A Probability Assessment Method for Degradation of Bridge Power MOSFET Circuit Based on Common Turn-On State

2012 ◽  
Vol 160 ◽  
pp. 125-129
Author(s):  
Xiang Fen Wang ◽  
Gui Cui Fu ◽  
Cheng Gao ◽  
Jin Yong Yao
Keyword(s):  
Assessment Method ◽  
Performance Degradation ◽  
Probability Assessment ◽  
Degradation Behavior ◽  
Power Mosfet ◽  
Turn On ◽  
State Of Power ◽  
Degradation Degree ◽  
Voltage Degradation ◽  
Threshold Voltage Degradation

A performance degradation assessment method is proposed based on probability statistic of common turn-on state of power MOSFET circuit in this paper. Threshold shift transient characteristics of MOSFET are studied and the performance degradation behavior of a bridge power MOSFET circuit is simulated. Threshold voltage degradation of a power MOSFET circuit caused by half bridge arm is observed and the probability of a period transient common turn-on state is calculated to evaluate the degree. The result can be used to evaluate the performance degradation trend and can also provide data support for predicting the degradation degree before circuit failed.

A model based on hidden graphic evaluation and review technique network to evaluate reliability and lifetime of multi-state systems

2018 ◽  
Vol 233 (3) ◽  
pp. 369-378 ◽  
Author(s):  
Wenjie Dong ◽  
Sifeng Liu ◽  
Zhigeng Fang ◽  
Yingsai Cao ◽  
Ye Ding
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Network Model ◽  
State Transition ◽  
Likelihood Function ◽  
Hidden Markov ◽  
Reliability Assessment ◽  
Assessment Method ◽  
Performance Degradation ◽  
State System

The essence of multi-state system performance degradation is a process of deteriorating state transition. On the basis of hidden Markov model and graphic evaluation and review technique network, this article proposes a new reliability assessment method called hidden graphic evaluation and review technique network model for multi-state system. Specifically, nodes in graphic evaluation and review technique network represent hidden states of a system at different deteriorating times, and they can be expanded through a series of observable sequences. Baum–Welch algorithm in hidden Markov model is introduced to train parameters and when logarithmic likelihood function of the output reaches convergent, we can estimate the most probable output state and obtain the state transition probability eventually. Suppose performance degradation amount between different nodes follows gamma distribution, a method of improved maximum likelihood function is introduced to estimate parameters. According to signal flow graph theory and Mason formula, equivalent transfer function from the initial node to any other nodes can be obtained, then expectation and variance of performance degradation amount can be presented. In the real case study, we compare the reliability assessment method proposed in this article with other two traditional methods, which show the rationality of hidden graphic evaluation and review technique network model.

Circuit Simulation of Threshold-Voltage Degradation in a-Si:H TFTs Fabricated at 175 $^{\circ}\hbox{C}$

2007 ◽  
Vol 54 (7) ◽  
pp. 1781-1783 ◽  
Author(s):  
Rahul Shringarpure ◽  
Sameer Venugopal ◽  
Zi Li ◽  
Lawrence T. Clark ◽  
David R. Allee ◽  
...  
Keyword(s):  
Threshold Voltage ◽  
Circuit Simulation ◽  
Voltage Degradation ◽  
Threshold Voltage Degradation

BEARING PERFORMANCE DEGRADATION ASSESSMENT BY ORTHOGONAL LOCAL PRESERVING PROJECTION AND CONTINUOUS HIDDEN MARKOV MODEL

2016 ◽  
Vol 40 (5) ◽  
pp. 1019-1030
Author(s):  
Tao Liu ◽  
Xing Wu ◽  
Yu Guo ◽  
Chang Liu
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Frequency Domain ◽  
Hidden Markov ◽  
Performance Degradation ◽  
Time Frequency ◽  
Rotating Machine ◽  
Accelerated Life ◽  
The Time Domain ◽  
Degradation Degree

Bearing is the key component in rotating machine. It is important to assess the performance degradation degree of bearings for making proactive maintenance and realizing near-zero downtime. A methodology based on orthogonal local preserving projection (OLPP) and continuous hidden Markov model (CHMM) is introduced in bearing performance degradation assessment. Firstly, the time domain, frequency domain and time-frequency domain features are extracted from the vibration signals. Then, the multi-dimensional features are reduced by OLPP. And the selection of the adjacent paragraph parameters in OLPP is optimized adaptively by minimizing the ratio of between-class distance to within-class distance. A CHMM is trained by using the reduced feature in normal condition. At last, the test bearing data are input into the pre-trained CHMM to calculate the log-likelihood of the test data, which can assess the performance degradation of bearings quantitatively. A bearing accelerated life experiment is performed to validate the feasibility and validity of the proposed method.

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