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Quality and reliability optimization design for electromagnetic devices based on various uncertainties and time-dependent sensitivity analysis

机译:基于各种不确定性和时变敏感性分析的电磁装置质量可靠性优化设计

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摘要

Quality and reliability problems in electromagnetic devices (EMDs) have attracted wide attentions from companies in the competitive market. Various uncertainties from manufacture processing, operation environment and degradation path during the lifecycle of EMDs have great impacts on its performance and reliability. Robust design (RD) is one of most effective methods to improve both quality and reliability of EMDs. However, state-of-art RD methods ignore the correlations between various uncertainties and sensitivity analysis method is no longer applicable when the parameters are extended from central values to uncertainties. To better qualify uncertainties in manufacturing and understand the time-dependent derogation path, this paper proposed a method of quality and reliability optimization for EMDs, considering correlated uncertainties in design parameters and degradation path. Rosenblatt transformation is carried out to normalize different-type uncertainties and the results are quantitatively described by the hyper-rectangular method. Afterwards, a lifecycle sensitivity analysis method is proposed to determine the critical design parameter when parameter degradation exists during the operation of EMDs. Then, the optimization model was established considering parameter uncertainties and reliability constraint, and particle swarm algorithm is used to obtain the solution. Finally, the effectiveness of proposed method was verified by a case study of electromagnetic relay in electric vehicles.
机译:电磁设备(EMD)中的质量和可靠性问题已引起竞争激烈的公司的广泛关注。 EMD生命周期中的制造工艺,操作环境和降级路径等各种不确定因素对其性能和可靠性都有很大影响。稳健设计(RD)是提高EMD品质和可靠性的最有效方法之一。但是,最新的RD方法忽略了各种不确定性之间的相关性,并且当参数从中心值扩展到不确定性时,灵敏度分析方法不再适用。为了更好地验证制造中的不确定性并了解时间依赖性减损路径,本文提出了一种EMD的质量和可靠性优化方法,同时考虑了设计参数和降级路径中的相关不确定性。进行Rosenblatt变换以归一化不同类型的不确定性,并通过超矩形方法定量描述结果。然后,提出了一种生命周期敏感性分析方法,以在EMD运行过程中存在参数降级时确定关键设计参数。然后,考虑参数不确定性和可靠性约束,建立了优化模型,并采用粒子群算法求解。最后,以电动汽车电磁继电器为例,验证了该方法的有效性。

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