Accurate testing history data is necessary for all fatigue life prediction approaches, but such data is always deficient especially for the microelectronic devices. Additionally, the sequence of the individual load cycle plays an important role in physical fatigue damage. However, most of the existing models based on the linear damage accumulation rule ignore the sequence effects. This paper proposes a thermal fatigue life prediction model for ball grid array (BGA) packages to take into consideration the load sequence effects. For the purpose of improving the availability and accessibility of testing data, a new failure criterion is discussed and verified by simulation and experimentation. The consequences for the fatigue underlying sequence load conditions are shown.

Aluminum alloy is widely used in aerospace structures. However, it often suffers from a harsh corrosion environment, resulting in different damage such as pitting corrosion, which leads to a reduction in the service life of aerospace structures. In the present study, the pitting corrosion with a radius of 1 mm and a depth of 0.6 mm was manufactured using hydrofluoric (HF) acid on a 2024-T3 aluminum alloy plate (400 mm × 400 mm × 2 mm) to simulate the corrosion state of equipment. A signal acquisition system with a square sensor network of 12 piezoelectric transducers (PZTs) was established. The sensor path weighting reconstruction algorithm for the probabilistic inspection of defects (SPW-RAPID) is proposed based on corrosion damage characteristic parameters including signal correlation coefficient (SDC), root mean squared error (RMSE), and signal energy damage index (E1) to explore the monitoring efficacy of pitting corrosion. The sensor path weight w, which is the product of value coefficient a and impact factor l, is established to modify the corrosion damage characteristic parameters. The results indicate that the SPW-RAPID algorithm can improve the accuracy and clarity of image reconstruction results based on SDC, RMSE and E1, which can locate the pitting corrosion with a radius of 1 mm and a depth of 0.6 mm, and the positioning error is controlled within 0.1 mm. The research work may provide an available way to monitor tiny corrosion damage on an aluminum alloy structure.

Plated through hole (PTH) plays a critical role in printed circuit board (PCB) reliability. Thermal fatigue deformation of the PTH material is regarded as the primary factor affecting the lifetime of electrical devices. Numerous research efforts have focused on the failure mechanism model of PTH. However, most of the existing models were based on the one-dimensional structure hypothesis without taking the multilayered structure and external pad into consideration. In this paper, the constitutive relation of multilayered PTH is developed to establish the stress equation, and finite element analysis (FEA) is performed to locate the maximum stress and simulate the influence of the material properties. Finally, thermal cycle tests are conducted to verify the accuracy of the life prediction results. This model could be used in fatigue failure portable diagnosis and for life prediction of multilayered PCB.

According to statistics, most of the electronic equipment fault cases are related to the analog circuit, which may lead to the failure of the whole system due to the drift or performance degradation of the parameters of an electronic component in the analog circuit. However, because of the continuity of the parameters of each component, the lack of test nodes and the nonlinearity problems, the analog circuit becomes more difficult to prognose. The fluctuation and inconspicuousness of the degradation trend in analog circuits lead to the difficulty in extracting characteristic parameters and low accuracy of fault prediction. Therefore, a fault prediction method for analog circuits based on Multi-scale and Long-Short Term Memory (MLSTM) is proposed in this paper. Multi-scale feature learning method can divide the original data into different scales to improve the learning accuracy of small sample data. LSTM can well identify the temporal sequence of the widely separated events in the noise input stream. In this paper, the multi-scale feature learning method is combined with LSTM to improve the accuracy of fault prediction. Finally, the DC-DC switching power supply is taken as a case for verification, and the proposed method is compared with other algorithms to verify its accuracy and applicability.

Based on VC dimension theory and structural risk minimization principle of statistical learning theory, Support vector machine (SVM) has a prominent advantage in solving classification and fault prediction problems, specifically suitable for small sample, nonlinear and high dimensional pattern recognition problems. However, SVM is originally created for solving binary classification problems. The efficient application of SVM on multi-classification has always been a hotspot. This paper represents a novel approach to the multi-class fault diagnosis based on support vector machine of particle swarm optimization method. Besides the one-against-one, one-against-other, directed acyclic graph and binary tree, the Huffman tree is introduced, and the priority of the classification is determined by calculating dissimilarity degree of each two class. Thus, a multi-classification model based on Huffman tree is built. When the sample amount of each class varies greatly, using the same penalty parameter for each class will lower the classification accuracy. Thus, the penalty parameters of different class is optimized by particle swarm optimization method, which guarantee each SVM is the optimal result. Finally, a database of power transformer is used to demonstrate the superiority of this new method.