An External Shock - Internal Barrier Degradation Model to Account for Operational Loads

2020 ◽  
Author(s):  
Jørn Vatn ◽  
Frank Børre Pedersen
Keyword(s):  
Degradation Model ◽  
Internal Barrier

Parameter Estimation of Fog Degradation Model and CUDA Design

2018 ◽  
Vol 30 (2) ◽  
pp. 327
Author(s):  
Chunyan Yu ◽  
Huixiang Lin ◽  
Xiaodan Xu ◽  
Xinyan Ye
Keyword(s):  
Parameter Estimation ◽  
Degradation Model

The analysis on the influence of mixed sliding-rolling motion mode to precision degradation of ball screw

2021 ◽  
pp. 095440622098201
Author(s):  
Qiang Cheng ◽  
Baobao Qi ◽  
Hongyan Chu ◽  
Ziling Zhang ◽  
Zhifeng Liu ◽  
...  
Keyword(s):  
Structural Parameters ◽  
Experimental Tests ◽  
Theoretical Models ◽  
Operating Conditions ◽  
Ball Screw ◽  
Rolling Motion ◽  
Degradation Model ◽  
Sliding Motion ◽  
Factors Analysis ◽  
Motion Mode

The combination of sliding/rolling motion can influence the degree of precision degradation of ball screw. Precision degradation modeling and factors analysis can reveal the evolution law of ball screw precision. This paper presents a precision degradation model for factors analysis influencing precision due to mixed sliding-rolling motion. The precision loss model was verified through the comparison of theoretical models and experimental tests. The precision degradation due to rolling motion between the ball and raceway accounted for 29.09% of the screw precision loss due to sliding motion. Additionally, the total precision degradation due to rolling motion accounted for 21.03% of the total sliding precision loss of the screw and nut, and 17.38% of the overall ball screw precision loss under mixed sliding-rolling motion. In addition, the effects of operating conditions and structural parameters on precision loss were analyzed. The sensitivity coefficients of factors influencing were used to quantitatively describe impact degree on precision degradation.

scholarly journals Remaining Useful Life Prediction of Broken Rotor Bar Based on Data-Driven and Degradation Model

2021 ◽  
Vol 11 (16) ◽  
pp. 7175
Author(s):  
Islem Bejaoui ◽  
Dario Bruneo ◽  
Maria Gabriella Xibilia
Keyword(s):  
Principal Component Analysis ◽  
Induction Motor ◽  
Life Prediction ◽  
Principal Component ◽  
Component Analysis ◽  
Remaining Useful Life ◽  
Degradation Model ◽  
Broken Rotor Bar ◽  
Industrial Sectors ◽  
Useful Life

Rotating machines such as induction motors are crucial parts of most industrial systems. The prognostic health management of induction motor rotors plays an essential role in increasing electrical machine reliability and safety, especially in critical industrial sectors. This paper presents a new approach for rotating machine fault prognosis under broken rotor bar failure, which involves the modeling of the failure mechanism, the health indicator construction, and the remaining useful life prediction. This approach combines signal processing techniques, inherent metrics, and principal component analysis to monitor the induction motor. Time- and frequency-domains features allowing for tracking the degradation trend of motor critical components that are extracted from torque, stator current, and speed signals. The most meaningful features are selected using inherent metrics, while two health indicators representing the degradation process of the broken rotor bar are constructed by applying the principal component analysis. The estimation of the remaining useful life is then obtained using the degradation model. The performance of the prediction results is evaluated using several criteria of prediction accuracy. A set of synthetic data collected from a degraded Simulink model of the rotor through simulations is used to validate the proposed approach. Experimental results show that using the developed prognostic methodology is a powerful strategy to improve the prognostic of induction motor degradation.

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.

Remaining useful life prediction method for machine tools based on meta-action theory

2021 ◽  
pp. 1748006X2110025
Author(s):  
Zongyi Mu ◽  
Yan Ran ◽  
Genbao Zhang ◽  
Hongwei Wang ◽  
Xin Yang
Keyword(s):  
Machine Tools ◽  
Life Prediction ◽  
Action Theory ◽  
Prediction Method ◽  
Operational Reliability ◽  
Remaining Useful Life ◽  
Practical Application ◽  
Dynamic Prediction ◽  
Degradation Model ◽  
Useful Life

Remaining useful life (RUL) is a crucial indictor to measure the performance degradation of machine tools. It directly affects the accuracy of maintenance decision-making, thus affecting operational reliability of machine tools. Currently, most RUL prediction methods are for the parts. However, due to the interaction among the parts, even RUL of all the parts cannot reflect the real RUL of the whole machine. Therefore, an RUL prediction method for the whole machine is needed. To predict RUL of the whole machine, this paper proposes an RUL prediction method with dynamic prediction objects based on meta-action theory. Firstly, machine tools are decomposed into the meta-action unit chains (MUCs) to obtain suitable prediction objects. Secondly, the machining precision unqualified rate (MPUR) control chart is used to conduct an out of control early warning for machine tools’ performance. At last, the Markov model is introduced to determine the prediction objects in next prediction and the Wiener degradation model is established to predict RUL of machine tools. According to the practical application, feasibility and effectiveness of the method is proved.

Validation metric of degradation model with dynamic performance

2015 ◽  
Vol 20 (3) ◽  
pp. 302-306 ◽  
Author(s):  
Chun-bo Yang ◽  
Sheng-kui Zeng ◽  
Jian-bin Guo
Keyword(s):  
Dynamic Performance ◽  
Degradation Model

Enhanced thermal stability and lifetime of epoxy nanocomposites using covalently functionalized clay: experimental and modelling

2015 ◽  
Vol 39 (3) ◽  
pp. 2269-2278 ◽  
Author(s):  
Omid Zabihi ◽  
Hamid Khayyam ◽  
Bronwyn L. Fox ◽  
Minoo Naebe
Keyword(s):  
Thermal Stability ◽  
Thermal Degradation ◽  
Structural Changes ◽  
Epoxy Nanocomposites ◽  
Degradation Model ◽  
Different Types

Relationship between thermal degradation model of epoxy nanocomposites containing different types of modified nanoclay and their structural changes is explained.

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