On-line condition monitoring for MOSFET and IGBT switches in digitally controlled drives

Anderson, Jason M.; Cox, Robert W.

doi:10.1109/ecce.2011.6064302

Cited by 31 publications

(10 citation statements)

References 26 publications

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“…The extrinsic failures include the transistor packaging issues and mainly summarized as a bond-wire lift, die solder detachment, and contact migration. Most of these studies verified that the bond-wire lift has a severe effect on the device failure over time [23,24].…”

Section: Precursor Identifications In Power Mosfet Degradationmentioning

confidence: 77%

Real-Time Deep Learning at the Edge for Scalable Reliability Modeling of Si-MOSFET Power Electronics Converters

Baharani

Biglarbegian

Parkhideh

et al. 2019

IEEE Internet Things J.

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With the significant growth of advanced high-frequency power converters, on-line monitoring and active reliability assessment of power electronic devices are extremely crucial. This article presents a transformative approach, named Deep Learning Reliability Awareness of Converters at the Edge (Deep RACE), for real-time reliability modeling and prediction of high-frequency MOSFET power electronic converters. Deep RACE offers a holistic solution which comprises algorithm advances, and full system integration (from the cloud down to the edge node) to create a near real-time reliability awareness. On the algorithm side, this paper proposes a deep learning algorithmic solution based on stacked LSTM for collective reliability training and inference across collective MOSFET converters based on device resistance changes. Deep RACE also proposes an integrative edge-to-cloud solution to offer a scalable decentralized devices-specific reliability monitoring, awareness, and modeling. The MOSFET convertors are IoT devices which have been empowered with edge real-time deep learning processing capabilities. The proposed Deep RACE solution has been prototyped and implemented through learning from MOSFET data set provided by NASA. Our experimental results show an average miss prediction of 8.9% over five different devices which is a much higher accuracy compared to well-known classical approaches (Kalman Filter, and Particle Filter). Deep RACE only requires 26ms processing time and 1.87W computing power on Edge IoT device. components in the energy conversion process. Therefore, understanding, modeling and predicting the reliability models of the power converters are crucial for enabling emerging technologies and future applications such as electric vehicles, smart grids, and renewable energy.Mathematically formulating and precise understanding of the physical degradation in high-frequency power converters is notoriously difficult, due to the system sophistication and many unknown non-deterministic variables. To solve this problem, a wide range of stochastical diagnostic and prognostics techniques have been proposed to address the reliability issues of a complex system in the design, fabrication, and maintenance process. The evaluation of these processes is beneficial to enable power convertors health management systems and resiliency for useful life estimation and reducing the risk of failures [2,3]. Kalman filter and Bayesian calibration are two examples of classical time series modeling and prediction techniques. However, these approaches are often bounded to first-order models in isolation and are not able to bring the collective behavior of many devices with the same underlying physic to create an accurate algorithmic construct. Therefore, their prediction accuracy is very limited. Moreover, they have very limited scalability for emerging advanced technologies [4,5].Recent advances in deep learning open a new horizon toward smart and autonomous systems. Deep learning offers a scalable data-driven discriminative paradigm to un...

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Section: Precursor Identifications In Power Mosfet Degradationmentioning

confidence: 77%

Real-Time Deep Learning at the Edge for Scalable Reliability Modeling of Si-MOSFET Power Electronics Converters

Baharani

Biglarbegian

Parkhideh

et al. 2019

IEEE Internet Things J.

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“…In this case, the latter has been chosen due to providing more control over inputs such as the dimensions of the component and constitutive material models as well as the boundary conditions. According to literature, the active switching devices, being the MOSFETs, are more prone to failure than the other components when not using aluminum electrolytic capacitors in the design [34,35]. Specifically the bond wires and the die-attach are sensitive to the thermal stress generated by the discrepancy in coefficients of thermal expansion (CTE) of the different materials [36].…”

Section: Constructing a Fem Mosfet Modelmentioning

confidence: 99%

The Sensitivity of an Electro-Thermal Photovoltaic DC–DC Converter Model to the Temperature Dependence of the Electrical Variables for Reliability Analyses

et al. 2020

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The operational expenditures of solar energy are gaining attention because of the continuous decrease of the capital expenditures. This creates a demand for more reliable systems to further decrease the costs. Increased reliability is often ensured by iterative use of design for reliability. The number of iterations that can take place strongly depends on the computational efficiency of this methodology. The main research objective is to quantify the influence of the temperature dependence of the electrical variables used in the electro-thermal model on the reliability and the computation time. The influence on the reliability is evaluated by using a 2-D finite elements method model of the MOSFET and calculating the plastic energy dissipation density in the die-attach and the bond wire. The trade-off between computation time of the electro-thermal model in PLECS (4.3, Plexim, Zurich, Switzerland) and generated plastic energy accuracy obtained in COMSOL (5.3, COMSOL Inc., Burlington, MA, USA) is reported when excluding a certain temperature dependence. The results indicate that the temperature dependence of the input and output capacitors causes no change in the plastic energy dissipated in the MOSFET but does introduce the largest increase in computation time. However, not including the temperature dependence of the MOSFET itself generates the largest difference in plastic energy of 10% as the losses in the die are underestimated.

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“…The phenomenon of intrinsic failure mechanism in power electronics includes hot carrier injection (HCI), latch up mechanism (i.e., sudden collapse of the collector to emitter voltage), dielectric breakdown, and electromigration. Extrinsic faults consist of wire lift off, die solder delamination, and substrate solder degradation [16], [17]. On the other hand, the aging of power electronic systems resulting from harsh operating conditions and environmental stress are two potential failure modes of stress and cause of catastrophic failure in IGBTs.…”

Section: Failure Mechanism Overviewmentioning

confidence: 99%

Computationally Efficient, Real-Time, and Embeddable Prognostic Techniques for Power Electronics

Alghassi

Perinpanayagam

Samie

et al. 2015

IEEE Trans. Power Electron.

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Power electronics are increasingly important in new generation vehicles as critical safety mechanical subsystems are being replaced with more electronic components. Hence, it is vital that the health of these power electronic components is monitored for safety and reliability on a platform. The aim of this paper is to develop a prognostic approach for predicting the remaining useful life of power electronic components. The developed algorithms must also be embeddable and computationally efficient to support on-board real-time decision making. Current state-of-the-art prognostic algorithms, notably those based on Markov models, are computationally intensive and not applicable to real-time embedded applications. In this paper, an isolated-gate bipolar transistor (IGBT) is used as a case study for prognostic development. The proposed approach is developed by analyzing failure mechanisms and statistics of IGBT degradation data obtained from an accelerated aging experiment. The approach explores various probability distributions for modeling discrete degradation profiles of the IGBT component. This allows the stochastic degradation model to be efficiently simulated, in this particular example ∼1000 times more efficiently than Markov approaches. Index Terms-Isolated-gate bipolar transistor (IGBT), Monte-Carlo simulation (MCS), power electronics, prognostics, remaining useful life (RUL).

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On-line condition monitoring for MOSFET and IGBT switches in digitally controlled drives

Cited by 31 publications

References 26 publications

Real-Time Deep Learning at the Edge for Scalable Reliability Modeling of Si-MOSFET Power Electronics Converters

Real-Time Deep Learning at the Edge for Scalable Reliability Modeling of Si-MOSFET Power Electronics Converters

The Sensitivity of an Electro-Thermal Photovoltaic DC–DC Converter Model to the Temperature Dependence of the Electrical Variables for Reliability Analyses

Computationally Efficient, Real-Time, and Embeddable Prognostic Techniques for Power Electronics

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