Research on Battery State Tracking and Remaining Life Prediction Method Based on Improved Particle Filtering and Reformed Self-Adaptive Fusion Algorithms

2019 ◽  
Author(s):  
Ziquan Jiao ◽  
Xingming Fan ◽  
Xin Zhang
Keyword(s):  
Particle Filtering ◽  
Life Prediction ◽  
Prediction Method ◽  
Remaining Life ◽  
Adaptive Fusion ◽  
State Tracking ◽  
Self Adaptive

scholarly journals Coating Life Prediction for Combustion Turbine Blades

1998 ◽  
Author(s):  
Kwai S. Chan ◽  
N. Sastry Cheruvu ◽  
Gerald R. Leverant
Keyword(s):  
Experimental Data ◽  
Turbine Blade ◽  
Cycle Time ◽  
Life Prediction ◽  
Aluminide Coating ◽  
Turbine Blades ◽  
Prediction Method ◽  
Remaining Life ◽  
Degradation Mechanisms ◽  
Useful Life

A life prediction method for combustion turbine blade coatings has been developed by modeling coating degradation mechanisms including oxidation, spallation, and aluminum loss due to inward diffusion. Using this model, the influence of cycle time on coating life is predicted for GTD-111 coated with an MCrAlY, PtAl, or aluminide coating. The results are used to construct a coating life diagram that depicts failure and safe regions for the coating in a log-log plot of number of startup cycles versus cycle time. The regime where failure by oxidation, spallation, and inward diffusion dominates is identified and delineated from that dominated by oxidation and inward diffusion only. A procedure for predicting the remaining life of a coating is developed. The utility of the coating life diagram for predicting the failure and useful life of MCrAlY, aluminide, or PtAl coatings on the GTD-111 substrate is illustrated and compared against experimental data.

Coating Life Prediction for Combustion Turbine Blades

1999 ◽  
Vol 121 (3) ◽  
pp. 484-488 ◽  
Author(s):  
K. S. Chan ◽  
N. S. Cheruvu ◽  
G. R. Leverant
Keyword(s):  
Experimental Data ◽  
Turbine Blade ◽  
Cycle Time ◽  
Life Prediction ◽  
Aluminide Coating ◽  
Turbine Blades ◽  
Prediction Method ◽  
Remaining Life ◽  
Degradation Mechanisms ◽  
Useful Life

A life prediction method for combustion turbine blade coatings has been developed by modeling coating degradation mechanisms including oxidation, spallation, and aluminum loss due to inward diffusion. Using this model, the influence of cycle time on coating life is predicted for GTD-111 coated with an MCrAlY, PtAl, or aluminide coating. The results are used to construct a coating life diagram that depicts failure and safe regions for the coating in a log-log Plot of number of startup cycles versus cycle time. The regime where failure by oxidation, spallation, and inward diffusion dominates is identified and delineated from that dominated by oxidation and inward diffusion only. A procedure for predicting the remaining life of a coating is developed. The utility of the coating life diagram for predicting the failure and useful life of MCrAlY, aluminide, or PtAl coatings on the GTD-111 substrate is illustrated and compared against experimental data.

Remaining Life Prediction Based on HOLT Algorithm for Civil Aero-Engine

2013 ◽  
Vol 483 ◽  
pp. 479-483
Author(s):  
Fang Bai
Keyword(s):  
Life Prediction ◽  
Residual Life ◽  
Engineering Application ◽  
Prediction Method ◽  
Remaining Life ◽  
Limited Information ◽  
Reliability Estimate ◽  
Aero Engine ◽  
Application Requirements ◽  
Real Time Application

Under the condition of limited information on route, incomplete check, minimum maintenance and other characteristics, it will be that the concept of reliability estimate extended to real-time application of reliability. The single remaining life prediction method based on HOLT algorithm to solve dynamic use under the environment of residual life prediction problem. Case analysis results show that the method of forecasting precision can get the engineering application requirements, so it is effective and feasible.

Remaining Life Prediction Method for Rolling Bearing Based on the Long Short-Term Memory Network

2019 ◽  
Vol 50 (3) ◽  
pp. 2437-2454 ◽  
Author(s):  
Fengtao Wang ◽  
Xiaofei Liu ◽  
Gang Deng ◽  
Xiaoguang Yu ◽  
Hongkun Li ◽  
...  
Keyword(s):  
Life Prediction ◽  
Short Term Memory ◽  
Prediction Method ◽  
Rolling Bearing ◽  
Remaining Life ◽  
Short Term ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory

scholarly journals Prognostic and Remaining Life Prediction of Electronic Device under Vibration Condition Based on CPSD of MPI

2016 ◽  
Vol 2016 ◽  
pp. 1-11
Author(s):  
Ying Chen ◽  
Ning Tang ◽  
Zenghui Yuan
Keyword(s):  
Resonance Frequency ◽  
Life Cycle Cost ◽  
Life Prediction ◽  
Electronic Device ◽  
Damping Ratio ◽  
Prediction Method ◽  
Vibration Response ◽  
Remaining Life ◽  
Modal Parameter ◽  
Vibration Condition

Prognostic of electronic device under vibration condition can help to get information to assist in condition-based maintenance and reduce life-cycle cost. A prognostic and remaining life prediction method for electronic devices under random vibration condition is proposed. Vibration response is measured and monitored with acceleration sensor and OMA parameters, including vibration resonance frequency, especially first-order resonance frequency, and damping ratio is calculated with cross-power spectrum density (CPSD) method and modal parameter identification (MPI) algorithm. Steinberg vibration fatigue model which considers transmissibility factor is used to predict the remaining life of electronic component. Case study with a test board is carried out and remaining life is predicted. Results show that with this method the vibration response characteristic can be monitored and predicted.

scholarly journals A Wiener process–based remaining life prediction method for light-emitting diode driving power in rail vehicle carriage

2019 ◽  
Vol 11 (3) ◽  
pp. 168781401983221 ◽  
Author(s):  
Zhi Gao ◽  
Xiaojing Yin ◽  
Bangcheng Zhang ◽  
Minmin Chen ◽  
Bo Li
Keyword(s):  
Wiener Process ◽  
Life Prediction ◽  
Light Emitting Diode ◽  
Prediction Method ◽  
Remaining Life ◽  
Output Current ◽  
Degradation Model ◽  
Light Emitting ◽  
Accelerated Degradation ◽  
Rail Vehicle

Remaining life prediction is an effective way to optimize maintenance strategy and improve service life for light-emitting diode driving power in rail vehicle carriage. In this article, a Wiener process–based remaining life prediction method is proposed with the analysis of performance degradation data of light-emitting diode driving power in rail vehicle carriage. First, the temperature and humidity stress accelerated degradation tests are put forward in order to measure the output current of light-emitting diode driving power. Based on the output current, the accelerated degradation model is established. The drift and diffusion coefficients of the Wiener process are then obtained without prior information. Finally, the reliability of light-emitting diode driving power in rail vehicle carriage is assessed and the remaining lifetime is predicted after updating the degradation model parameters with Bayesian inference. The results show that the proposed method can improve the precision of assessment and reduce the uncertainty of prediction significantly. It also provides a potential solution for life prediction of other similar products.

Hybrid remaining useful life prediction method. A case study on railway D-cables

2021 ◽  
pp. 107746
Author(s):  
Yu Zang ◽  
Wei Shangguan ◽  
Baigen Cai ◽  
Huasheng Wang ◽  
Michael. G. Pecht
Keyword(s):  
Life Prediction ◽  
Prediction Method ◽  
Remaining Useful Life ◽  
Useful Life
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