Alireza Alghassi

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

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.

Developing Prognostic Models Using Duality Principles for DC-to-DC Converters

Within the field of Integrated System Health Management, there is still a lack of technological approaches suitable for the creation of adequate prognostic model for large applications whereby a number of similar or even identical subsystems and components are used. Existing similarity among a number of different systems, which are comprised of similar components but with different topologies, can be employed to assign the prognostics of one system to other systems using an inference engine. In the process of developing prognostics, this approach will thereby save resources and time. This paper presents a radically novel approach for building prognostic models based on system similarity in cases where duality principle in electrical systems is utilized. In this regard, unified damage model is created based on standard Tee/Pi models, prognostics model based on transfer functions, and remaining useful life (RUL) estimator based on how energy relaxation time of system is changed due to degradation. An advantage is that the prognostic model can be generalized such that a new system could be developed on the basis and principles of the prognostic model of other systems. Simple electronic circuits, dc-to-dc converters, are to be used as an experiment to exemplify the potential success of the proposed technique validated with prognostics models from particle filter.

Prognostic capability evaluation of power electronic modules in transportation electrification and vehicle systems

Health management and reliability are fundamental part of the design and development cycle of the power electronic products. This paper presents the study and investigation about the prognostic and health management of power electronic IGBT module. To achieve this aim, a fusion approach has been introduced. In addition, for the physic based part of approach, a thermal model is used to predict temperatures of inaccessible locations within the power module. Then by employing the rainflow algorithm, the remaining life of the power electronic IGBT module has been determined.

A simple state-based prognostic model for predicting remaining useful life of IGBT power module

Health management and reliability are fundamental aspects of the design and development cycle of power electronic products. This paper presents the prognostic evaluation of a power electronic IGBT module. To achieve this aim, a simple state-based prognostic (SSBP) method has been introduced and applied on the data which was extracted from an aged power electronic IGBT and its remaining useful life was determined.

Relibility enhance powertrain using ANFIS base prognostics model

In the past decades power electronics have become more interested devices for underpinning research towards the feasibility of new generation of electrical vehicle (EV) which helping to reduce the reliance on fossil fuels. Power electronic semiconductor devices play an important role in power electronic converter and inverter and rectification systems and design enhance the efficiency of EV performance as well as lowering the cost of electric power propulsion systems. The aim of this paper is to develop a prognostics capability for estimating remaining useful life (RUL) of power electronics components. There is a need for an efficient prognostics algorithm that is embeddable and able to improve on the current prognostic models. A positive aspect of this approach is that the IGBT failure model develops using fuzzy logic adapts prognostic model with the fuzzy nature of failure mechanism. Actually, this method is like adaptive neuro-fuzzy inference system (ANFIS). We also compare the results from the proposed prognostic model with stochastic Monte-Carlo approach which can efficiently estimate the remaining useful life of Insulated Gate Bipolar Transistor (IGBT). The RUL (i.e. mean and confident bounds) is then calculated from the simulated of the estimated degradation states to support on-board real-time decision-making. The prognostics results are evaluated using RMSE prognostics evaluation metrics.

Principle of Duality on Prognostics

The accurate estimation of the remaining useful life (RUL) of various components and devices used in complex systems, e.g., airplanes remain to be addressed by scientists and engineers. Currently, there area wide range of innovative proposals put forward that intend on solving this problem. Integrated System Health Management (ISHM) has thus far seen some growth in this sector, as a result of the extensive progress shown in demonstrating feasible and viable techniques. The problems related to these techniques were that they often consumed time and were too expensive and resourceful to develop. In this paper we present a radically novel approach for building prognostic models that compensates and improves on the current prognostic models inconsistencies and problems. Broadly speaking, the new approach proposes a state of the art technique that utilizes the physics of a system rather than the physics of a component to develop its prognostic model. A positive aspect of this approach is that the prognostic model can be generalized such that a new system could be developed on the basis and principles of the prognostic model of another system. This paper will mainly explore single switch dc-to-dc converters which will be used as an experiment to exemplify the potential success that can be discovered from the development of a novel prognostic model that can efficiently estimate the remaining useful life of one system based on the prognostics of its dual system.

Unified IGBT prognostic using natural computation

Within the field of Integrated System Health Management, ISHM, there is still a lack of adequate prognostic models for critical applications while systems are missioned to work in harsh environments. One of the main challenges is that prognostic models should be adaptive to changes in working conditions so that prediction of the remaining useful life of systems/components can be well adjusted with the dynamic of variation of both system and working conditions. Among various modeling and prediction techniques, natural computation and soft computing techniques, such as neural networks, offer interesting solutions to adjust prediction of the remaining useful life of systems/components while the complexity of modeling and real-time calculation is also reduced. This paper presents a radically-novel approach for building per-unit prognostic models applied on one of the most critical components of power modules, IGBT, that presents high failure rates in power electronic systems. An advantage is that the prognostic model can be generalized in a per-unit form; and then, its features are adjusted depending on the application, working condition, and dynamic of changes.

Reliability enhanced EV using pattern recognition techniques

The following paper will contribute to the development of novel data transmission techniques from an IVHM perspective so that Electrical Vehicles (EV) will be able to communicate semantically by directly pointing out to the worst failure/threat scenarios. This is achieved by constructing an image-based data communication in which the data that is monitored by a vast number of different sensors are collected as images; and then, the meaningful failure/threat objects are transmitted among a number of EVs. The meanings of these objects that are clarified for each EV by a set of training patterns are semantically linked from one to other EVs through the similarities that the EVs share. This is a similar approach to wellknown image compression and retrieval techniques, but the difference is that the training patterns, codebook, and codewords within the different EVs are not the same. Hence, the initial image that is compressed at the transmitter side does not exactly match the image retrieved at the receivers side; as it concerns both EVs semantically that mainly addresses the worst risky scenarios. As an advantage, connected EVs would require less number of communication channels to talk together while also reducing data bandwidth as it only sends the similarity rates and tags of patterns instead of sending the whole initial image that is constructed from various sensors, including cameras. As a case study, this concept is applied to DC-DC converters which refer to a system that presents one of the major problems for EVs.