics.
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Huilai, Zhang. Prognostic factors or prediction models for POD24 in patients with newly diagnosed FL：a systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, February 2021. http://dx.doi.org/10.37766/inplasy2021.2.0034.
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Shadurdyyev, G. Analysis of sets of factors affecting the variable flow of the Amu Darya River to create a seasonal prognostic model. Kazakh-German University, December 2022. http://dx.doi.org/10.29258/dkucrswp/2022/53-72.eng.
Full textAbstract:
The Amu Darya River is a transboundary river whose flow of the river in high-water years reaches up to 108 km3 and in low-water years up to 47 km3 and these are huge fluctuations in the water flow of the river for Tajikistan, Kyrgyzstan, Uzbekistan, Turkmenistan, and Afghanistan, that share water among themselves. The point to consider is that the downstream countries Turkmenistan and Uzbekistan (and possibly Afghanistan in the future) use a lot of water for irrigation, and therefore these countries are the ones most in need of an accurate forecast of the volume of water for the upcoming season. An accurate forecast of the volume of water on the seasonal scale is necessary for better planning of the structure of crops, and subsequently water use in the irrigation of crops. An acceptable solution to this challenge is the construction of an empirical time series model that will be used to predict the seasonal flows of the Amu Darya River to improve the planning and management of water resources in downstream countries. This article considers three important discharge time series in the larger Amu Darya Basin. These include the Kerki Gauge on the Amu Darya, Darband Gauge on Vaksh River and Khorog Gauge on Gunt River. Long-term time series from these stations are available for the study of the development and implementation of time-series based models for the prediction of discharge in the basin. At this stage, we attempt to demonstrate a proof-of-concept which can in a second step convince stakeholders to share such type of discharge data operationally for more effective water allocation between sectors and countries. All our work was carried out with the quantitative tools R/RStudio and QGIS. It can serve as a stepping stone for more complex forecasting models in the future.
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Neodo, Anna, Fiona Augsburger, Jan Waskowski, Joerg C. Schefold, and Thibaud Spinetti. Monocytic HLA-DR expression and clinical outcomes in adult ICU patients with sepsis – a systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, November 2022. http://dx.doi.org/10.37766/inplasy2022.11.0119.
Full textAbstract:
Review question / Objective: The scope of this review was defined using PICOTS framework where 1) population: adult critically ill patients with sepsis or septic shock; 2) index prognostic factor: cell surface protein expression of mHLA-DR in blood; 3) comparative factor: none; 4) outcomes to be predicted: mortality, secondary infections, length of stay, and organ dysfunction score (sequential organ failure assessment [SOFA], multiple organ dysfunction score [MODS], logistic organ dysfunction score [LODS]), composite outcomes where component endpoints consist of at least one of the outcomes stated above (e.g., “adverse outcome” defined as death or secondary infection), 5) timing (of the prediction horizon and the moment of prognosis): any; and 6) setting: ICU. Condition being studied: Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to severe infections. It can further progress to septic shock, which includes hemodynamic failure and increased mortality rates. A recent worldwide epidemiological study estimated 48.9 million sepsis cases and 11 million of sepsis-related deaths (~20% of global deaths in 2017). Although its management has advanced considerably, sepsis remains deadly and challenging to treat. The 28/30-day mortality averages around 25% for sepsis and 38% for septic shock in high-income countries. Current models describe the underlying pathophysiologic mechanisms of sepsis as an interplay between concurrent dysfunctional pro- and anti-inflammatory immune response.