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High strength aluminium alloy fatigue damage alert of high speed train gearbox shell using acoustic emission instrument

机译:高速铝合金疲劳损伤损伤高速列车齿轮壳的原声发射仪器

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摘要

As a key component of high speed train, the gearbox shell must be running safely. The main damage form of high speed train gearbox shell is fatigue, and to effectively predict the working state and give out safety alert is of great significance of operation safety. In this study, the acoustic emission instrument has been used for real-time and non-destruction monitoring fatigue damage progress of high strength aluminium alloy which is the material of high speed train gearbox shell. By comparing with the fatigue damage progress, the feature parameter and its threshold of acoustic emission (AE) signal for classifying the states has been defined. The consistence of the feature is discussed by Hurst index method. A particle swarm optimisation-least square support vector machines (PSO-LSSVM) prediction model has been designed to predict the feature of next step, and the safety alert is given by comparing with the threshold of the feature. In this study, the prediction result is about 600s to 1600s earlier than the critical time, and by comparing acceleration test and real condition, it can give enough time for the train to stop and evacuate passengers.
机译:作为高速列车的关键部件,齿轮箱外壳必须安全运行。高速列车变速箱壳的主要损伤形式是疲劳,有效地预测工作状态,发出安全警报具有重要意义安全性。在本研究中,声发射仪已用于实时和非销毁监测监测疲劳损伤的高强度铝合金的损伤进展,这是高速列车齿轮箱壳的材料。通过比较疲劳损伤进度,已经定义了用于分类状态的声发射(AE)信号的特征参数及其阈值。赫斯特索引方法讨论了该特征的一致性。粒子群优化 - 最小二乘支持向量机(PSO-LSSVM)预测模型被设计为预测下一步骤的特征,并且通过与特征的阈值进行比较给出安全警报。在这项研究中,预测结果比临界时间更早为600岁至1600岁,并且通过比较加速试验和实际情况,它可以给火车停止和疏散乘客的足够的时间。

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