In many reliability design and model-based health management applications where load profiles are variable and unpredictable, it is desirable to have efficient cycle counting methods to identify equivalent full and half cycles within the irregular load profile. Conventional cycle-based lifetime models can then be applied directly to provide information about the life consumption of the products. The use of an off-line rainflow algorithm is a common solution for arbitrary loads, but it cannot be applied in real time in its original form. This paper presents an in-line coding algorithm which uses a stack-based implementation, and a recursive algorithm to pick out the equivalent full and half cycles of the irregular load profile. The method can be integrated easily within time-domain or serial data applications to generate equivalent full and half cycles as they occur. Thus it is of particular significance for life estimation in real-time applications where use of the traditional implementations of the counting algorithm is impractical. In comparison with the off-line traditional rainflow method, the on-line method doesn't require any knowledge of the time history of the load profile because it processes each minimum or maximum when it occurs. Therefore, it provides a more efficient cycle counting method using less memory storage, and making more efficient use of computational resources within the real-time environment.

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A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription.For more information, please contact eprints@nottingham.ac.ukManuscript ID TPEL-Reg-2014-04-0475.R2 1  Abstract-Power electronics are efficient for conversion and conditioning of the electrical energy through a wide range of applications. Proper life consumption estimation methods applied for power electronics that can operate in real-time under inservice mission profile conditions will not only provide an effective assessment of the products life expectancy but also they can deliver reliability design information. This is important to aid in manufacturing and thus help in reducing costs and maximizing through-life availability. In this paper, a mission profile based approach for real-time life consumption estimation which can be used for reliability design of power electronics is presented. The paper presents the use of electro-thermal models coupled with physics-of-failure analysis by means of real-time counting algorithm to provide accurate life consumption estimations for power modules operating under in-service conditions. These models, when driven by the actual mission profiles, can be utilized to provide advanced warning of failures and thus deliver information that can be useful to meet particular application requirements for reliability at the design stage. To implement this approach, an example of two case studies using mission profiles of a metro-system and wind-turbines applications are presented.

Health management and reliability form a fundamental part of the design and development cycle of electronic products. In this paper compact real-time thermal models are used to predict temperatures of inaccessible locations within the power module. These models are then combined with physics of failure based reliability analysis to provide in-service predictions of crack propagation in solder layers and at the bond wire joints as a result of thermal cycling. The temperature estimates are combined with lifetime based reliability models to provide a tool for life consumption monitoring. Rainflow counting algorithms are applied to the temperature vs. time data to extract the occurrence frequencies of different thermal cycling ranges. Knowledge of the life consumed for each different cycle then allows the remaining life time to be estimated under arbitrary operational conditions. The technique can be employed to provide functions such as life consumption monitoring and prognostic maintenance scheduling.

A real-time prognostic tool to predict life-time of IGBT power modules in a metro application is presented. Applying conventional life models (e.g. Coffin-Manson) for real applications is infeasible because these models are only applicable to cyclic data. Use of off-line rainflow algorithm is common solution but cannot be applied in real-time in its original form. This paper presents on-line life-estimation of the power modules using real-time rainflow coding algorithm. This technique is applied to an example metro application that requires use of cycle counting for an arbitrary load profile. The proposed method uses a stack-based implementation which employs a recursive algorithm to identify full and half cycles of the temperatures obtained as outputs from real-time compact thermal models. This then allows life-time models to be used to provide life consumption estimates. This method provides less complexity and more accurate on-line prediction for the studied module's failure mechanisms.