Journal articles on the topic 'Prognostics prediction model'
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1
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.
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Xie, Mingjiang, Zishuo Li, Jianli Zhao, and Xianjun Pei. "A Prognostics Method Based on Back Propagation Neural Network for Corroded Pipelines." Micromachines 12, no. 12 (December 16, 2021): 1568. http://dx.doi.org/10.3390/mi12121568.
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A method that employs the back propagation (BP) neural network is used to predict the growth of corrosion defect in pipelines. This method considers more diversified parameters that affect the pipeline’s corrosion rate, including pipe parameters, service life, corrosion type, corrosion location, corrosion direction, and corrosion amount in a three-dimensional direction. The initial corrosion time is also considered, and, on this basis, the uncertainties of the initial corrosion time and the corrosion size are added to the BP neural network model. In this paper, three kinds of pipeline corrosion growth models are constructed: the traditional corrosion model, the corrosion model considering the uncertainties of initial corrosion time and corrosion depth, and corrosion model also considering the uncertainties of corrosion size (length, width, depth). The rationality and effectiveness of the proposed prediction models are verified by three case studies: the uniform model, the exponential model, and the gamma process model. The proposed models can be widely used in the prediction and management of pipeline corrosion.
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Cheng, Shan-Jen, Wen-Ken Li, Te-Jen Chang, and Chang-Hung Hsu. "Data-Driven Prognostics of the SOFC System Based on Dynamic Neural Network Models." Energies 14, no. 18 (September 15, 2021): 5841. http://dx.doi.org/10.3390/en14185841.
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Prognostics technology is important for the sustainability of solid oxide fuel cell (SOFC) system commercialization, i.e., through failure prevention, reliability assessment, and the remaining useful life (RUL) estimation. To solve SOFC system issues, data-driven prognostics methods based on the dynamic neural network (DNN), one of non-linear models, were investigated in this study. Based on DNN model types, the neural network autoregressive (NNARX) model with external inputs, the neural network autoregressive moving average (NNARMAX) model with external inputs, and the neural network output error (NNOE) were utilized to predict the degradation trend and estimate the RUL. First, the degradation trend prediction was executed to evaluate the correctness of the proposed DNN model structures in the first learning phase. Then, the RUL was estimated on the basis of the degradation trend of the NN models in the second inference phase. The comparison test results show the prediction accuracy of the NNARX model is higher and the RUL estimation can be given within a smaller relative error than the NNARMAX and NNOE models. The evaluation criteria of the root mean square error and mean absolute error of the NNARX model are the smallest among these three models. Therefore, the proposed NNARX model can effectively and precisely provide degradation trend prediction and RUL estimation of the SOFC system.
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Liu, Yumei, Ningguo Qiao, Congcong Zhao, Jiaojiao Zhuang, and Guangdong Tian. "Using the AR–SVR–CPSO hybrid model to forecast vibration signals in a high-speed train transmission system." Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 233, no. 7 (October 18, 2018): 701–14. http://dx.doi.org/10.1177/0954409718804908.
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Accurate vibration time series modeling can mine the internal law of data and provide valuable references for reliability assessment. To improve the prediction accuracy, this study proposes a hybrid model – called the AR–SVR–CPSO hybrid model – that combines the auto regression (AR) and support vector regression (SVR) models, with the weights optimized by the chaotic particle swarm optimization (CPSO) algorithm. First, the auto regression model with the difference method is employed to model the vibration time series. Second, the support vector regression model with the phase space reconstruction is constructed for predicting the vibration time series once more. Finally, the predictions of the AR and SVR models are weighted and summed together, with the weights being optimized by the CPSO. In addition, the data collected from the reliability test platform of high-speed train transmission systems and the “NASA prognostics data repository” are used to validate the hybrid model. The experimental results demonstrate that the hybrid model proposed in this study outperforms the traditional AR and SVR models.
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Khan, Faisal, Omer Eker, Atif Khan, and Wasim Orfali. "Adaptive Degradation Prognostic Reasoning by Particle Filter with a Neural Network Degradation Model for Turbofan Jet Engine." Data 3, no. 4 (November 6, 2018): 49. http://dx.doi.org/10.3390/data3040049.
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In the aerospace industry, every minute of downtime because of equipment failure impacts operations significantly. Therefore, efficient maintenance, repair and overhaul processes to aid maximum equipment availability are essential. However, scheduled maintenance is costly and does not track the degradation of the equipment which could result in unexpected failure of the equipment. Prognostic Health Management (PHM) provides techniques to monitor the precise degradation of the equipment along with cost-effective reliability. This article presents an adaptive data-driven prognostics reasoning approach. An engineering case study of Turbofan Jet Engine has been used to demonstrate the prognostic reasoning approach. The emphasis of this article is on an adaptive data-driven degradation model and how to improve the remaining useful life (RUL) prediction performance in condition monitoring of a Turbofan Jet Engine. The RUL prediction results show low prediction errors regardless of operating conditions, which contrasts with a conventional data-driven model (a non-parameterised Neural Network model) where prediction errors increase as operating conditions deviate from the nominal condition. In this article, the Neural Network has been used to build the Nominal model and Particle Filter has been used to track the present degradation along with degradation parameter.
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Su, Xiaohong, Shuai Wang, Michael Pecht, Peijun Ma, and Lingling Zhao. "Prognostics of lithium-ion batteries based on different dimensional state equations in the particle filtering method." Transactions of the Institute of Measurement and Control 39, no. 10 (April 22, 2016): 1537–46. http://dx.doi.org/10.1177/0142331216642836.
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Accurate prediction of the remaining useful life of lithium-ion batteries plays a significant role in various devices and many researchers have focused on lithium-ion battery reliability and prognosis. A particle filter (PF) is an effective filter for estimation and prediction of time series data where model structure is available. The prediction accuracy of a PF depends on two key factors: parameter initialization and the state equation. In this paper, parameters are estimated using a PF and two empirical exponential models, i.e. the exponential model and improved exponential model, are used to track the battery capacity degradation; each model uses a different state equation. Experiments were performed to compare prediction accuracy using the related parameters estimation model with that using the capacity decline model; this paper compares the effects of the different state equations on the lithium-ion battery remaining useful life prediction. The experimental results show the merits of the capacity decline model based on particle filtering. The capacity decline model PF is more suitable for estimating the battery capacity trend in the long term.
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Vernez, Simone Lucia, Yair Lotan, Shahrokh F. Shariat, Arthur I. Sagalowsky, Jacob B. Morgan, Jay D. Raman, Christopher G. Wood, et al. "Predictive models for improved prognostication and selection of neoadjuvant and adjuvant systemic chemotherapy in upper tract urothelial cell carcinoma." Journal of Clinical Oncology 34, no. 2_suppl (January 10, 2016): 456. http://dx.doi.org/10.1200/jco.2016.34.2_suppl.456.
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456 Background: Chemotherapy is still underutilized in management of upper tract urothelial carcinoma (UTUC). We created pre and post-operative predictive tools combining independent prognostics to guide selection of patients for neoadjuvant and adjuvant chemotherapy. Methods: From the UTUC collaboration database (1,453 patients who underwent radical nephroureterectomy [RNU] at 13 academic institutions); a preoperative predictive model was created using 659 patients in whom all preoperative prognostic variables were available and a post-operative model was created using 586 patients with non-metastatic/ high-grade UTU. After multivariable survival analyses, a backward step-down selection process was applied to create a preoperative nomogram. Internal validation was performed using 200 bootstrap resamples. For the postoperative model, TALL score was created based on the sum of the independent prognostic variables. Results: Preoperative model: Grade, architecture and location of the tumor were independently associated with nonorgan confined disease. A nomogram including these 3 variables achieved 76.6% accuracy in predicting nonorgan confined upper tract urothelial cancer. Postoperative model: TALL score (1-7) was the sum of T ( ≤ T1 = 1, T2 = 2, T3 = 3 and T4 = 4), A (papillary = 0 and sessile = 1), LVI (absent = 0 and present = 1) and L (lymphadenectomy = 0 and no lymphadenectomy = 1). Five-year disease-free survival (DFS) and cancer-specific survival (CSS) were stratified into four risk categories according to the TALL score: low (TALL 0-2; 86 % DFS and 90 % CSS), intermediate (TALL = 3; 71 % DFS and 75 % CSS), high (TALL = 4; 57 % DFS and 58 % CSS) and very high risk (TALL ≥ 5; 34 % DFS and 38 % CSS) using Kaplan-Meier survival analyses. TALL score was externally validated in a single-center cohort of 85 UTUC patients. Conclusions: We developed validated multivariable prognostic tools for prediction of locally advanced UTUC and oncological outcomes after RNU for UTUC. These prediction models can be used for patient counseling, selection for neoadjuvant/adjuvant systemic therapies and design of clinical trials.
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Boutrous, Khoury, Iury Bessa, Vicenç Puig, Fatiha Nejjari, and Reinaldo M. Palhares. "Data-driven Prognostics based on Evolving Fuzzy Degradation Models for Power Semiconductor Devices." PHM Society European Conference 7, no. 1 (June 29, 2022): 68–77. http://dx.doi.org/10.36001/phme.2022.v7i1.3338.
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The increasing application of power converter systems based on semiconductor devices such as Insulated-Gate Bipolar Transistors (IGBTs) has motivated the investigation of strategies for their prognostics and health management. However, physicsbased degradation modelling for semiconductors is usually complex and depends on uncertain parameters, which motivates the use of data-driven approaches. This paper addresses the problem of data-driven prognostics of IGBTs based on evolving fuzzy models learned from degradation data streams. The model depends on two classes of degradation features: one group of features that are very sensitive to the degradation stages is used as a premise variable of the fuzzy model, and another group that provides good trendability and monotonicity is used for the auto-regressive consequent of the fuzzy model for degradation prediction. This strategy allows obtaining interpretable degradation models, which are improved when more degradation data is obtained from the Unit Under Test (UUT) in real time. Furthermore, the fuzzy-based Remaining Useful Life (RUL) prediction is equipped with an uncertainty quantification mechanism to better aid decisionmakers. The proposed approach is then used for the RUL prediction considering an accelerated aging IGBT dataset from the NASA Ames Research Center.
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Zhu, Hongmin. "Real-Time Prognostics of Engineered Systems under Time Varying External Conditions Based on the COX PHM and VARX Hybrid Approach." Sensors 21, no. 5 (March 2, 2021): 1712. http://dx.doi.org/10.3390/s21051712.
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In spite of the development of the Prognostics and Health Management (PHM) during past decades, the reliability prognostics of engineered systems under time-varying external conditions still remains a challenge in such a field. When considering the challenge mentioned above, a hybrid method for predicting the reliability index and the Remaining Useful Life (RUL) of engineered systems under time-varying external conditions is proposed in this paper. The proposed method is competent in reflecting the influence of time-varying external conditions on the degradation behaviour of engineered systems. Based on a subset of the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset as case studies, the Cox Proportional Hazards Model (Cox PHM) with time-varying covariates is utilised to generate the reliability indices of individual turbofan units. Afterwards, a Vector Autoregressive model with Exogenous variables (VARX) combined with pairwise Conditional Granger Causality (CGC) tests for sensor selections is defined to model the time-varying influence of sensor signals on the reliability indices of different units that have been previously generated by the Cox PHM with time-varying covariates. During the reliability prediction, the Fourier Grey Model (FGM) is employed with the time series models for long-term forecasting of the external conditions. The results show that the method that is proposed in this paper is competent for the RUL prediction as compared with baseline approaches.
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Li, Hongru, Zaike Tian, He Yu, and Baohua Xu. "Fault Prognosis of Hydraulic Pump Based on Bispectrum Entropy and Deep Belief Network." Measurement Science Review 19, no. 5 (October 1, 2019): 195–203. http://dx.doi.org/10.2478/msr-2019-0025.
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Abstract Fault prognosis plays a key role in the framework of Condition-Based Maintenance (CBM). Limited by the inherent disadvantages, most traditional intelligent algorithms perform not very well in fault prognosis of hydraulic pumps. In order to improve the prediction accuracy, a novel methodology for fault prognosis of hydraulic pump based on the bispectrum entropy and the deep belief network is proposed in this paper. Firstly, the bispectrum features of vibration signals are analyzed, and a bispectrum entropy method based on energy distribution is proposed to extract the effective feature for prognostics. Then, the Deep Belief Network (DBN) model based on the Restrict Boltzmann Machine (RBM) is proposed as the prognostics model. For the purpose of accurately predicting the trends and the random fluctuations during the performance degradation of the hydraulic pump, the Quantum Particle Swarm Optimization (QPSO) is introduced to search for the optimal value of initial parameters of the network. Finally, analysis of the hydraulic pump degradation experiment demonstrates that the proposed algorithm has a satisfactory prognostics performance and is feasible to meet the requirements of CBM.
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Khumprom, Phattara, David Grewell, and Nita Yodo. "Deep Neural Network Feature Selection Approaches for Data-Driven Prognostic Model of Aircraft Engines." Aerospace 7, no. 9 (September 4, 2020): 132. http://dx.doi.org/10.3390/aerospace7090132.
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Predicting Remaining Useful Life (RUL) of systems has played an important role in various fields of reliability engineering analysis, including in aircraft engines. RUL prediction is critically an important part of Prognostics and Health Management (PHM), which is the reliability science that is aimed at increasing the reliability of the system and, in turn, reducing the maintenance cost. The majority of the PHM models proposed during the past few years have shown a significant increase in the amount of data-driven deployments. While more complex data-driven models are often associated with higher accuracy, there is a corresponding need to reduce model complexity. One possible way to reduce the complexity of the model is to use the features (attributes or variables) selection and dimensionality reduction methods prior to the model training process. In this work, the effectiveness of multiple filter and wrapper feature selection methods (correlation analysis, relief forward/backward selection, and others), along with Principal Component Analysis (PCA) as a dimensionality reduction method, was investigated. A basis algorithm of deep learning, Feedforward Artificial Neural Network (FFNN), was used as a benchmark modeling algorithm. All those approaches can also be applied to the prognostics of an aircraft gas turbine engines. In this paper, the aircraft gas turbine engines data from NASA Ames prognostics data repository was used to test the effectiveness of the filter and wrapper feature selection methods not only for the vanilla FFNN model but also for Deep Neural Network (DNN) model. The findings show that applying feature selection methods helps to improve overall model accuracy and significantly reduced the complexity of the models.
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Verstraete, David, Enrique Droguett, and Mohammad Modarres. "A Deep Adversarial Approach Based on Multi-Sensor Fusion for Semi-Supervised Remaining Useful Life Prognostics." Sensors 20, no. 1 (December 27, 2019): 176. http://dx.doi.org/10.3390/s20010176.
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Multi-sensor systems are proliferating in the asset management industry. Industry 4.0, combined with the Internet of Things (IoT), has ushered in the requirements of prognostics and health management systems to predict the system’s reliability and assess maintenance decisions. State of the art systems now generate big machinery data and require multi-sensor fusion for integrated remaining useful life prognostic capabilities. When dealing with these data sets, traditional prediction methods are not equipped to handle the multiple sensor signals in unison. To address this challenge, this paper proposes a new, deep, adversarial approach to any remaining useful life prediction in which a novel, non-Markovian, variational, inference-based model, incorporating an adversarial methodology, is derived. To evaluate the proposed approach, two public multi-sensor data sets are used for the remaining useful life prediction applications: (1) CMAPSS turbofan engine dataset, and (2) FEMTO Pronostia rolling element bearing data set. The proposed approach obtains favorable results when against similar deep learning models.
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Wang, Jiwei, Zhongwei Deng, Kaile Peng, Xinchen Deng, Lijun Xu, Guoqing Guan, and Abuliti Abudula. "Early Prognostics of Lithium-Ion Battery Pack Health." Sustainability 14, no. 4 (February 17, 2022): 2313. http://dx.doi.org/10.3390/su14042313.
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Accurate health prognostics of lithium-ion battery packs play a crucial role in timely maintenance and avoiding potential safety accidents in energy storage. To rapidly evaluate the health of newly developed battery packs, a method for predicting the future health of the battery pack using the aging data of the battery cells for their entire lifecycles and with the early cycling data of the battery pack is proposed. Firstly, health indicators (HIs) are extracted from the experimental data, and high correlations between the extracted HIs and the capacity are verified by the Pearson correlation analysis method. To predict the future health of the battery pack based on the HIs, degradation models of HIs are constructed by using an exponential function, long short-term memory network, and their weighted fusion. The future HIs of the battery pack are predicted according to the fusion degradation model. Then, based on the Gaussian process regression algorithm and battery pack data, a data-driven model is constructed to predict the health of the battery pack. Finally, the proposed method is validated with a series-connected battery pack with fifteen 100 Ah lithium iron phosphate battery cells. The mean absolute error and root mean square error of the health prediction of the battery pack are 7.17% and 7.81%, respectively, indicating that the proposed method has satisfactory accuracy.
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Dong, Jiankang, Jiaqi Wang, and Runxia Guo. "Remaining useful life prognostics for the electro-hydraulic actuator using relevance vector machine and optimized on-line incremental learning." MATEC Web of Conferences 277 (2019): 02009. http://dx.doi.org/10.1051/matecconf/201927702009.
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The electro-hydraulic actuator plays a significant role in the automatic flight control system, so it is vital to predict the remaining useful life (RUL) for the electro-hydraulic actuators. Relevance vector machine (RVM) is flourishing in the field of RUL prognostics and gradually applied to the prediction of complex systems or components, but the general RVM cannot achieve on-line prediction efficiently due to its high computational complexity, besides, the sparse RVM model which is only based on historical data set could cause a large prediction error in the long term. To deal with these plights, an optimized incremental learning algorithm based on RVM is presented taking full advantage of the on-line updating samples to improve the precision of prognostics.
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Ding, Gang, Da Lei, and Wei Yao. "Health Condition Prognostics of Complex Equipment Based on Discrete Input Process Neural Networks." Applied Mechanics and Materials 423-426 (September 2013): 2347–54. http://dx.doi.org/10.4028/www.scientific.net/amm.423-426.2347.
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Considering the problem of health condition prognostics of complex equipment, a discrete input process neural networks (DPNN) model based on process neural networks (PNN) is proposed in this paper. DPNN utilizes vector inputs together with convolution operator to gain the capability of time and spatial aggregation operation, which is implemented with continuous function inputs and integral operator by PNN. Different from PNN, DPNN can use discrete samples as inputs directly, thus can avoid precision loss during procedures of data fitting and function expanding required by PNN. The application of DPNN to health condition prognostics of complex equipment is described through the prediction of the future health state of the civil aircraft engines, where the short-term and long-term predictions of the health condition represented by the exhausted gas temperature time series are conducted. Moreover, the performance of DPNN is compared with common artificial neural networks (NN) and PNN. The results show that DPNN has satisfied performance for health condition prognostics of civil aircraft engines, and DPNN performs better than both NN and PNN, which prove that DPNN is suitable for health condition prognostics of complex equipment.
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Bernardino, Lucas Ferreira, André Felipe Ferreira de Souza, Argimiro Resende Secchi, Maurício Bezerra de Souza Jr., and Anne Barros. "Integration of Prognostics and Control of an Oil/CO2 Subsea Separation System." Processes 8, no. 2 (January 23, 2020): 148. http://dx.doi.org/10.3390/pr8020148.
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The exploitation of reserves with a high CO2 content is challenging because of the need for its separation and the environmental impact associated with its generation. In this context, a suitable use for the generated CO2 is its reinjection into the reservoir, and subsea CO2 separation improves the efficiency of this process. The main objective of this work is to investigate the health-aware control of a subsea CO2 separation system. Previously identified linear models were used in a predictive controller with Kalman filter-based state estimation and online model update, and simulations were performed to evaluate the controller tuning. Regarding prognostics, a stochastic model of pump degradation, sensitive to its operating conditions, was proposed, and a particle filter was implemented to perform online degradation state estimation and remaining useful lifetime prediction. Finally, a health-aware controller was designed, which could extend the life of the process by four months when compared to operation with a conventional model predictive controller. Some difficulties in combining reference tracking and lifetime extension objectives were also investigated. The obtained results indicate that dealing with the control problem through the multiobjective optimization theory or addressing the lifetime extension in an optimization layer may improve its performance.
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Li, Mingfei, Jiajian Wu, Zhengpeng Chen, Jiangbo Dong, Zhiping Peng, Kai Xiong, Mumin Rao, Chuangting Chen, and Xi Li. "Data-Driven Voltage Prognostic for Solid Oxide Fuel Cell System Based on Deep Learning." Energies 15, no. 17 (August 29, 2022): 6294. http://dx.doi.org/10.3390/en15176294.
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A solid oxide fuel cell (SOFC) is an innovative power generation system that is green, efficient, and promising for a wide range of applications. The prediction and evaluation of the operation state of a solid oxide fuel cell system is of great significance for the stable and long-term operation of the power generation system. Prognostics and Health Management (PHM) technology is widely used to perform preventive and predictive maintenance on equipment. Unlike prediction based on the SOFC mechanistic model, the combination of PHM and deep learning has shown wide application prospects. Therefore, this study first obtains an experimental dataset through short-term degradation experiments of a 1 kW SOFC system, and then proposes an encoder-decoder RNN-based SOFC state prediction model. Based on the experimental dataset, the model can accurately predict the voltage variation of the SOFC system. The prediction results of the four different prediction models developed are compared and analyzed, namely, long short-term memory (LSTM), gated recurrent unit (GRU), encoder–decoder LSTM, and encoder–decoder GRU. The results show that for the SOFC test set, the mean square error of encoder–decoder LSTM and encoder–decoder GRU are 0.015121 and 0.014966, respectively, whereas the corresponding error results of LSTM and GRU are 0.017050 and 0.017456, respectively. The encoder–decoder RNN model displays high prediction precision, which proves that it can improve the accuracy of prediction, which is expected to be combined with control strategies and further help the implementation of PHM in fuel cells.
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Tseng, Fling, Dimitar Filev, Murat Yildirim, and Ratna Babu Chinnam. "Online System Prognostics with Ensemble Models and Evolving Clustering." Machines 11, no. 1 (December 29, 2022): 40. http://dx.doi.org/10.3390/machines11010040.
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An online evolving clustering (OEC) method equivalent to ensemble modeling is proposed to tackle prognostics problems of learning and the prediction of remaining useful life (RUL). During the learning phase, OEC extracts predominant operating modes as multiple evolving clusters (EC). Each EC is associated with its own Weibull distribution-inspired degradation (survivability) model that will receive incremental online modifications as degradation signals become available. Example case studies from machining (drilling) and automotive brake-pad wear prognostics are used to validate the effectiveness of the proposed method.
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Blair, Jennifer, Bruce Stephen, Blair Brown, Alistair Forbes, and Stephen Mcarthur. "Hybrid Fault Prognostics for Nuclear Applications: Addressing Rotating Plant Model Uncertainty." PHM Society European Conference 7, no. 1 (June 29, 2022): 58–67. http://dx.doi.org/10.36001/phme.2022.v7i1.3321.
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Nuclear plant operators are required to understand the uncertainties associated with the deployment of prognostics toolsin order to justify their inclusion in operational decision making processes and satisfy regulatory requirements. Operationaluncertainty can cause underlying prognostics models to underperform on assets that are subject to evolving impactsof age, manufacturing tolerances, operating conditions, and operating environment effects, of which may be capturedthrough a condition monitoring (CM) system that itself may be degraded. Sources of uncertainty in the data acquisitionpipeline can impact the health of CM data used to estimate the remaining useful life (RUL) of assets. These uncertaintiescan disguise or misrepresent developing faults, where (for example) the fault identification is not achieved until it hasprogressed to an unmanageable state. This leaves little flexibility for the operator’s maintenance decisions and generallyundermines model confidence.
One method to quantify and account for operational uncertainty is calibrated hybrid models, employing physics, knowledgeor data driven methods to improve model accuracy and robustness. Hybrid models allow known physical relations tooffset full reliance on potentially untrustworthy data, whilst reducing the need for an abundance of representative historicaldata to reliably identify the monitored asset’s underlying behavioural trends. Calibration of the model then ensuresthe model is updated and representative of the real monitored asset by accounting for differences between the physics orknowledge model and CM data.
In this paper, an open-source bearing knowledge informed machine learning (ML) model and CM datasets are utilizedin an illustrative bearing prognostic application. The uncertainty incurred by the decisions made at key stages in thedevelopment of the model’s data acquisition and processing pipeline are assessed and demonstrated by the resultant impacton RUL prediction performance. It was shown that design decisions could result in multiple valid pipeline designswhich generated different predicted RUL trajectories, increasing the uncertainty in the model output.
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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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Dong, Han Cheng, San Tong Zhang, Qing Hua Li, and Chang Hong Wang. "A New Approach to Battery Capacity Prediction Based on Hybrid ARMA and ANN Model." Applied Mechanics and Materials 190-191 (July 2012): 241–44. http://dx.doi.org/10.4028/www.scientific.net/amm.190-191.241.
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Research on battery prognostics and health monitoring (PHM) is important in many engineering areas, and battery capacity is a good indicator of battery condition, this paper introduces a new approach to predict battery capacity use hybrid ARMA and ANN model. First, two time series forecast models ARMA and ANN are introduced, since these two models have their own shortcoming in forecasting nonlinear and linear time series respectively, hybrid ARMA and ANN model are established in order to combine both advantage of the two and get more precise prediction result. Then capacity data applied in this paper is described, prediction results and errors based on these data and among three models are compared. At last, the conclusion that hybrid model shows the best performance and will provide a new approach to realize battery capacity predictor is given.
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Khumprom, Phattara, and Nita Yodo. "A Data-Driven Predictive Prognostic Model for Lithium-Ion Batteries based on a Deep Learning Algorithm." Energies 12, no. 4 (February 18, 2019): 660. http://dx.doi.org/10.3390/en12040660.
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Prognostic and health management (PHM) can ensure that a lithium-ion battery is working safely and reliably. The main approach of PHM evaluation of the battery is to determine the State of Health (SoH) and the Remaining Useful Life (RUL) of the battery. The advancements of computational tools and big data algorithms have led to a new era of data-driven predictive analysis approaches, using machine learning algorithms. This paper presents the preliminary development of the data-driven prognostic, using a Deep Neural Networks (DNN) approach to predict the SoH and the RUL of the lithium-ion battery. The effectiveness of the proposed approach was implemented in a case study with a battery dataset obtained from the National Aeronautics and Space Administration (NASA) Ames Prognostics Center of Excellence (PCoE) database. The proposed DNN algorithm was compared against other machine learning algorithms, namely, Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), Artificial Neural Networks (ANN), and Linear Regression (LR). The experimental results reveal that the performance of the DNN algorithm could either match or outweigh other machine learning algorithms. Further, the presented results could serve as a benchmark of SoH and RUL prediction using machine learning approaches specifically for lithium-ion batteries application.
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Tsoutsanis, Elias, Yi-Guang Li, Pericles Pilidis, and Mike Newby. "Non-linear model calibration for off-design performance prediction of gas turbines with experimental data." Aeronautical Journal 121, no. 1245 (September 18, 2017): 1758–77. http://dx.doi.org/10.1017/aer.2017.96.
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ABSTRACTOne of the key challenges of the gas turbine community is to empower the condition based maintenance with simulation, diagnostic and prognostic tools which improve the reliability and availability of the engines. Within this context, the inverse adaptive modelling methods have generated much attention for their capability to tune engine models for matching experimental test data and/or simulation data. In this study, an integrated performance adaptation system for estimating the steady-state off-design performance of gas turbines is presented. In the system, a novel method for compressor map generation and a genetic algorithm-based method for engine off-design performance adaptation are introduced. The methods are integrated into PYTHIA gas turbine simulation software, developed at Cranfield University and tested with experimental data of an aero derivative gas turbine. The results demonstrate the promising capabilities of the proposed system for accurate prediction of the gas turbine performance. This is achieved by matching simultaneously a set of multiple off-design operating points. It is proven that the proposed methods and the system have the capability to progressively update and refine gas turbine performance models with improved accuracy, which is crucial for model-based gas path diagnostics and prognostics.
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Figueroa Barraza, Joaquín, Enrique López Droguett, and Marcelo Ramos Martins. "Towards Interpretable Deep Learning: A Feature Selection Framework for Prognostics and Health Management Using Deep Neural Networks." Sensors 21, no. 17 (September 1, 2021): 5888. http://dx.doi.org/10.3390/s21175888.
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In the last five years, the inclusion of Deep Learning algorithms in prognostics and health management (PHM) has led to a performance increase in diagnostics, prognostics, and anomaly detection. However, the lack of interpretability of these models results in resistance towards their deployment. Deep Learning-based models fall within the accuracy/interpretability tradeoff, which means that their complexity leads to high performance levels but lacks interpretability. This work aims at addressing this tradeoff by proposing a technique for feature selection embedded in deep neural networks that uses a feature selection (FS) layer trained with the rest of the network to evaluate the input features’ importance. The importance values are used to determine which will be considered for deployment of a PHM model. For comparison with other techniques, this paper introduces a new metric called ranking quality score (RQS), that measures how performance evolves while following the corresponding ranking. The proposed framework is exemplified with three case studies involving health state diagnostics and prognostics and remaining useful life prediction. Results show that the proposed technique achieves higher RQS than the compared techniques, while maintaining the same performance level when compared to the same model but without an FS layer.
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Li, Ni, and Zhen Hua Li. "The Fault Prediction of Aerospace Equipment PHM Technology and its Demonstrated Failure Prediction Module Simulation." Advanced Materials Research 505 (April 2012): 239–44. http://dx.doi.org/10.4028/www.scientific.net/amr.505.239.
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Prognostics and Health Management Technology (PHM) will make up the deficiencies of current device monitoring and fault diagnosis, especially the lack of fault prediction. Its combination with the neural network can provide a universal theory and technology of the intelligence prediction. Though the experiments, we establish the BP neural network, as well as the most suitable prediction model. After testing data verification, neural networks can accurately predict the status of the equipment, and the health trends in the future. With the network we can accurately predict the system state, remaining life for the aerospace equipment, make it possible to provide maintenance in time, reduce failure losses and improve reliability.
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Maulana, Faisal, Andrew Starr, and Agusmian Partogi Ompusunggu. "Explainable Data-Driven Method Combined with Bayesian Filtering for Remaining Useful Lifetime Prediction of Aircraft Engines Using NASA CMAPSS Datasets." Machines 11, no. 2 (January 24, 2023): 163. http://dx.doi.org/10.3390/machines11020163.
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An aircraft engine is expected to have a high-reliability system as a safety-critical asset. A scheduled maintenance strategy based on statistical calculation has been employed as the current practice to achieve the reliability requirement. Any improvement to this maintenance interval is made after significant reliability issues arise (such as flight delays and high component removals). Several publications and research studies have been conducted related to this issue, one of them involves performing simulations and providing aircraft operation datasets. The recently published NASA CMAPPS datasets have been utilised in this paper since they simulate flight data recording from various measurements. A prognostics model can be developed by analysing these datasets and predicting the engine’s reliability before failure. However, the state-of-the-art prognostics techniques published in the literature using these NASA CMAPPS datasets are mainly purely data-driven. These techniques mainly deal with a “black box” process which does not include uncertainty quantification (UQ). These two factors are barriers to prognostics applications, particularly in the aviation industry. To tackle these issues, this paper aims at developing explainable and transparent algorithms and a software tool to compute the engine health, estimate engine end of life (EoL), and eventually predict its remaining useful life (RUL). The proposed algorithms use hybrid metrics for feature selection, employ logistic regression for health index estimation, and unscented Kalman filter (UKF) to update the prognostics model for predicting the RUL in a recursive fashion. Among the available datasets, dataset 02 is chosen because it has been widely used and is an ideal candidate for result comparison and dataset 03 is employed as a new state-of-the-art. As a result, the proposed algorithms yield 34.5–55.6% better performance in terms of the root mean squared error (RMSE) compared with the previous work. More importantly, the proposed method is transparent and it quantifies the uncertainty during the prediction process.
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Laddada, S., T. Benkedjouh, M. O. Si- Chaib, and R. DRAI. "Remaining useful life prediction of cutting tools using wavelet packet transform and extreme learning machine." Algerian Journal of Signals and Systems 3, no. 4 (December 15, 2018): 156–65. http://dx.doi.org/10.51485/ajss.v3i4.72.
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Online tool wear prediction is a determining factor to the success of smart manufacturing operations. The implementation of sensors based Prognostic and Health Management (PHM) system plays an important role in estimating Remaining Useful Life (RUL) of cutting tools and optimizing the usage of Computer Numerically Controlled (CNC) machines. The present paper deals with health assessment and RUL estimation of the cutting tool machines based on Wavelet Packet Transform (WPT) and Extreme Learning Machine (ELM). This approach is done in two phases: a learning (offline) phase and a testing (online) phase. During the first phase, the WPT is used to extract the relevant features of raw data computed in the form of nodes energy. The extracted features are then fed to the learning algorithm ELM in order to build an offline model. In the online phase, the constructed model is exploited for assessing and predicting the RUL of cutting tool. The main idea is that ELM involves nonlinear regression in a high dimensional feature space for mapping the input data via a nonlinear function to build a prognostics model. The method was applied to real world data gathered during several cuts of a milling CNC tool. The performance of the proposed method is evaluated through the accuracy metric. Results showed the significance performances achieved by the WPT and ELM for early detection and accurate prediction of the monitored cutting tools.
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Hansen, R. J., D. L. Hall, and S. K. Kurtz. "A New Approach to the Challenge of Machinery Prognostics." Journal of Engineering for Gas Turbines and Power 117, no. 2 (April 1, 1995): 320–25. http://dx.doi.org/10.1115/1.2814097.
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Current generation mechanical diagnostic equipment is designed to identify individual events or trends in the output of sensors mounted on a mechanical component, subsystem, or system. Such equipment can provide a useful indication that a failure condition may be developing, but it cannot provide reliable predictions of the remaining safe or operational life. Typically, these diagnostic systems simply compare the output of individual sensors against a priori thresholds to establish a measure of the system’s health. Two problems result from this approach: (1) There is no advantage taken of possible synergy among the sensors, i.e., the determination of health is one dimensional; and (2) the diagnosis provides only a statement regarding the current equipment health, but does not provide a prediction of the time remaining to failure. This often leads to an operational environment in which diagnostic equipment outputs are either ignored because of frequent false alarms or frequent (and costly) time-based preventive maintenance is performed to avoid hazardous failures. This paper describes a new approach to the development of a more robust diagnosis and prognostic capability. It is based on the fusion of sensor-based and model-based information. Sensor-based information is the essence of current diagnostic systems. Model-based information combines dynamic models of individual mechanical components with micromechanical models of relevant failure mechanisms, such as fracture and crack propagation. These micromechanical models must account for initial flaw size distribution and other microstructural parameters describing initial components condition. A specific application of this approach is addressed, the diagnosis of mechanical failure in meshing gears. Four specific issues are considered: (a) how to couple a validated numerical simulation of gear transmission error (due to tooth spacing irregularity, contour irregularity, or material inhomogeneity) with physically and empirically based descriptions of fatigue crack growth to predict a failure precursor signature at the component level; (b) how to predict the manifestation of this signature at the subsystem or system level where sensors are located; (c) how to fuse this model-based information with the corresponding sensor-based information to predict remaining safe or operational life of a gear; and (d) issues associated with extending this methodology to bearings and other rotating machinery components.
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Liu, Chenyu, Alexandre Mauricio, Junyu Qi, Dandan Peng, and Konstantinos Gryllias. "Domain Adaptation Digital Twin for Rolling Element Bearing Prognostics." Annual Conference of the PHM Society 12, no. 1 (November 3, 2020): 10. http://dx.doi.org/10.36001/phmconf.2020.v12i1.1294.
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Artificial Intelligence (AI) is escalating in data-driven condition monitoring research. Traditional expert knowledge-based Prognostics and Health Management (PHM) processes can be smartened up with the assistance of various AI techniques, such as deep learning models. On the other hand, current deep learning based prognostics suffers from the data deficit issue, especially considering the varying operating conditions and the degradation modes of the components in practical industrial applications. With the development of simulation techniques, physical-knowledge based digital twin models give engineers access to a large amount of simulation data at a lower cost. These simulation data contain the physical characteristics and the degradation information of the component. In order to accurately predict the Remaining Useful Life (RUL) during the degradation process, in this paper, a bearing digital twin model is constructed based on a phenomenological vibration model. A Domain Adversarial Neural Network (DANN) is used to achieve the domain adaptation target between the simulation and the real data. Regarding the simulation data as the source domain and real data as the target domain, the DANN model is able to predict the RUL without any priori knowledge of the labelling information. Based on real bearing run-to-failure experiments, the performance of the proposed method is validated with high RUL prediction accuracy.
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Ghavami, Peter, and Kailash Kapur. "Prognostics and Prediction of Patient Health Status Using a Multi-Model Artificial Intelligence Framework." Public Health Frontier 2, no. 2 (June 26, 2013): 46–60. http://dx.doi.org/10.5963/phf0202001.
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Hagmeyer, Simon, Peter Zeiler, and Marco F. Huber. "On the Integration of Fundamental Knowledge about Degradation Processes into Data-Driven Diagnostics and Prognostics Using Theory-Guided Data Science." PHM Society European Conference 7, no. 1 (June 29, 2022): 156–65. http://dx.doi.org/10.36001/phme.2022.v7i1.3352.
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In Prognostics and Health Management, there are three main approaches for implementing diagnostic and prognostic applications. These approaches are data-driven methods, physical model-based methods, and combinations of them, in the form of hybrid methods. Each of them has specific advantages but also limitations for their purposeful implementation. In the case of data-driven methods, one of the main limitations is the availability of sufficient training data that adequately cover the relevant state space. For model-based methods, on the other hand, it is often the case that the degradation process of the considered technical system is of significant complexity. In such a scenario physics-based modeling requires great effort or is not possible at all. Combinations of data-driven and model-based approaches in form of hybrid approaches offer the possibility to partially mitigate the shortcomings of the other two approaches, however, require a sufficiently detailed data-driven and physics-based model. This paper addresses the transitional field between data-driven and hybrid approaches. Despite the issues of formulating a physics-based model that provides a representation of the degradation process, basic knowledge of the considered system and of the laws governing its degradation process is usually available. Integration of such knowledge into a machine learning process is part of a research field that is either called theory-guided data science, (physics) informed machine learning, physics-based learning or physics guided machine learning. First, the state of research in Prognostics and Health Management on methods of this field is presented and existing research gaps are outlined. Then, a concept is introduced for incorporating fundamental knowledge, such as monotonicity constraints, into data-driven diagnostic and prognostic applications using approaches from theory-guided data science. A special aspect of this concept is its cross-application usability through the consideration of knowledge that repeatedly occurs in diagnostics and prognostics. This is, for example, knowledge about physically justified boundaries whose compliance makes a prediction of the data-driven model plausible in the first place.
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Kong, Zhengmin, Yande Cui, Zhou Xia, and He Lv. "Convolution and Long Short-Term Memory Hybrid Deep Neural Networks for Remaining Useful Life Prognostics." Applied Sciences 9, no. 19 (October 3, 2019): 4156. http://dx.doi.org/10.3390/app9194156.
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Reliable prediction of remaining useful life (RUL) plays an indispensable role in prognostics and health management (PHM) by reason of the increasing safety requirements of industrial equipment. Meanwhile, data-driven methods in RUL prognostics have attracted widespread interest. Deep learning as a promising data-driven method has been developed to predict RUL due to its ability to deal with abundant complex data. In this paper, a novel scheme based on a health indicator (HI) and a hybrid deep neural network (DNN) model is proposed to predict RUL by analyzing equipment degradation. Explicitly, HI obtained by polynomial regression is combined with a convolutional neural network (CNN) and long short-term memory (LSTM) neural network to extract spatial and temporal features for efficacious prognostics. More specifically, valid data selected from the raw sensor data are transformed into a one-dimensional HI at first. Next, both the preselected data and HI are sequentially fed into the CNN layer and LSTM layer in order to extract high-level spatial features and long-term temporal dependency features. Furthermore, a fully connected neural network is employed to achieve a regression model of RUL prognostics. Lastly, validated with the aid of numerical and graphic results by an equipment RUL dataset from the Commercial Modular Aero-Propulsion System Simulation(C-MAPSS), the proposed scheme turns out to be superior to four existing models regarding accuracy and effectiveness.
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Zhao, Yuntian, Maxwell Toothman, James Moyne, and Kira Barton. "An Adaptive Modeling Framework for Bearing Failure Prediction." Electronics 11, no. 2 (January 14, 2022): 257. http://dx.doi.org/10.3390/electronics11020257.
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Rolling element bearings are a common component in rotating equipment, a class of machines that is essential in a wide range of industries. Detecting and predicting bearing failures is then vital for reducing maintenance and production costs due to unplanned downtime. In previous literature, significant efforts have been devoted to building data-driven health models from historical bearing data. However, a common limitation is that these methods are typically tailored to specific failure instances and have limited ability to model bearing failures between repairs in the same system. In this paper, we propose a multi-state health model to predict bearing failures before they occur. The model employs a regression-based method to detect health state transition points and applies an exponential random coefficient model with a Bayesian updating process to estimate time-to-failure distributions. A model training framework is also introduced to make our proposed model applicable to more bearing instances in the same system setting. The proposed method has been tested on a publicly available bearing prognostics dataset. Case study results show that the proposed method provides accurate failure predictions across several system failures, and that the training approach can significantly reduce the time necessary to generate an effective, generalized model.
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Wang, Yi, Ming Qing Xiao, and Jia Yong Fang. "Integrate Uncertainty in the Process of Prognostics for Electronics." Applied Mechanics and Materials 69 (July 2011): 132–37. http://dx.doi.org/10.4028/www.scientific.net/amm.69.132.
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Elements of uncertainty in the electronics Prognostics process were studied. A method for electronics Dynamic Damage Optimal Estimation and prognostics based on Particle Filtering were proposed. Under the effect of time stress, the electronics cumulative damage is the result of the continuous effect of the stress, as a result, a HMM based electronics dynamic damage model was built at first place, analytical results of uncertainties in the process of prognostics were given and thus a Bayesian based filter system was built. Bayesian Filter change the problem of uncertainty into an optimal estimation processes as a result, the optimal estimation was fetched by applying the particle filtering into the estimation. The experiment case study proved that the proposed method can eliminate the uncertainties caused by measurement and the system effectively and improve the RUL prediction accuracy.
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Ding, Fangfang, and Zhigang Tian. "Integrated Prognosis for Wind Turbine Gearbox Condition-Based Maintenance Considering Time-Varying Load and Crack Initiation Time Uncertainty." International Journal of Reliability, Quality and Safety Engineering 28, no. 04 (February 23, 2021): 2150024. http://dx.doi.org/10.1142/s0218539321500248.
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Maintenance management in wind energy industry has great impact on the overall wind power cost. Maintenance services are either supported by wind turbine manufacturers within warranty period, or managed by wind farm owners. With condition-based maintenance (CBM) strategy, maintenance activities are scheduled based on the predicted health conditions of wind turbine components, and accurate prognostics methods are critical for effective CBM. The reported studies on integrated health prognostics considered the uncertainty in crack initiation time (CIT) uncertainty, but did not incorporate time-varying loading conditions, which could also have a significant impact on future health condition and remaining useful life (RUL) prediction. Constant loads were generally used to approximate the actual time-varying loading conditions. In this paper, an integrated prognostics method is proposed for wind turbine gearboxes considering both time-varying loading conditions and CIT uncertainty. As new condition monitoring observations are available, the distributions of both material model parameter and CIT are updated via Bayesian inference, and the failure time prediction is updated accordingly. An example is provided to demonstrate that the proposed time-varying load approach presents more benefits considering the uncertainty of CIT, with significant accuracy improvement comparing to the constant-load approach.
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Zhou, Jianmin, Sen Gao, Jiahui Li, and Wenhao Xiong. "Bearing Life Prediction Method Based on Parallel Multichannel Recurrent Convolutional Neural Network." Shock and Vibration 2021 (October 13, 2021): 1–9. http://dx.doi.org/10.1155/2021/6142975.
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To extract the time-series characteristics of the original bearing signals and predict the remaining useful life (RUL) more effectively, a parallel multichannel recurrent convolutional neural network (PMCRCNN) is proposed for the prediction of RUL. Firstly, the time domain, frequency domain, and time-frequency domain features are extracted from the original signal. Then, the PMCRCNN model is constructed. The front of the model is the parallel multichannel convolution unit to learn and integrate the global and local features from the time-series data. The back of the model is the recurrent convolution layer to model the temporal dependence relationship under different degradation features. Normalized life values are used as labels to train the prediction model. Finally, the RUL was predicted by the trained neural network. The proposed method is verified by full life tests of bearing. The comparison with the existing prognostics approaches of convolutional neural network (CNN) and the recurrent convolutional neural network (RCNN) models proves that the proposed method (PMCRCNN) is effective and superior in improving the accuracy of RUL prediction.
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Shoriat Ullah, MD, and Kangwon Seo. "Prediction of Lithium-Ion Battery Capacity by Functional Principal Component Analysis of Monitoring Data." Applied Sciences 12, no. 9 (April 24, 2022): 4296. http://dx.doi.org/10.3390/app12094296.
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The lithium-ion (Li-ion) battery is a promising energy storage technology for electronics, automobiles, and smart grids. Extensive research was conducted in the past to improve the prediction of the remaining capacity of the Li-ion battery. A robust prediction model would improve the battery performance and reliability for forthcoming usage. In the development of a data-driven capacity prediction model of Li-ion batteries, most past studies employed capacity degradation data; however, very few tried using other performance monitoring variables, such as temperature, voltage, and current data, to estimate and predict the battery capacity. In this study, we aimed to develop a data-driven model for predicting the capacity of Li-ion batteries adopting functional principal component analysis (fPCA) applied to functional monitoring data of temperature, voltage, and current observations. The proposed method is demonstrated using the battery monitoring data available in the NASA Ames Prognostics Center of Excellence repository. The main contribution of the study the development of an empirical data-driven model to diagnose the state-of-health (SOH) of Li-ion batteries based on the health monitoring data utilizing fPCA and LASSO regression. The study obtained encouraging battery capacity prediction performance by explaining overall variation through eigenfunctions of available monitored discharge parameters of Li-ion batteries. The result of capacity prediction obtained a root mean square error (RMSE) of 0.009. The proposed data-driven approach performs well for predicting the capacity by employing functional performance measures over the life span of a Li-ion battery.
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Huang, Run Qing, Li Feng Xi, C. Richard Liu, and Jay Lee. "Prognostics for Ball Bearing Based on Neural Networks and Morlet Wavelet." Materials Science Forum 505-507 (January 2006): 1153–58. http://dx.doi.org/10.4028/www.scientific.net/msf.505-507.1153.
Full textAbstract:
This paper deals with a new scheme for prognostics of ball bearing based on Self-Organizing Map (SOM), back propagation neural-network and complex Morlet Wavelet methods. It uses complex Morlet wavelet-based envelope to extract successfully the characteristic frequencies of ball bearing. Then the minimum quantization error (MQE) indicator deriving from SOM is used for performance degradation assessment. Based on Weight Application to Failure Times (WAFT) technology, which deriving from back propagation neural networks, a prognostics model of ball bearing is developed successfully. And the experimental results show that the proposed methods are greatly superior to the currently used L10 bearing life prediction.
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Tyagi, Ankita, and Ritika Mehra. "An optimized CNN based intelligent prognostics model for disease prediction and classification from Dermoscopy images." Multimedia Tools and Applications 79, no. 35-36 (July 18, 2020): 26817–35. http://dx.doi.org/10.1007/s11042-020-09074-3.
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Choi, Bokyung, Kenneth Santo-Domingo, Alan S. Penzias, Ernesto Bosch, Arthur Leader, Antonio Pellicer, and Mylene Yao. "Turning Past IVF Data into Personalized Prognostics through a Validated, Multi-Center IVF Prediction Model." Fertility and Sterility 99, no. 3 (March 2013): S10—S11. http://dx.doi.org/10.1016/j.fertnstert.2013.01.021.
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Sun, Junjie, Lehui Zheng, Ying Huang, and Yu Ge. "Remaining Useful Life Prediction Based on CNN-BGRU-SA." Journal of Physics: Conference Series 2405, no. 1 (December 1, 2022): 012007. http://dx.doi.org/10.1088/1742-6596/2405/1/012007.
Full textAbstract:
Abstract Remaining Useful Life (RUL) prediction has very high importance for improving the safety and reliability of equipment, and its accurate estimation can provide technical support for fault Prognostics and Health Management (PHM). A hybrid deep neural network model has been proposed to improve the prognosis accuracy of equipment RUL. Convolution Neural Network (CNN) is used to extract the local features with Bidirectional Gated Recurrent Unit (BGRU) capturing the anterior and backward long-term dependence and then Self Attention (SA) assigning weights. Finally, Fully-Connection Network (FCN) is stacked to output the RUL prediction value, and its superiority is verified on C-MAPSS dataset. Experimental results show that CNN-BGRU-SA acquires better prognosis performance compared with a single network and the approach based on the BLCNN model, with high accuracy and generalization.