Imaging Fatigue Damage Precursors Based on Nonlinear Phased Array Ultrasonic Measurements of Diffuse Field

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Gheorghe Bunget ◽  
James Rogers ◽  
Cristina J. Bunget ◽  
Analeia E. Lavitz ◽  
Stanley Henley
Keyword(s):  
Fatigue Damage ◽  
Phased Array ◽  
Nonlinear Effects ◽  
Remaining Useful Life ◽  
Spectral Energy ◽  
Highly Sensitive ◽  
Ultrasonic Measurements ◽  
Useful Life ◽  
Higher Harmonic ◽  
Nondestructive Measurements

Abstract Nonlinear ultrasonic (NLU) techniques have emerged as a potential solution to improve the resolution of nondestructive measurements to detect microstructural changes of cyclically loaded materials. However, current NLU methods need power-demanding instrumentation that is useful only in the laboratory settings. On the other hand, phased array systems provide the capability of sensing such changes when the later portion of the elastic waveforms, called diffuse field, is analyzed. Moreover, phased array systems are an excellent solution for field test measurement and imaging of material damage. This study explores the use of NLU metrics based on ratios of harmonic amplitudes and frequencies to map the buildup of damage precursors, such as crystal dislocations, under cyclic loading within the microstructure of fatigued 2024-T3 aluminum specimens. The results show that these metrics are highly sensitive to microstructural fatigue damage making them significantly important to measure mechanical properties, such as fracture toughness, that are extremely useful in predicting the remaining useful life of a studied material. A nonlinear metric of elastic energy that encapsulates the nonlinear effects of subharmonic and higher-harmonic generations and frequency ratio is proposed. These effects of spectral energy shifts are combined making this metric highly sensitive to nano- and micro-scale damage within the fatigued medium.

scholarly journals A Fault-Signal-Based Generalizing Remaining Useful Life Prognostics Method for Wheel Hub Bearings

2019 ◽  
Vol 9 (6) ◽  
pp. 1080 ◽  
Author(s):  
Shixi Tang ◽  
Jinan Gu ◽  
Keming Tang ◽  
Rong Zou ◽  
Xiaohong Sun ◽  
...  
Keyword(s):  
Fatigue Damage ◽  
Remaining Useful Life ◽  
Working Environment ◽  
Short Term ◽  
Signal Model ◽  
Useful Life ◽  
Simplified Solution ◽  
Damage Type ◽  
Damage Types

The goal of this work is to improve the generalization of remaining useful life (RUL) prognostics for wheel hub bearings. The traditional life prognostics methods assume that the data used in RUL prognostics is composed of one specific fatigue damage type, the data used in RUL prognostics is accurate, and the RUL prognostics are conducted in the short term. Due to which, a generalizing RUL prognostics method is designed based on fault signal data. Firstly, the fault signal model is designed with the signal in a complex and mutative environment. Then, the generalizing RUL prognostics method is designed based on the fault signal model. Lastly, the simplified solution of the generalizing RUL prognostics method is deduced. The experimental results show that the proposed method gained good accuracies for RUL prognostics for all the amplitude, energy, and kurtosis features with fatigue damage types. The proposed method can process inaccurate fault signals with different kinds of noise in the actual working environment, and it can be conducted in the long term. Therefore, the RUL prognostics method has a good generalization.

Dynamic Modeling and Wear-Based Remaining Useful Life Prediction of High Power Clutch Systems

2005 ◽  
Vol 48 (2) ◽  
pp. 208-217 ◽  
Author(s):  
Matthew Watson ◽  
Carl Byington ◽  
Douglas Edwards ◽  
Sanket Amin
Keyword(s):  
Dynamic Modeling ◽  
High Power ◽  
Life Prediction ◽  
Remaining Useful Life ◽  
Useful Life

Remaining useful life estimation: a review on stochastic process-based approaches

2020 ◽  
Vol 14 ◽  
Author(s):  
Dangbo Du ◽  
Jianxun Zhang ◽  
Xiaosheng Si ◽  
Changhua Hu
Keyword(s):  
Stochastic Process ◽  
Health Management ◽  
Remaining Useful Life ◽  
Future Trends ◽  
Prognostics And Health Management ◽  
Inverse Gaussian ◽  
Life Estimation ◽  
Inverse Gaussian Process ◽  
Pros And Cons ◽  
Useful Life

Background: Remaining useful life (RUL) estimation is the central mission to the complex systems’ prognostics and health management. During last decades, numbers of developments and applications of the RUL estimation have proliferated. Objective: As one of the most popular approaches, stochastic process-based approach has been widely used for characterizing the degradation trajectories and estimating RULs. This paper aimed at reviewing the latest methods and patents on this topic. Methods: The review is concentrated on four common stochastic processes for degradation modelling and RUL estimation, i.e., Gamma process, Wiener process, inverse Gaussian process and Markov chain. Results: After a briefly review of these four models, we pointed out the pros and cons of them, as well as the improvement direction of each method. Conclusion: For better implementation, the applications of these four approaches on maintenance and decision-making are systematically introduced. Finally, the possible future trends are concluded tentatively.

Remaining Useful Life Prediction of Lithium-ion Batteries Using Multiple Kernel Extreme Learning Machine

2020 ◽  
Vol 13 ◽  
Author(s):  
Renxiong Liu
Keyword(s):  
Lithium Ion Batteries ◽  
Extreme Learning Machine ◽  
Kernel Function ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Prediction Methods ◽  
Multiple Kernel ◽  
Kernel Extreme Learning Machine ◽  
Useful Life ◽  
Learning Machine

Objective: Lithium-ion batteries are important components used in electric automobiles (EVs), fuel cell EVs and other hybrid EVs. Therefore, it is greatly important to discover its remaining useful life (RUL). Methods: In this paper, a battery RUL prediction approach using multiple kernel extreme learning machine (MKELM) is presented. The MKELM’s kernel keeps diversified by consisting multiple kernel functions including Gaussian kernel function, Polynomial kernel function and Sigmoid kernel function, and every kernel function’s weight and parameter are optimized through differential evolution (DE) algorithm. Results : Battery capacity data measured from NASA Ames Prognostics Center are used to demonstrate the prediction procedure of the proposed approach, and the MKELM is compared with other commonly used prediction methods in terms of absolute error, relative accuracy and mean square error. Conclusion: The prediction results prove that the MKELM approach can accurately predict the battery RUL. Furthermore, a compare experiment is executed to validate that the MKELM method is better than other prediction methods in terms of prediction accuracy.

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