SAC305焊料PCB在不同温度和振动载荷条件下的RUL估计

P. Lall, Tony Thomas, K. Blecker
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引用次数: 3

摘要

电子封装在不同振动载荷和温度条件下的剩余使用寿命(RUL)估算在计划维护和部件更换方面有多种应用,可以有效地降低电子封装的成本。本研究采用SAC305合金作为焊料合金,采用不同的粒子滤波和时间序列分析技术估计了RUL。测试板为无铅SAC305菊花链CABGA封装,承受25oC、55oC、100oC和155oC的不同温度,承受5g和10g两种振动加速度水平。在所有温度和振动载荷条件下,测试板的振动都进行到其第一固有频率。采用数据采集和信号放大装置,在振动过程中以频繁的时间间隔从测试板的四个不同位置获取应变信号,作为预测故障的参数。现场测量的阻力包也进行了测量,以确定在振动过程中的失效包。利用在振动过程中在板的不同位置以一定的间隔获得的应变信号来寻找能够预测故障的特征向量。采用主成分分析(PCA)作为数据约简技术,对应变信号的时间和频率特征进行约简。利用不同的多元统计技术,从应变信号的时间、频率和频谱内容估计特征向量。将所有的特征向量数据结合在一起,进行不同模式的分析,研究了不同温度和载荷条件下特征向量的变化规律。通过研究二者之间的相关性来了解不同条件下特征向量的变化。可以预测故障的两个主要特征向量包括应变信号在500hz ~ 2000hz范围内的频率和频谱含量以及整个应变信号的瞬时频率。
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RUL Estimations of SAC305 Solder PCB's under Different Conditions of Temperature and Vibration Loads
Remaining Useful Life (RUL) estimation of electronic packages for different conditions of vibration loads and temperatures have various applications in scheduling maintenance and component replacement effectively to reduce the cost of the same. In this study, SAC305 alloy is used as the solder alloy, and the RUL is estimated using different particle filtering and time-series analysis techniques. The test board is a lead-free SAC305 daisy chain CABGA package which is subjected to different temperatures 25oC, 55oC, 100oC and 155oC for two vibration acceleration levels of 5g and 10g. The vibration of the test board is carried out to its first natural frequency for all conditions of temperature and vibration load. Strain signals are acquired using data acquisition and signal amplifying unit from four separate locations of the test board at a frequent time interval during vibration as the parameter used for predicting failure. In-situ measurements of resistance of the packages are also measured to identify the failure of the packages during vibration. The strain signals acquired at regular intervals during vibration at different locations of the board are used to find the feature vectors that can predict failure. Principal component analysis (PCA) is used as the data reduction technique for both time and frequency-based features of the strain signal. Feature vectors are estimated from the time, frequency, and spectral content of the strain signal using different multivariate statistical techniques. The variations in the feature vectors for different conditions of temperature and load is studied by combining all the feature vector data together and analyzing it for different patterns. The correlation of the same is studied to understand the changes in the feature vectors with different conditions. The two major feature vectors that can predict the failure includes frequency and spectral content from 500 Hz to 2000 Hz of the strain signal and the instantaneous frequency of the whole strain signal.
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