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 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.

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.

scholarly journals Remaining Useful Life Estimation for Rolling Bearing With SIOS-Based Indicator and Particle Filtering

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 24521-24532 ◽  
Author(s):  
Mingquan Qiu ◽  
Wei Li ◽  
Fan Jiang ◽  
Zhencai Zhu
Keyword(s):  
Particle Filtering ◽  
Rolling Bearing ◽  
Remaining Useful Life ◽  
Life Estimation ◽  
Useful Life

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 Degradation Monitoring of Insulation Systems Used in Low-Voltage Electromagnetic Coils under Thermal Loading Conditions from a Creep Point of View

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3696
Author(s):  
Kai Wang ◽  
Haifeng Guo ◽  
Aidong Xu ◽  
Michael Pecht
Keyword(s):  
Creep Deformation ◽  
High Frequency ◽  
Thermal Loading ◽  
Point Of View ◽  
Electromagnetic Coils ◽  
Degradation Monitoring ◽  
Insulation Degradation ◽  
Insulation Systems ◽  
Micro Level ◽  
Turn Insulation

Electromagnetic coils are a key component in a variety of systems and are widely used in many industries. Because their insulation usually fails suddenly and can have catastrophic effects, degradation monitoring of coil insulation systems plays a vital role in avoiding unexpected machine shutdown. The existing insulation degradation monitoring methods are based on assessing the change of coil high-frequency electrical parameter response, whereas the effects of the insulation failure mechanisms are not considered, which leads to inconsistency between experimental results. Therefore, this paper investigates degradation monitoring of coil insulation systems under thermal loading conditions from a creep point of view. Inter-turn insulation creep deformation is identified as a quantitative index to manifest insulation degradation changes at the micro level. A method is developed to map coil high-frequency electrical monitoring parameters to inter-turn insulation creep deformation in order to bridge the gap between the micro-level and macro-level changes during the incipient insulation degradation process. Thermally accelerated tests are performed to validate the developed method. The mapping method helps to determine the physical meaning of coil electrical monitoring parameters and presents opportunities for predictive maintenance of machines that incorporate electromagnetic coils.

scholarly journals A Stochastic Deterioration Process Based Approach for Micro Switches Remaining Useful Life Estimation

2019 ◽  
Vol 9 (3) ◽  
pp. 613
Author(s):  
Bangcheng Zhang ◽  
Yubo Shao ◽  
Zhenchen Chang ◽  
Zhongbo Sun ◽  
Yuankun Sui
Keyword(s):  
Kalman Filter ◽  
Health Management ◽  
Remaining Useful Life ◽  
Degradation Model ◽  
Time Prediction ◽  
Model Based ◽  
Stochastic Parameters ◽  
Deterioration Process ◽  
Useful Life ◽  
Posterior Estimation

Real-time prediction of remaining useful life (RUL) is one of the most essential works inprognostics and health management (PHM) of the micro-switches. In this paper, a lineardegradation model based on an inverse Kalman filter to imitate the stochastic deterioration processis proposed. First, Bayesian posterior estimation and expectation maximization (EM) algorithm areused to estimate the stochastic parameters. Second, an inverse Kalman filter is delivered to solvethe errors in the initial parameters. In order to improve the accuracy of estimating nonlinear data,the strong tracking filtering (STF) method is used on the basis of Bayesian updating Third, theeffectiveness of the proposed approach is validated on an experimental data relating tomicro-switches for the rail vehicle. Additionally, it proposes another two methods for comparisonto illustrate the effectiveness of the method with an inverse Kalman filter in this paper. Inconclusion, a linear degradation model based on an inverse Kalman filter shall deal with errors inRUL estimation of the micro-switches excellently.

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