Fatigue failure of insulated gate bipolar transistor modules (IGBTs) packages under low-amplitude temperature swings is of great significance for the reliability evaluation of IGBTs operating in actual power electronic devices. In this article, failure mechanism of die-attach joints in IGBTs under normal operating and accelerated aging was comparably investigated by a three-dimensional electro-thermal-mechanical coupled model. Results indicated that local viscoplastic deformation of solder alloys around stress concentrated areas caused by material microdefects is the root cause of fatigue of die-attach joints under low-amplitude temperature swings. Fatigue cracks can only initiate and propagate at those plastically deformed areas (activation points). Fatigue of die-attach joints is co-determined by the number of activation points and crack growth rate. Comparably, the whole die-attach solder is in viscoplastic deformation under accelerated aging and the number of activation points has reached its saturation. Fatigue of die-attach joints under accelerated aging is only determined by the crack growth rate. Due to the difference in failure mechanism, it is questionable to directly extend conventional life models to normal operating conditions. Accordingly, an energy-based physical life model for die-attach joints in IGBTs under low-amplitude temperature swings was proposed.
The evaluation of dynamic degradation in electrothermal characteristics of insulated gate bipolar transistors (IGBTs) caused by package fatiguing is critical to the operating safe and reliable application of IGBTs under their long-term servicing. In the present work, the degradation mechanism in electro thermal characteristics of IGBTs caused by package fatiguing (die-attach solder cracking and Al-wires lifting-off) was analyzed, first. An iterative looping consisting of the electrothermal simulation, fatigue damage calculation, and degradation of electrothermal characteristics in IGBTs was proposed based on the physics of failure. Based on that, the degradation in electrothermal characteristics of IGBTs during power cycling (PC) caused by the two fatigue modes was investigated. PC tests were carried out to 1700 V/3600 A IGBT modules under various conditions for verifications. Under six various PC conditions, the maximum of errors is lower than 7%. It indicates well consistence with experiments. The proposed iterative looping directly simulates the degradation of electrothermal characteristics of IGBTs during thermal cycling. It may provide a possibility of evaluating offline or online the important electrothermal parameters of IGBTs (such as Tj and VCEsat ) during operating, which is meaningful for diagnosing the safe operating area of IGBTs after long-term servicing.
Both rotor aerodynamic characteristics and structural performance of the blade are critical to the wind turbine system service life; an accurate loading model of the blade is extraordinary complex due to the complexity of the geometry shape and variety of blade thickness. In this paper, a 10KW fixed-pitch variable-speed wind turbine blade with five different thickness of aerofoil shape along the span of the blade is presented as a case study, main parameters of the wind turbine rotor and the blade aerodynamic geometry shape are determined based on the principles of the blade element momentum (BEM) theory, a specific blade internal structure and layup schedule are designed. Based on the FE method, deflections and strain distributions of the designed blade under extreme wind conditions are numerically predicted. Theoretical and numerical results indicate that aerodynamic characteristics of the designed blade meet the requirement, the tip clearance is sufficient to prevent collision with the tower, and the blade material is linear and safe.
Drivers passing through an extra-long tunnel are confined in an airtight, low-luminance, monotonous light environment for a long time. That can cause visual overload and delayed reaction times, seriously threatening driving safety. To investigate the influence of the light environment on traffic safety in extra-long tunnels with increased length, five tunnels were studied: the Qinlingzhongnanshan (18 km), Chengkai (11.5 km), Wujialiang (6.6 km), Shuanghekou (3.0 km), and Caishenliang (634 m) tunnels. Eighteen drivers were selected to perform real vehicle experiments. An eye movement meter and a physiological instrument recorded four types of eye movements and physiological parameters, including pupil diameter, blink duration, root-mean-square of successive differences (RMSSD), and (α+θ)/β. The effects of the light environment in tunnels of different lengths on drivers were evaluated. Our results showed that as driving time in extra-long-tunnel light environments increased, pupil diameter, blink duration, and (α+θ)/β increased, and RMSSD values decreased slightly. Those results indicated that a driver’s cardiac physiological load increases with driving time in tunnels. However, when drivers entered special light belt, their pupil diameter decreased, and RMSSD value increased, but both returned to their previous level when the vehicles left the belt. Overall, we found that increasing luminance and/or improving the monotonous environment in tunnels longer than 6 km can improve traffic safety.
In high speed machining superalloys processes, tool wear is strongly influenced by the cutting temperature and contact stresses. Finite element analysis of machining can be used as a supplementary to the physical experiment, this paper provides investigations in 2D and 3D finite element modeling and simulation of prestressed cutting for GH4169 superalloy, a tool wear model for the specified tool and workpiece pair is developed based on the Usui's wear model, furthermore, tool temperature, wear rate and nodal displacement on the face of tool in prestressed cutting of superalloy is analyzed under various prestress condition and cutting parameters, and Python language is adopted to modify the Abaqus code used to allow tool wear calculation and tool geometry updating. The results of the simulation indicate that the tool wear rate increases with the increase of cutting time, and the influence of the prestress to tool wear in prestressed cutting process of shaft part is unremarkable.
The accuracy of data-driven open-circuit fault diagnosis methods is affected by varying operating conditions. This issue is often ignored. In this paper, an improved convolutional neural network (CNN) and a sample amplification method are proposed to eliminate the influence of varying operating conditions on the online open-circuit fault (OCF) diagnosis for neutral point clamped (NPC) inverter. Firstly, 73 types of open-circuit fault sample collection can be greatly reduced to 14 by following the sample amplification method. The signals of any phase can be generated by a single fundamental period signal. This provides a significant savings in sample collection time. Secondly, the spatial attention mechanism (SAM) is added after the first convolutional layer of the CNN model. The feature extraction capability of the model is enhanced for time-domain waveform scaling under variable operating conditions. Simultaneously, the last full connection (FC) layer of the CNN model is retained and the other FC layers are substituted with a global maximum pooling (GMP) layer. This has the advantage of reducing the number of network parameters and further conserving the effective feature information. In conclusion, the experimental results show that the sample amplification method and the improved CNN model for online fault diagnosis under varying operating conditions exceed 99% accuracy. The SAMCNN-GMP is more effective and stable than the CNN model.
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