Accelerated degradation data of SiC MOSFETs for lifetime and Remaining Useful Life assessment

2014 ◽  
Vol 54 (9-10) ◽  
pp. 1718-1723 ◽  
Author(s):  
T. Santini ◽  
S. Morand ◽  
M. Fouladirad ◽  
L.V. Phung ◽  
F. Miller ◽  
...  
Keyword(s):  
Remaining Useful Life ◽  
Life Assessment ◽  
Accelerated Degradation ◽  
Degradation Data ◽  
Useful Life

scholarly journals Remaining Useful Life Estimation Considering Prior Accelerated Degradation Data and Bayesian Inference for Multi-Stress Operating Conditions

2020 ◽  
Vol 6 (1) ◽  
pp. 103-117
Author(s):  
Shah M. Limon ◽  
Om Prakash Yadav
Keyword(s):  
Asset Management ◽  
Field Performance ◽  
Degradation Process ◽  
Management Plan ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Degradation Behavior ◽  
Accelerated Degradation ◽  
Degradation Data ◽  
Useful Life

Prediction of remaining useful life using the field monitored performance data provides a more realistic estimate of life and helps develop a better asset management plan. The field performance can be monitored (indirectly) by observing the degradation of the quality characteristics of a product. This paper considers the gamma process to model the degradation behavior of the product characteristics. An integrated Bayesian approach is proposed to estimate the remaining useful life that considers accelerated degradation data to model degradation behavior first. The proposed approach also considers interaction effects in a multi-stress scenario impacting the degradation process. To reduces the computational complexity, posterior distributions are estimated using the MCMC simulation technique. The proposed method has been demonstrated with an LED case example and results show the superiority of Bayesian-based RUL estimation.

scholarly journals Particle Filtering Based Remaining Useful Life Prediction for Electromagnetic Coil Insulation

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 473
Author(s):  
Haifeng Guo ◽  
Aidong Xu ◽  
Kai Wang ◽  
Yue Sun ◽  
Xiaojia Han ◽  
...  
Keyword(s):  
Particle Filtering ◽  
Remaining Useful Life ◽  
Electrical Parameters ◽  
Degradation Model ◽  
Accelerated Degradation ◽  
Insulation Failure ◽  
Electromagnetic Coils ◽  
Degradation Monitoring ◽  
Insulation Degradation ◽  
Useful Life

Electromagnetic coils are one of the key components of many systems. Their insulation failure can have severe effects on the systems in which coils are used. This paper focuses on insulation degradation monitoring and remaining useful life (RUL) prediction of electromagnetic coils. First, insulation degradation characteristics are extracted from coil high-frequency electrical parameters. Second, health indicator is defined based on insulation degradation characteristics to indicate the health degree of coil insulation. Finally, an insulation degradation model is constructed, and coil insulation RUL prediction is performed by particle filtering. Thermal accelerated degradation experiments are performed to validate the RUL prediction performance. The proposed method presents opportunities for predictive maintenance of systems that incorporate coils.

scholarly journals Deep learning-based anomaly-onset aware remaining useful life estimation of bearings

2021 ◽  
Vol 7 ◽  
pp. e795
Author(s):  
Pooja Vinayak Kamat ◽  
Rekha Sugandhi ◽  
Satish Kumar
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Short Term Memory ◽  
Rotating Machinery ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Learning Models ◽  
Vibration Data ◽  
Degradation Data ◽  
Useful Life

Remaining Useful Life (RUL) estimation of rotating machinery based on their degradation data is vital for machine supervisors. Deep learning models are effective and popular methods for forecasting when rotating machinery such as bearings may malfunction and ultimately break down. During healthy functioning of the machinery, however, RUL is ill-defined. To address this issue, this study recommends using anomaly monitoring during both RUL estimator training and operation. Essential time-domain data is extracted from the raw bearing vibration data, and deep learning models are used to detect the onset of the anomaly. This further acts as a trigger for data-driven RUL estimation. The study employs an unsupervised clustering approach for anomaly trend analysis and a semi-supervised method for anomaly detection and RUL estimation. The novel combined deep learning-based anomaly-onset aware RUL estimation framework showed enhanced results on the benchmarked PRONOSTIA bearings dataset under non-varying operating conditions. The framework consisting of Autoencoder and Long Short Term Memory variants achieved an accuracy of over 90% in anomaly detection and RUL prediction. In the future, the framework can be deployed under varying operational situations using the transfer learning approach.

Fitness-for-Service and Remaining Useful Life Assessment of a Steam Drum

2003 ◽  
Author(s):  
Raymond K. Yee
Keyword(s):  
Power Plant ◽  
Small Sample ◽  
Analysis Procedure ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Life Assessment ◽  
Life Evaluation ◽  
Useful Life ◽  
Size Evaluation ◽  
Steam Drum

A steam drum in a typical power plant has experienced in-service cracking. Nondestructive examinations (NDE) were performed and a small sample was collected from the drum to evaluate the extent of the cracking that had occurred in the drum shell. Fitness-for-service and remaining useful life analyses of the drum were performed based on the NDE results and operating conditions. In this paper, the fitness-for-service analyses of the steam drum are described. The analysis procedure, material property determination, stress analysis, limiting flaw size evaluation, and remaining useful life evaluation for the drum are discussed. Recommendations for appropriate action are also presented.

A nonlinear Wiener degradation model integrating degradation data under accelerated stresses and real operating environment

2020 ◽  
pp. 1748006X2097809
Author(s):  
Li Sun ◽  
Fangchao Zhao ◽  
Narayanaswamy Balakrishnan ◽  
Honggen Zhou ◽  
Xiaohui Gu
Keyword(s):  
Remaining Useful Life ◽  
Normal Stresses ◽  
Operating Environment ◽  
Unknown Parameters ◽  
Step Method ◽  
Degradation Model ◽  
Accelerated Degradation ◽  
Degradation Data ◽  
Proposed Model ◽  
The Difference

Remaining useful life (RUL) prediction in real operating environment (ROE) plays an important role in condition-based maintenance. However, the life information in ROE is limited, especially for some long-life products. In such cases, accelerated degradation test (ADT) is an effective method to collect data and then the accelerated degradation data are converted to normal level of accelerated stresses through acceleration factors. However, the stresses in ROE are different from normal stresses since there are some other stresses except normal stresses, which cannot be accelerated, but still have impact on the degradation. To predict the RUL in ROE, a nonlinear Wiener degradation model is proposed based on failure mechanism invariant principle which is the precondition and requirement of an ADT and a calibration factor is introduced to calibrate the difference between ROE and normal stresses. Moreover, the unit-to-unit variability is considered in the concern model. Based upon the proposed approach, the RUL distribution is derived in closed form. The unknown parameters in the model are obtained by a new two-step method through fuzing converted degradation data in normal stresses and degradation data in ROE. Finally, the validity of the proposed model is demonstrated through several simulation data and a case study.

scholarly journals Estimating Remaining Useful Life for Degrading Systems with Large Fluctuations

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Dang-Bo Du ◽  
Jian-Xun Zhang ◽  
Zhi-Jie Zhou ◽  
Xiao-Sheng Si ◽  
Chang-Hua Hu
Keyword(s):  
Stochastic Process ◽  
Standard Deviation ◽  
Health Management ◽  
Prediction Method ◽  
Remaining Useful Life ◽  
Threshold Function ◽  
Engineering Practice ◽  
Practical Case ◽  
Degradation Data ◽  
Useful Life

Remaining useful life (RUL) prediction method based on degradation trajectory has been one of the most important parts in prognostics and health management (PHM). In the conventional model, the degradation data are usually used for degradation modeling directly. In engineering practice, the degradation of many systems presents a volatile situation, that is, fluctuation. In fact, the volatility of degradation data shows the stability of system, so it could be used to reflect the performance of system. As such, this paper proposes a new degradation model for RUL estimation based on the volatility of degradation data. Firstly the degradation data are decomposed into trend items and random items, which are defined as a stochastic process. Then the standard deviation of the stochastic process is defined as another performance variable because standard deviation reflects the system performance. Finally the Wiener process and the normal stochastic process are used to model the trend items and random items separately, and then the probability density function (PDF) of the RUL is obtained via a redefined failure threshold function that combines the trend items and the standard deviation of the random items. Two practical case studies demonstrate that, compared with traditional approaches, the proposed model can deal with the degradation data with many fluctuations better and can get a more reasonable result which is convenient for maintenance decision.

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