failure modes
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Abstract This paper presents the extension and validation of omni-failure envelopes for first-ply failure (FPF) and last-ply failure (LPF) analysis of advanced composite materials under general three-dimensional (3D) stress states. Phenomenological failure criteria based on invariant structural tensors are implemented to address failure events in multidirectional laminates using the “omni strain failure envelope” concept. This concept enables the generation of safe predictions of FPF and LPF of composite laminates, providing reliable and fast laminate failure indications that can be particularly useful as a design tool for conceptual and preliminary design of composite structures. The proposed extended omni strain failure envelopes allow not only identification of the controlling plies for FPF and LPF, but also of the controlling failure modes. FPF/LPF surfaces for general 3D stress states can be obtained using only the material properties extracted from the unidirectional (UD) material, and can predict membrane FPF or LPF of any laminate independently of lay-up, while considering the effect of out-of-plane stresses. The predictions of the LPF envelopes and surfaces are compared with experimental data on multidirectional laminates from the first and second World-Wide Failure Exercise (WWFE), showing a satisfactory agreement and validating the conservative character of omni-failure envelopes also in the presence of high levels of triaxiality.

In this paper, the short-circuit robustness of 1200 V silicon carbide (SiC) trench MOSFETs with different gate structures has been investigated. The MOSFETs exhibited different failure modes under different DC bus voltages. For double trench SiC MOSFETs, failure modes are gate failure at lower dc bus voltages and thermal runaway at higher dc bus voltages, while failure modes for asymmetric trench SiC MOSFETs are soft failure and thermal runaway, respectively. The shortcircuit withstanding time (SCWT) of the asymmetric trench MOSFET is higher than that of the double trench MOSFETs. The thermal and mechanical stresses inside the devices during the short-circuit tests have been simulated to probe into the failure mechanisms and reveal the impact of the device structures on the device reliability. Finally, post-failure analysis has been carried out to verify the root causes of the device failure.

This study seeks to evaluate the effectiveness of glass-FRP-reinforced geopolymer concrete columns integrating hybrid fibres (GFGC columns) and steel bar-reinforced geopolymer concrete columns incorporating hybrid fibres (SFGC columns) under eccentric and concentric loadings. Steel fibre (SF) and polypropylene fibres (PF) are two types of fibres that are mixed into hybrid fibre-reinforced geopolymer concrete (HFRGC). Eighteen circular concrete columns with a cross-section of 300 mm × 1200 mm were cast and examined under axial loading up to failure. Nine columns were cast with glass-FRP rebars, whereas the other nine were cast with steel rebars. Using ABAQUS, a nonlinear finite element model was established for the GFGC and SFGC columns. The HFRGC material was modelled using a simplified concrete damage plasticity model, whereas the glass-FRP material was simulated as a linear elastic material. It was observed that GFGC columns had up to 20% lower axial strength (AST) and up to 24% higher ductility indices than SFGC columns. The failure modes of both GFGC and SFGC columns were analogous. Both GFGC and SFGC columns revealed the same effect of eccentricity in the form of a decline in AST. A novel statistical model was suggested for predicting the AST of GFGC columns. The outcomes of the experiments, finite element simulations, and theoretical results show that the models can accurately determine the AST of GFGC columns.

Gearboxes are widely used in industrial processes as mechanical power transmission systems. Then, gearbox failures can affect other parts of the system and produce economic loss. The early detection of the possible failure modes and their severity assessment in such devices is an important field of research. Data-driven approaches usually require an exhaustive development of pipelines including models’ parameter optimization and feature selection. This paper takes advantage of the recent Auto Machine Learning (AutoML) tools to propose proper feature and model selection for three failure modes under different severity levels: broken tooth, pitting and crack. The performance of 64 statistical condition indicators (SCI) extracted from vibration signals under the three failure modes were analyzed by two AutoML systems, namely the H2O Driverless AI platform and TPOT, both of which include feature engineering and feature selection mechanisms. In both cases, the systems converged to different types of decision tree methods, with ensembles of XGBoost models preferred by H2O while TPOT generated different types of stacked models. The models produced by both systems achieved very high, and practically equivalent, performances on all problems. Both AutoML systems converged to pipelines that focus on very similar subsets of features across all problems, indicating that several problems in this domain can be solved by a rather small set of 10 common features, with accuracy up to 90%. This latter result is important in the research of useful feature selection for gearbox fault diagnosis.