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This paper explores the application and development trends of artificial intelligence (AI) technology, particularly machine learning and natural language processing in the field of failure analysis. Failure analysis is a crucial method for ensuring the reliability and safety of equipment, and is widely used in aerospace, automotive manufacturing, electronic devices, and other fields. Traditional failure analysis methods often rely on expert experience, which is time-consuming and laborious. By integrating AI’s powerful data processing capabilities with traditional methods, the accuracy and efficiency of analysis have been significantly enhanced. In terms of failure mode diagnosis, AI can rapidly and accurately identify various fault modes and provide precise diagnostic results. In failure cause diagnosis, AI integrates data from multiple sources to uncover complex failure factors and potential causal relationships, improving diagnostic reliability. In failure prediction, machine learning can accurately forecast material lifespan and strength, reducing experimental time and costs. In failure prevention, AI offers new approaches to effectively reduce the risk of failure and lower product maintenance costs. The paper also looks forward the future development prospects of AI in failure analysis and highlights challenges and recommendations in the areas, such as data quality improvement, model optimization, interdisciplinary collaboration, and ethical and safety issues.

Aviation aircraft will be subjected to a large number of complex random vibration loads during service， and its structure is prone to vibration fatigue，which leads to damage or even failure，which can cause serious losses. The stress intensity factor（SIF）solution method for cracked beams under random vibration loading is investigated，and a time-domain method based on Hudson's theory is proposed to estimate the vibration fatigue crack growth life by combining with the Paris formula. The theoretical analysis and vibration fatigue test are conducted on a typical cantilever beam as an object. The results show that the life prediction results of the proposed time-domain method based on the Hudson's theory is better than that of the experimental estimation results，which can verify that the method can effectively describe the random vibration crack growth behavior.

To reduce the losses caused by aging failure of insulation gate bipolar transistor (IGBT), which is the core components of nuclear power plant rod position indicating and rod control (RPC) system. It is necessary to conduct studies on its life prediction. The selection of IGBT failure characteristic parameters in existing research relies heavily on failure principles and expert experience. Moreover, the analysis and learning of time-domain degradation data have not been fully conducted, resulting in low prediction efficiency as the monotonicity, time correlation, and poor anti-interference ability of extracted degradation features. This paper utilizes the advantages of the stacked denoising autoencoder(SDAE) network in adaptive feature extraction and denoising capabilities to perform adaptive feature extraction on IGBT time-domain degradation data; establishes a long-short-term memory (LSTM) prediction model, and optimizes the learning rate, number of nodes in the hidden layer, and number of hidden layers using the Gray Wolf Optimization (GWO) algorithm; conducts verification experiments on the IGBT accelerated aging dataset provided by NASA PCoE Research Center, and selects performance evaluation indicators to compare and analyze the prediction results of the SDAE-LSTM model, PSO-LSTM model, and BP model. The results show that the SDAE-LSTM model can achieve more accurate and stable IGBT life prediction.

As the core component of strip mills,roll consumption has an essential influence on the production cost.With the increasing demand for cost reduction and efficiency increase caused by the intensifying competition of iron and steel enterprises,the recycling of roll re-manufacturing has been gradually promoted.It is one of the main ways to transform the largesize roll of the large rolling mill to a small rolling mill after its work layer consumption.For rolls with local size,especially those with insufficient shoulder size,casting lengthening is usually adopted to meet the needs of further use.The service life of the roll after casting joint repair and re-manufacturing becomes the key to ensuring safe production.Because of the above problems,the fatigue performance test was carried out on the roll casting part sample.At the same time,the torsional fatigue model was used to predict the life of the roll neck after the repair of the work roll based on proving the calculation accuracy and combining it with the load spectrum established based on the field production data.The expected service life met the performance requirements of the repaired roll on the site.It can provide theoretical support for establishing an on-site roll safety maintenance system.

Highlights Fiber hybrid with bundle‐by‐bundle dispersion can achieve the high performance of composites, fully understand the hybrothermal resistance of hybrid composites is the key to prove the applications in engineering structures. In the present paper, the hygrothermal resistance performances of new type carbon and glass fiber reinforced epoxy based hybrid rod with bundle‐by‐bundle dispersion produced by pultrusion technology is investigated experimentally. The tests of water absorption and desorption, thermal and mechanical performances are performed to obtain the evolution rules of hygrothermal aging. As a result, the water absorption of hybrid rod confirms to the Fick's diffusion behavior, the serious relaxation of resin matrix and the interfacial debonding of fiber‐resin exposed at high temperature provide more diffusion space for water molecules. Long‐term hydrothermal aging results in the recoverable plasticization of resin matrix and irrecoverable interfacial debonding of fiber‐resin, which brings about the degradation of 6.7%–15.0% for short beam shear strength (SBSS) retention. In addition, the plasticization effect reduces the cross‐linking density of resin matrix, which leads to the degradation of 3.3%–15.6% for glass transition temperature (Tg) retention. The life prediction results show that degradation rate of SBSS is gradually slow down and reached to a stable retention of 85.5%. Relaxation of resin matrix and interfacial debonding accelerate the diffusion of water molecules. Plasticization and interfacial debonding lead to the degradation of short beam shear strength retention and glass transition temperature. Long‐term life prediction shows that short beam shear strength e retention is 85.5%. [ABSTRACT FROM AUTHOR]

Components created with five‐axis machining show a multi‐scale surface character due to cusps created on the surface and feed and tool marks within the cusps. Therefore, it becomes difficult to incorporate the effects of surface character on fatigue life for such components. In this work, an Forschungskuratorium Maschinenbau (FKM) guideline is adapted to develop a fatigue prediction model which considers cusps as notches and marks within the cusps as surface roughness (characterized by parameter R10z). The assessment uses stresses obtained from an finite element analysis model to predict the fatigue life of components whilst considering stress concentration, stress gradient, mean stress, and surface roughness effects. When cusps are regarded as surface roughness within the conventional FKM approach, fatigue life is considerably underestimated. In comparison, fatigue life predictions that take into consideration the roughness within cusps and treat cusps as stress‐raising notches are closer to experimental life. [ABSTRACT FROM AUTHOR]

To assess the contribution of residual stress to the improvement of high‐cycle fatigue (HCF) properties, HCF tests are performed on Ti‐6Al‐4V specimens with different residual stress levels but no other evident surface state modification induced by machining. Results indicate that compressive residual stress induced by machining significantly improves the HCF performance of Ti‐6Al‐4V specimens. Of all the specimens, the fatigue strength of specimens with a surface residual stress level of −400 MPa is increased by approximately 16.4%. Furthermore, to accurately predict the fatigue life based on three mean stress correction equations (e.g., Walker, Goodman, and Smith–Watson–Topper), the method of reducing surface residual stress by a reduction factor and then adding to the mean stress is proposed. A reduction factor of 0.55 is found to be appropriate for the alloy under the test condition, with majority of the predicted data falling within a factor of three scatter band. Highlights: Isolate residual stress from other surface integrity parameters by machining.Compressive residual stress improves both the fatigue life and strength.Effect of residual stress is considered by introducing a reduction factor in mean stress.Develop a life prediction method based on mean stress correction model. [ABSTRACT FROM AUTHOR]

This paper aims to determine the relationship between thermodynamic entropy generation and fatigue crack propagation life of superalloy GH4169 at 300–650°C. The entire specimen was considered as the thermodynamic system. The plastic energy dissipation in the crack tip was obtained by finite element simulation utilizing the Chaboche nonlinear hardening model. Then the cyclic entropy generation rate (CEGR) and the accumulated entropy generation are calculated by combining simulation and experimental methods. Results show that the CEGR is a power function of the stress intensity factor range, and it is almost a constant at fatigue failure. The fatigue fracture entropy (FFE) increases as fatigue cycles at failure increase at constant temperature, but it first decreases and then increases when temperature increases from 300 to 650°C. A fatigue life prediction model based on the thermodynamic damage parameter is established and verified by comparison with experimental results and available data in the literature. Highlights: Thermodynamics provides a new thought to the study of elevated temperature crack growth.The Chaboche nonlinear hardening model is considered in entropy generation calculation.An entropy‐based damage parameter can represent the damage of a thermodynamic system.Entropy‐based life prediction model can acceptably predict the crack propagation life. [ABSTRACT FROM AUTHOR]

The Proton Exchange Membrane Fuel Cell (PEMFC) is a fast-developing battery technology, and the key to its reliability and lifespan improvement lies in the accurate assessment of durability. However, the degradation mechanism of the PEMFC is hard to determine and its internal parameters are highly coupled. Thus, the development of a more accurate life prediction model that meets the actual scenarios is to be investigated urgently. To solve this problem, a multi-feature fusion life prediction method based on the Temporal Convolutional Network-Gated Recurrent Unit (TCN-GRU) is proposed. A TCN algorithm is used as the prediction base model, and two GRU modules are included with the model to strengthen the model's expression ability and improve its predictive accuracy. Two widely recognized datasets and two operating conditions are utilized for model training and prediction, respectively. Comparisons are made with single-feature parameter models in terms of Root Mean Square Error (RMSE) and the Determination Coefficient (R2). The results show that the prediction accuracy of the TCN-GRU multi-feature fusion model is higher than that of the single-feature models in terms of stability and anti-interference under both operating conditions. The accuracy of the TCN-GRU (three-feature) model is the most optimal in a steady-state condition at 80% of the training set ratio (RMSE = 3.27 × 10−3, R2 = 0.965). Furthermore, with the increase in the input feature parameter, the TCN-GRU model is closer to the real value, which proves once again that the proposed model can meet the accuracy requirements of the life prediction of the PEMFC. [ABSTRACT FROM AUTHOR]

To ensure the safe operation of equipment, it is necessary to conduct health monitoring and life assessment for key steel structures susceptible to damage. Firstly, nonlinear ultrasonic guided waves, which offer high detection efficiency and sensitivity to micro-damage, are used for both off-line and on-line nonlinear detection methods to assess the fatigue damage of Q460 high-strength steel. The relationship between nonlinear ultrasonic parameters and fatigue life curve is established. Secondly, a fatigue life evaluation method for Q460 high-strength steel is established based on the relationship curve between nonlinear ultrasonic parameters and fatigue life. Both off-line and on-line experimental results show that the nonlinear ultrasonic parameter increase first and then decrease with the fatigue life, and the peak value appears when the fatigue life is near 60%. [ABSTRACT FROM AUTHOR]

Copyright of Electronic Components & Materials is the property of Electronic Components & Materials and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The contents of waste glass powder (WGP) (0%, 10%, 15%, 20%, 25%) and water-binder ratio (W/C) (0.24, 0.26, 0.28) were used as influencing factors, and the quality loss rate (Δm) and compressive strength loss rate (Δfc) were used as characterization parameters. The Ca/Si ratio and main element contents of C-S-H gels with different WGP content were investigated by energy dispersive spectrometry (EDS). The pore structure evolution characteristics of WGP composite cementing materials were investigated by low field nuclear magnetic resonance (NMR). Using Δfc as the index of frost resistance degradation and Weibull function, the frost resistance degradation of glass doped pervious concrete (WGP-PC) was modeled. The results show that, with WGP, for the same number of cycles, Δm and Δfc decrease and increase with the increase of WGP. Under the same WGP content, Δm and Δfc decrease first and then increase with the increase of W/C. After 100 freeze-thaw cycles, the samples with WGP content of 20% and W/C of 0.26 have the best freeze-resistance. Microscopic tests show that with the increase of WGP content, the Ca/Si ratio of C-S-H gel decreases at first and then increases with the increase of WGP content. The extreme value of Ca/Si is 2.36 when WGP is added by 20%. The pore volume of hardened paste with 20% WGP content decreased by 18.6% compared with that of cement system without WGP. The overall compactness of the specimen was improved. On the basis of the test data, a life prediction model was established according to Weibull function. The experiment showed that Δfc could be used as a durability degradation index, and the slope of the reliability curve became gentle after WGP was added, which reduced the damage degradation rate of PC. W/C was 0.26. It's about 5 000 hours. [ABSTRACT FROM AUTHOR]

In recent decades, in order to replace traditional synthetic polymer composites, engineering research has focused on the development of new alternatives such as green biocomposites constituted by an eco-sustainable matrix reinforced by natural fibers. Such innovative biocomposites are divided into two different typologies: random short fiber biocomposites characterized by low mechanical strength, used for non-structural applications such as covering panels, etc., and high-performance biocomposites reinforced by long fibers that can be used for semi-structural and structural applications by replacing traditional materials such as metal (carbon steel and aluminum) or synthetic composites such as fiberglass. The present research work focuses on the high-performance biocomposites reinforced by optimized sisal fibers. In detail, in order to contribute to the extension of their application under fatigue loading, a systematic experimental fatigue test campaign has been accomplished by considering four different lay-up configurations (unidirectional, cross-ply, angle-ply and quasi-isotropic) with volume fraction Vf = 70%. The results analysis found that such laminates exhibit good fatigue performance, with fatigue ratios close to 0.5 for unidirectional and angle-ply (±7.5°) laminates. However, by passing from isotropic to unidirectional lay-up, the fatigue strength increases significantly by about four times; higher increases are revealed in terms of fatigue life. In terms of damage, it has been observed that, thanks to the high quality of the proposed laminates, in any case, the fatigue failure involves the fiber failure, although secondary debonding and delamination can occur, especially in orthotropic and cross-ply lay-up. The comparison with classical synthetic composites and other similar biocomposite has shown that in terms of fatigue ratio, the examined biocomposites exhibit performance comparable with the biocomposites reinforced by the more expensive flax and with common fiberglass. Finally, appropriate models, that can be advantageously used at the design stage, have also been proposed to predict the fatigue behavior of the laminates analyzed. [ABSTRACT FROM AUTHOR]

To advance the technology of Certification by Analysis (CbA), as called for by the aerospace industry, the fatigue problems of SAE keyhole specimens are analyzed to demonstrate a subcase of CbA. First, phenomena identification and ranking table (PIRT) analysis is performed. Second, modeling of the key phenomena is conducted, and finally, verification and validation with the experimental results are achieved. In particular, the elastic/elastoplastic stress distributions in the keyhole specimens are obtained using the finite element method (FEM). Plasticity correction for stress/strain at the notch root is made using the modified Neuber's rule along with the Ramberg–Osgood equation. The low cycle fatigue (LCF) crack nucleation life is analytically predicted using the modified Tanaka–Mura model, a.k.a. the TMW model, given the material's elastic modulus, Poisson's ratio, Burgers vector, and surface energy, without the need for coupon fatigue data regression. The Tomkins equation is used to simulate plastic crack growth within the notch plastic zone. The above analytical life predictions are validated against the SAE keyhole specimen tests, becoming the first successful case of fatigue CbA at a sub-element level. [ABSTRACT FROM AUTHOR]

A bi-Gamma distribution model is proposed to determine the probability density function (PDF) of broadband non-Gaussian random stress rainflow ranges during vibration fatigue. A series of stress Power Spectral Densities (PSD) are provided, and the corresponding Gaussian random stress time histories are generated using the inverse Fourier transform and time-domain randomization methods. These Gaussian random stress time histories are then transformed into non-Gaussian random stress time histories. The probability density values of the stress ranges are obtained using the rainflow counting method, and then the bi-Gamma distribution PDF model is fitted to these values to determine the model's parameters. The PSD parameters and the kurtosis, along with their corresponding model parameters, constitute the neural network input–output dataset. The neural network model established after training can directly provide the parameter values of the bi-Gamma model based on the input PSD parameters and kurtosis, thereby obtaining the PDF of the stress rainflow ranges. The predictive capability of the neural network model is verified and the effects of non-Gaussian random stress with different kurtosis on the structural fatigue life are compared for the same stress PSD. And all life predicted results were within the second scatter band. [ABSTRACT FROM AUTHOR]

Tensile and low-cycle fatigue tests of high-strength compacted graphite cast iron (CGI, RuT450) were carried out at 25 °C, 400 °C, and 500 °C, respectively. The results show that with the increase in temperature, the tensile strength decreases slowly and then decreases rapidly. The fatigue life decreases, and the life reduction increases at high temperature and high strain amplitude. The oxide layer appears around the graphite and cracks at high temperature, and the dependence of crack propagation on ferrite gradually decreases. With the increase in strain amplitude, the initial cyclic stress of compacted graphite cast iron increases at three temperatures, and the cyclic hardening phenomenon is obvious. The fatigue life prediction method based on the energy method and damage mechanism for compacted graphite cast iron is summarized and proposed after comparing and analyzing a large amount of fatigue data. [ABSTRACT FROM AUTHOR]

Introduction: Accurately predicting the remaining mechanical equipment is of great significance for ensuring the safe operation of the equipment and improving economic efficiency. Methods: To accurately assess the mechanical equipment degradation, predict its remaining useful life, and ensure efficient, stable, and safe operation, a degradation and life prediction model for mechanical equipment based on multivariate stochastic processes is studied. The study innovatively predicts the remaining life of mechanical equipment using multivariate stochastic processes, and facilitates the correlation analysis between performance indicators based on the characteristics of Copula functions. Results and discussion: The results showed that the Root Mean Squared Error value of the prediction results based on the trivariate Wiener process was 2.58, which decreased by 46.91% and 35.82% compared with the univariate and bivariate Wiener processes, respectively. The prediction value based on the trivariate gamma process was 3.49, which decreased by 44.95% and 40.54% compared with the univariate and bivariate gamma processes, respectively. In conclusion, the degradation and life prediction model with multivariate stochastic processes can effectively assess the mechanical equipment degradation and predict its remaining useful life. This provides an important reference for the maintenance and management of mechanical equipment, improving equipment efficiency and extend its service life. [ABSTRACT FROM AUTHOR]

Copyright of Bulletin of the Chinese Ceramic Society is the property of Bulletin of the Chinese Ceramic Society Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the advances in infrastructure construction in various countries around the world, extensive requirements have been promoted for the mechanical properties and durability of concrete. In this article, the effects of single and compound additions of nano-SiO2 (NS) and nano-Fe2O3 (NF) on the mechanical properties and durability of concrete were evaluated through different experiments. Moreover, the optimal contents of these additions corresponding to their different properties were explored. The macroscopic test results indicated that the addition of nanomaterials had a perceptible effect on the mechanical properties and durability of concrete. The concrete mixed with 1.0% NS and 0.5% NF achieved optimal performance. With this composition, the compressive strength, flexural strength, water absorption rate, and chloride ion diffusion coefficient (corrosion resistance) of the 28 days concrete were 52.94 MPa, 7.27 MPa, 4.82%, and 4.52 × 10–12 m2/s, respectively, which were 21.5%, 23.0%, 29.4%, and 37.2% higher than those of ordinary concrete at the same age. Microscopic observation and elemental analysis of the ITZ (interfacial transition zone) interface in concrete revealed that NS and NF contributed to nucleation. The two components reacted chemically with Ca (OH)2 grains, resulting in the synergistic effect of the spatial morphology of the hydration products, thus increasing the density of the internal structure of the concrete. To facilitate the application of nanomaterials in engineering, functional relationships between the content of nanomaterials in concrete and the improvements in various properties of concrete were constructed with high accuracy. In addition, the time-dependent correlation coefficients of apparent chloride ion concentration and chloride ion diffusion were introduced based on Fick's second law, and this model was applied to multiple long-term monitoring experiments to verify its accuracy under various exposure conditions, such as tidal zones, splash zones, and atmospheric zones. The improved Fick model was used to predict the service life of concrete. By taking the splash zone as an example, it was reported that under the same conditions, the expected lives of S2F0, S0F2, and S2F1 increased by 31.8%, 25.7%, and 50.2%, respectively, compared to that of OPC. The research results could provide a reference for the development of high-performance concrete. [ABSTRACT FROM AUTHOR]

Copyright of Iron Steel Vanadium Titanium is the property of Iron Steel Vanadium Titanium Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The bypass valve of a positive displacement motor is a key component for regulating the bottom hole pressure and ensuring the normal circulation of drilling fluid during the drilling process. Severe erosion damage to the bypass valve significantly affects the service life of the positive displacement motor, yet there is currently a lack of related research. In this research, the flow characteristics of drilling fluid inside the valve core were analyzed through flow field simulation, and the main factors influencing erosion damage to the valve core were investigated. The results indicate that the side holes and flow channel structure of the valve core are the main causes of erosion. Based on this, two optimization schemes are proposed, namely, reducing the number of bypass side holes to 4 and optimizing the flow channel cone angle to 45°. The simulation results show that the erosion rate of the optimized valve core is significantly reduced, and the service life is effectively improved. Finally, a valve core life prediction model is established using a back propagation (BP) neural network to evaluate the optimization effect. The results show that the applicable flow range and maximum service life of the optimized valve core are increased by approximately 60% and 75.4%, respectively, validating the effectiveness of the optimization scheme. [ABSTRACT FROM AUTHOR]

Copyright of Electric Drive for Locomotives is the property of Electric Drive for Locomotives Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper proposes a data-driven prediction scheme for the remaining life of centrifugal pump bearings based on the KPCA–LSTM network. A centrifugal pump bearing fault experiment bench is built to collect data, and the performance of time domain, frequency domain, and time-frequency domain characteristics under different working conditions is analyzed. Time domain characteristics, frequency domain characteristics, wavelet packet decomposition energy characteristics, and CEEMDAN energy features are found to be able to capture fault information under different working conditions. Therefore, 43 sensitive features are determined from the time domain, frequency domain, and time-frequency domain. Through the analysis of XJTU-SY bearing life cycle data and based on the weighted scores of monotonicity, robustness, and trend indicators, twelve outstanding characteristics of the bearing in the whole life cycle are selected, and a one-dimensional feature quantity that can characterize the life-degradation process of the centrifugal pump bearing is constructed after KPCA dimension reduction processing. The LSTM network, sensitive to time series, is selected to predict and analyze the constructed one-dimensional feature trend, and the prediction effects of the BP network and the CNN network are compared. The results show that this method has advantages in prediction accuracy and model training time. [ABSTRACT FROM AUTHOR]

There has always been high interest in predicting the solder joint fatigue life in advanced packaging with high accuracy and efficiency. Artificial Intelligence Plus (AI+) is becoming increasingly popular as computational facilities continue to develop. This study will introduce machine learning (a core component of AI). With machine learning, metamodels that approximate the attributes of systems or functions are created to predict the fatigue life of advanced packaging. However, the prediction ability is highly dependent on the size and distribution of the training data. Increasing the amount of training data is the most intuitive approach to improve prediction performance, but this implies a higher computational cost. In this research, the adaptive sampling methods are applied to build the machine learning model with a small dataset sampled from an existing database. The performance of the model will be visualized using predefined criteria. Moreover, ensemble learning can be used to improve the performance of AI models after they have been fully trained. [ABSTRACT FROM AUTHOR]

The transformation of energy structure has brought about new changes in the power system, and the environmentally friendly gas switchgear with the goal of low carbon and environmental protection has been widely spread and applied, but due to its short application time and the accumulation of related research, it continues to carry out life prediction to improve the related operation and maintenance system. The article proposes the environmental protection gas switchgear data processing technology based on data enhancement technology, proposes the environmental protection gas switchgear life prediction technology based on approximate dynamic planning, and verifies the algorithm through case analysis, which proves the effectiveness and accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

Integrated gate commutated thyristors (IGCTs) are critical components in high-voltage, high-current, and high-power conversion systems, particularly in offshore wind energy systems. However, the working environment of offshore wind energy conversion systems is extremely harsh. In this article, we propose an active damage control approach aiming at enhancing the reliability of the conversion system. By employing electro-thermal modeling for the equipment of the offshore wind energy conversion system, the junction temperature and fatigue damage of IGCT are simulated during the operation process. Using the improved model predictive current control (MPCC) method, active damage control effectively regulates the switching frequency of IGCT. IGCTs are symmetrically distributed on each leg of the converter, so the lifespan of the two IGCTs on each leg is also considered to be similar. This method balances the life of the IGCTs on the three legs of the converter and optimizes their utilization to the maximum extent. These measures effectively enhance the reliability of the conversion system and lower the operation and maintenance cost of high-power IGCT converters. The effectiveness of the proposed method is validated by co-simulation results by ANSYS and MATLAB/Simulink. [ABSTRACT FROM AUTHOR]

After the implantation of lower limb artery stents, the complex loading conditions imposed on the limb can lead to fatigue failure, which may induce inflammation and restenosis. To investigate the effect of multi-axial loading conditions on the fatigue performance of stents, five stents, namely APsolute Pro (APbott Vascular, USA), Complete SE (Medtronic, USA), Protégé EverFlex (PE3, USA), Pulsar-35 (Biotronik, Germany), and E-luminexx-B (Bard, USA), were analyzed based on the finite element method (FEM). Besides, their fatigue strength was determined under three levels of loading conditions, including tension-bending-torsion and compression-bending-torsion. Based on that, the fatigue life of these stents was predicted. The results showed that based on the nominal stress method, tension-bending-torsion loading had a more significant impact on the fatigue life of stents than compression-bending-torsion loading. Besides, two different types of initial cracks were analyzed by the fracture mechanics method. The results suggested that both the initial crack and the external load were the main causes of stent fatigue fractures. Compared with the loading nature, the influence of the initial crack on stent fatigue life was more significant. Under the same loading condition, the APsolute Pro stent had the longest fatigue life, while the E-luminexx-B stent had the shortest. Moreover, the mechanism of stent fatigue failure was revealed by exploring the fatigue performance and life prediction of stents under complex loading conditions. These findings have important implications for improving the structural design of stents and their clinical selection. [ABSTRACT FROM AUTHOR]

The electric fatigue load has a significant effect on the crack propagation behavior and failure life of piezoelectric materials and devices. In this paper, an electrical mixed-mode fatigue crack propagation model for piezoelectric materials is proposed based on the piezoelectric Jk-integral theory. The crack initiation, propagation, and life prediction criteria of piezoelectric materials under electric fatigue loading are given by this model, and the finite element simulation model is established to study the electrical mixed-mode crack propagation behavior of piezoelectric structures. Meanwhile, the electrical mixed-mode fatigue crack propagation model is applied to the fatigue crack propagation behavior of a piezoelectric typical defective structure, the crack–hole interference model. The mixed-mode crack propagation, fatigue life, and the interference behavior between the crack and hole at various hole locations of the crack–hole interference model are well recognized by this model. The crack propagation behavior under different electrical load intensities is also considered. The results show that the hole in front of the crack tip inhibits crack propagation to a certain extent, and the strength of electrical load affects the fatigue life of piezoelectric materials and structures. Therefore, the proposed electrical mixed-mode fatigue crack propagation model provides a reference for predicting the mixed-mode fatigue crack propagation behavior and fatigue life of piezoelectric structures under electric fatigue loading. [ABSTRACT FROM AUTHOR]

In advanced nuclear power systems, structure materials have compatibility problems with lead-bismuth eutectic coolants such as liquid metal corrosion, and the corrosion failure behavior of the circulation pump is one of the main causes of nuclear safety accidents. Specifically, this paper reviews the failure behavior of the main pump in a flowing lead-bismuth environment for a prolonged period, alongside the prediction of the impeller blade's fatigue life. The development process and main structure of the nuclear reactor are introduced, and the failure behavior and influencing factors of the impeller blade of the main pump are discussed in detail. This paper also provides an in-depth analysis of material composition, oxygen content, temperature, corrosion time, relative velocity, and coating, to identify the failure reason and mechanism of the structural components featuring austenitic stainless steel as the main material. Finally, the fatigue life of the impeller and its prediction model are described from the perspective of fatigue failure, and the reliability analysis of fatigue life prediction is analyzed. [ABSTRACT FROM AUTHOR]

This article provides an overview of important considerations in the modeling, operation, management, and diagnosis of lithium batteries. It emphasizes the significance of accurate modeling for battery simulation, optimization control, and life management. The article also highlights the need for reliable operation and management, including effective heat dissipation and equilibrium strategies for battery state of charge. Additionally, it discusses the importance of accurate diagnosis for improving reliability and safety. The article concludes by suggesting prospects for future research in these areas, including modeling analysis, parameter identification techniques, optimization strategies, data-driven methods, and further studies on internal mechanisms. The research was not funded and the authors have no conflicts of interest. [Extracted from the article]

Copyright of Lubrication Engineering (0254-0150) is the property of Editorial Office of LUBRICATION ENGINEERING and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The concentration of interfacial normal stress at the free edges of thermal barrier coatings (TBCs) can result in coating spallation. Fatigue cracking is one of the main reasons for creating free edges under complex loads. It is crucial to investigate the fatigue cracking of coatings under cyclic loads to assess potential coating failure. To address this issue, a novel model was proposed to predict the fatigue life of the YSZ topcoat under stress parallel to the interface. Firstly, this study conducted uniaxial and tensile-torsional fatigue tests at elevated temperatures on specimens with atmospheric plasma-sprayed TBCs. The test results revealed that fatigue cracks appeared in the topcoat under cyclic loads, but these cracks did not propagate into the bondcoat or substrate immediately. The number of cycles before the topcoat cracked was found to be associated with the magnitude of the cyclic load. Secondly, this study analyzed the test conditions using the finite element method. Simulations indicated that the crack direction in the topcoat under complex loading conditions was aligned with the first principal stress direction. Finally, the fatigue life prediction model of the topcoat was established based on experiments and simulations. The predicted results fell within a fourfold scatter band. [ABSTRACT FROM AUTHOR]

Accurate classification of battery quality and prediction of battery lifetime before leaving the factory would bring economic and safety benefits. Here, we propose a data-driven approach with machine learning to classify the battery quality and predict the battery lifetime before usage only using formation data. We extract three classes of features from the raw formation data, considering the statistical aspects, differential analysis, and electrochemical characteristics. The correlation between over 100 extracted features and the battery lifetime is analysed based on the ageing mechanisms. Machine learning models are developed to classify battery quality and predict battery lifetime by features with a high correlation with battery ageing. The validation results show that the quality classification model achieved accuracies of 89.74% and 89.47% for the batteries aged at 25°C and 45°C, respectively. Moreover, the lifetime prediction model is able to predict the battery end-of-life with mean percentage errors of 6.50% and 5.45% for the batteries aged at 25°C and 45°C, respectively. This work highlights the potential of battery formation data from production lines in quality classification and lifetime prediction.

Unlike traditional steel bars, the long-term performance of GFRP (glass fiber reinforced plastic) bars in natural erosion environments remains unclear. This study simulated the water environment at the entrance of the Chai River, a tributary to Dianchi Lake in China, and investigated the mechanical properties of GFRP bars under varying corrosion concentrations. Microstructural analysis using SEM (scanning electronic microscopy) was also conducted to uncover the degradation mechanism. The results showed that after 180 days of immersion in natural water and exposure to environmental concentrations of 5 times, 10 times, and 20 times, the tensile strength of GFRP bars exhibited reductions of 2.8 %, 4.2 %, 7.2 %, and 9.5 %, respectively. However, the compressive strength decreased by 38.0 %, 48.6 %, 50.2 %, and 53.2 % under the same condition. As the number of corrosion cycles increased, the rate of strength decline decreased gradually. The combined action of hydroxyl ions generated through bicarbonate hydrolysis and water molecules in the solution led to the breakdown and swelling of the resin matrix, ultimately causing detachment at the fiber-resin interface. An analysis was conducted on the time-shifted regression equation of the accelerated conversion factor (ASF) under different concentrations, based on the correlation between natural erosion and acceleration testing. It was observed that a strong correlation exists between the strength retention rate and the natural erosion environment at 5 times the concentration. Finally, an improved time-shift method based on the Arrhenius theory was proposed to predict the tensile strength of GFRP bars in a natural erosion environment.

Despite the global abundance of Desert sand (DS), its application in engineering remains limited. This application can help alleviate challenges related to high unit costs and the scarcity of natural fine aggregate supplies. Freeze resistance experiments were conducted on C30 concrete incorporating varying proportions of DS, including 0 %, 25 %, 50 %, 75 %, and 100 %. DS was mixed with Tuff crushed stone fine aggregate (Manufactured sand, referred to as MS). The freeze durability of the composite fine aggregate concrete was comprehensively evaluated from three perspectives: surface morphology, a macroscopic analysis including mass loss and relative dynamic elastic modulus (RDEM), and microscopic examinations encompassing pore structure observation and scanning electron microscope (SEM) imaging. Utilizing the damage degree index of the dynamic elastic modulus and the Weibull distribution model, the life prediction process was conducted to elucidate the time-varying behavior of DS-MS concrete. According to the test findings, the concrete specimen DS25-MS75 demonstrates the highest freeze resistance. The mass loss rate shows a pattern of initial decrease followed by an increase with higher desert sand admixture, whereas the RDEM initially increases and then decreases. Optimal desert sand admixture levels can prevent and delay concrete degradation, thereby enhancing its freeze resistance. Excessive desert sand content leads to increased water demand during the curing process, resulting in elevated porosity and facilitating the formation of voids. Under the action of freezing and thawing cycles in water, DS25 %-MS75 % concrete has the longest expected service life with an actual life of 101 years, while DS100 %-MS0 % concrete has the shortest service life with an actual life of only 54.5 years.

The remaining creep life of the reheat section pipeline of low hardness P91 steel replaced in a certain power plant was studied. The rupture behavior of the joint was studied using metallographic microscope (OM), scanning electron microscope (SEM), and Electron Back Scatter Diffraction (EBSD), and the creep life was predicted. The results show that the rupture behavior of P91 steel and its joint during service is related to external stress. When the stress is high, the failure position of the joint is concentrated at the lowest hardness position, dominated by low strength martensitic deformation cracking; When the stress is low, the joint fails from FGHAZ in the form of Type IV cracking, resulting in a combination of martensitic structure degradation and pore nucleation. Type IV cracking has a significant impact on the life of joints, and the polygonal line OSD method based on fracture mode for life prediction can effectively improve the accuracy of material life prediction.

In order to study the crack damage propagation law of rotor blades under complex loads in the actual service of aero-engines, the crack propagation life curve of blades was constructed based on the operational data of aero-engine, and the crack propagation rate formula was established by fracture mechanics theory. Taking the high-pressure compressor blade as an example, the load model of the compressor blade structure was constructed by the fluid-solid coupling method. The Paris， Walker and Newman crack growth rate formulas were used to analyze the three-dimensional crack propagation in the tip region of the compressor blade. The simulation results show that the prediction results of the three crack propagation rate formulas are consistent with the crack propagation curve of the engine operation data in the early stage of crack propagation, which can better describe the crack propagation law of the blade. In the stage of stable crack propagation, the prediction results of Walker and Newman formulas are relatively dangerous, and the prediction results of Paris formula are in good agreement with each other, and can be mutually confirmed with the requirements of damage development control in the manufacturer's technical manual.

Abstract This work focuses on the prediction and comparison of the fatigue life of topologically optimized pads in an externally adjustable fluid film (EAFF) bearing. It integrates one-way/two-way fluid–structure interaction analysis, topological optimization (TO), and design modifications of the pad of an externally adjustable fluid film bearing. The major goal is to create an optimum pad design that minimizes weight and maintains structural integrity, and then to predict and compare the fatigue life of these alternative designs. The outcomes of the present study are as follows: (i) Two-way FSI results show a decrease of 65.64% in hydrodynamic fluid film pressure when compared to one-way FSI results because they take into account modifications in the fluid region's geometry caused by pad deformation; (ii) even though the maximum pad deformation in optimized pad geometry (Type-4) resulting from oil film pressure is relatively small (0.0036551 mm), the influence of pad deformation on the fluid domain due to hydrodynamic fluid film pressure cannot be understated; and (iii) when comparing the TO technique's results with fatigue life results, four elongated holes in the radial direction (Type-4) are most appropriate.

Abstract As the new energy industry continues to progress, the health management of power batteries has become the key to ensuring the performance and safety of automobiles. Therefore, accurately predicting battery capacity decline is particularly important. A battery capacity degradation prediction model combining unscented particle filtering, particle swarm optimization, and SVR is constructed. It optimizes regression parameters through the introduced optimization strategy. Unscented particle filtering is used to improve particle swarm optimization and battery detection model. The study tested four various models of lithium-ion batteries. The model predicted a mean square error of 0.0011 for battery 5, 0.0007 for battery 6, 0.0022 for battery 7, and 0.0013 for battery 18. In the prediction of different battery types, the mean square error of the NIMH battery was reduced by 0.0008 compared with the particle swarm optimization-support vector regression algorithm, and by 0.0005 compared with the unscented particle filtering-regression vector regression algorithm. The mean square error of lithium-iron phosphate battery was reduced by 0.0008 and 0.0004 respectively compared with comparison models. The mean square error value of lithium titanate battery was reduced by 0.0007 and 0.0003 respectively in the research model compared with comparison models. It improves the prediction accuracy in lithium-ion batteries. Its application in battery health management can provide important technical support for improving battery performance and extending service cycles. The proposed method can be used for battery monitoring and management of power grid energy storage system. By accurately predicting the capacity decline of battery, the operation strategy of energy storage system can be optimized to ensure the efficient operation and long life of the system. The battery management system can be used for drones and aviation equipment to predict battery health and capacity decline in real time, ensuring the safety and reliability of flight missions.

As the main force-bearing component of cable-stayed bridges, cables are often in complex and harsh working environments, and traditional cable force monitoring methods have certain limitations. A fiber grating cable force sensor was developed by encapsulating the fiber grating sensor embedded into the groove on the metal pressure block, and the durability performance of two epoxy resin adhesive encapsulated fiber grating cable force sensors in the acid and alkali corrosion environment was studied, and then the sensor life prediction equation was derived by relying on the inverse power law model and aging kinetic theory. The test results show that the sensor perform well on the linearity and stability, and the main reason for the failure of the sensor in the corrosion solution is the corrosion and peeling of the adhesive in the boundary zone of the package site. The acid and alkali resistance of class A sensors is stronger than that of class B sensors, and the service life of class A sensors in weak acid and weak alkali environments is more than 35 years, which is longer than the average cable change life of cable-stayed bridge cables of 20 years, and can realize the monitoring of the whole life cycle of cables. [ABSTRACT FROM AUTHOR]

Addressing the limitation of traditional deep learning models in capturing the spatio-temporal characteristics of flight data and the constrained prediction accuracy due to sequence length in aero-engine life prediction, this study proposes an aero-engine remaining life prediction approach integrating a kernel slow feature analysis, a Gaussian mixture model, and an improved Autoformer model. Initially, the slow degradation features of gas path performance parameters over time are extracted through kernel slow feature analysis, followed by the establishment of a Gaussian mixture model to create a health state representation using Bayesian inferred distances for quantifying the aero-engine's health status. Moreover, a spatial attention mechanism is introduced alongside the autocorrelation mechanism of the Autoformer model to augment the global feature extraction capacity. Additionally, a multilayer perceptron is employed to further elucidate the degradation trends, which enhances the model's learning and predictive capabilities for extended sequences. Subsequently, experiments are conducted using authentic aero-engine operational data, comparing the proposed method with the standard Autoformer and Transformer models. The results demonstrate that the proposed method outperforms both models in swiftly and accurately predicting the remaining life of aero-engines with robustness and high prediction accuracy. [ABSTRACT FROM AUTHOR]

The combined high and low cycle fatigue (CCF) loading condition and random uncertainty exert a considerable impact on the design of turbine shafts. To enhance the fatigue life and reliability, this research proposes a CCF reliability analysis and optimization method for turbine shafts. A CCF fatigue reliability analysis framework is established, which focuses on the quantification of CCF loading characteristics and random uncertainty. The consideration of CCF loading characteristics contain the loading frequency ratio of high cycle fatigue (HCF) to low cycle fatigue (LCF), the stress amplitude ratio of HCF to LCF, as well as their interaction. The consideration of random uncertainty contains material, geometry and load, and a surrogate model‐based method is introduced to improve the quantification efficiency. Through the validation by comparing with experimental data and traditional methods, the proposed method is with higher accuracy and efficiency. By integrating the proposed fatigue reliability analysis method with design optimization, optimal design values for the turbine shaft were identified. This method theoretically extends the shaft's CCF life and provides practical engineering guidance for its reliability analysis and design. [ABSTRACT FROM AUTHOR]

Accurately assessing the fatigue performance of components is the key to ensuring structural integrity and reliability, but there is a lack of fatigue life prediction methods that effectively couple the stress gradient effect, the non-proportional additional strengthening effect, and the size effect. Accordingly, a fatigue life prediction model for notched specimens under multiaxial loading is established by analyzing the influence of tension–torsion proportional load and tension–torsion non-proportional load on the fatigue strength of notched specimens. Firstly, based on the energy critical plane method, the location of the critical plane is determined with the help of the coordinate transformation principle. Secondly, the material constant is used to quantify the level of cyclic strengthening, and a non-proportional additional strengthening function is proposed by considering the influence of phase difference. Thirdly, the influence of the non-uniform stress field at the notch root on the fatigue life is considered, and the distribution of equivalent stress on the specific paths is extracted and normalized to give an equivalent stress gradient factor. Then, a fatigue strength reduction factor is constructed by considering the influence of different notch geometrical parameters. Finally, a fatigue life assessment method for notched specimens is proposed based on the Manson–Coffin equation. With the help of the test data of three materials, En8, Al7050-T7451, and GH4169, the method is validated and compared with the calculation results of the Manson–Coffin equation, SWT model, and FS model. The results show that the prediction accuracy of the method in this study is high, which is located in the two-fold error dispersion band, and the prediction results are better than that of the other three models. [ABSTRACT FROM AUTHOR]

Considering the inherent characteristics of strength degradation, a residual strength model is improved reasonably through the incorporation of the two‐parameter Weibull distribution. Building upon this foundation, an enhanced nonlinear fatigue damage accumulation model that is closer to the actual situation is proposed in view of the randomness of fatigue damage. The model takes into account the effect of load action on fatigue damage. To ascertain the feasibility of the proposed model, the time‐varying reliability of the model is evaluated by applying the probability density evolution method. The model's precision in predicting residual life is corroborated through five experimental data sets under bilevel and multilevel loading conditions. The results demonstrate that the proposed model exhibits excellent applicability and remarkable precision, thereby confirming its potential value in practical applications. Highlights: Using strength degradation characteristics, new residual strength model is crafted.Combined with the Weibull distribution, a new fatigue damage model is established.The PDEM is applied to obtain the time‐varying fatigue reliability curve. [ABSTRACT FROM AUTHOR]

This study proposes a multiaxial rainflow counting method to realize simultaneous counting of four‐channel data. The power spectral density (PSD) expressions of stresses and strains in different planes are derived according to multiaxial theories to address the fatigue problems under random vibration loading. This study takes the plane with maximum variance of shear strain PSD as the critical plane and proposes a hybrid frequency‐time domain life prediction method for calculating the life under random vibration loading in combination with the proposed new multiaxial rainflow counting method. Both the feasibilities of the proposed multiaxial rainflow counting method and hybrid frequency‐time domain life prediction method are verified by using the multiaxial fatigue experimental results of 6061‐T6 and 7075‐T6 aluminum alloys under random loading. Meanwhile, the calculation results and efficiencies of the proposed life prediction method are summarized, and the prediction effects of the different multiaxial fatigue models are evaluated, and different rainflow counting methods are compared. Highlights: A novel four‐channel multiaxial rainflow counting method is proposed.A novel hybrid frequency‐time domain life prediction method is proposed.Validities of the proposed methods are confirmed by the fatigue life prediction cases.Different life prediction methods and multiaxial fatigue models are evaluated. [ABSTRACT FROM AUTHOR]

To address global research in energy technologies, a UK–US collaboration was setup to look at implications in the use of alternative gas turbine liquid/gaseous fuels and first-generation IGCC-derived syngas. The development of predictive models for deposition, corrosion and component living in gas turbines operating on such novel fuel gases is now being developed. Linked to this, the non-destructive inspection of hot gas path components needs to be refined and the life prediction models utilising thermal barrier coatings need to be validated to enable the on-going assessment of the remnant lives of components during their use. Thermal barrier coatings (TBCs) have been critical to the operation of gas turbines in aircraft and land-based applications. As the engine demands increase with respect to operational temperature and efficiency, so has the requirement for TBCs, both from the perspective of their performance (thermal conductivity, durability) as well as their reliability. Because TBCs play critical role in protecting the underlying super alloy, their failure (spallation) cause accelerated component damage leading to unplanned outage. Therefore, it is important to inspect and monitor the TBC condition to assure its quality and reliability. Non-destructive evaluation (NDE) methods would allow for direct determination of TBC thermal conductivity on coated components and, therefore, they can be used to inspect the quality of as processed components and monitor TBC degradation during service. This paper presents a new method, the pulsed thermal imaging–multilayer analysis (PTI–MLA) method, which can measure the coating thermal conductivity and heat capacity distributions for coatings on different substrates and thickness. NDE methods were also evaluated based on preliminary experimental results for TBC samples that were thermal cycled to various lifetimes to monitor TBC degradation and predict TBC life. Initial insight into the TBC response with painted and unpainted surfaces is also discussed to demonstrate next steps. [ABSTRACT FROM AUTHOR]

This review paper is an homage to Arvid Palmgren's pioneering paper on rolling bearing service life to highlight its relevance a century later. It follows the evolution of bearing service life theory from Palmgren's fundamental research to the contemporary international standard ISO 281. Palmgren's theory, based on the previously published papers of Stribeck and Hertz, laid the basis for the later development of bearing service life assessment methodology. Based on the Weibull theory of probability of damage, Lundberg and Palmgren introduced stochastic elements to explain the effect of reliability on bearing service life prediction. Harris and Ioannides, who made a significant contribution to the revision of the international standard on rolling bearing load rating and rating life are mentioned as well. Zaretsky's critical analysis also was not neglected in this review, due to a different approach respecting the original influence of material properties and bearing performances. Despite standardization, ongoing research by leading advanced bearing industries and academic institutions continues to refine methodologies for service life assessment. Through a comprehensive review and analysis, this paper offers insight into the current state of bearing service life theory, highlighting the collaborative efforts bringing progress in this field. [ABSTRACT FROM AUTHOR]

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As the new energy industry continues to progress, the health management of power batteries has become the key to ensuring the performance and safety of automobiles. Therefore, accurately predicting battery capacity decline is particularly important. A battery capacity degradation prediction model combining unscented particle filtering, particle swarm optimization, and SVR is constructed. It optimizes regression parameters through the introduced optimization strategy. Unscented particle filtering is used to improve particle swarm optimization and battery detection model. The study tested four various models of lithium-ion batteries. The model predicted a mean square error of 0.0011 for battery 5, 0.0007 for battery 6, 0.0022 for battery 7, and 0.0013 for battery 18. In the prediction of different battery types, the mean square error of the NIMH battery was reduced by 0.0008 compared with the particle swarm optimization-support vector regression algorithm, and by 0.0005 compared with the unscented particle filtering-regression vector regression algorithm. The mean square error of lithium-iron phosphate battery was reduced by 0.0008 and 0.0004 respectively compared with comparison models. The mean square error value of lithium titanate battery was reduced by 0.0007 and 0.0003 respectively in the research model compared with comparison models. It improves the prediction accuracy in lithium-ion batteries. Its application in battery health management can provide important technical support for improving battery performance and extending service cycles. The proposed method can be used for battery monitoring and management of power grid energy storage system. By accurately predicting the capacity decline of battery, the operation strategy of energy storage system can be optimized to ensure the efficient operation and long life of the system. The battery management system can be used for drones and aviation equipment to predict battery health and capacity decline in real time, ensuring the safety and reliability of flight missions. [ABSTRACT FROM AUTHOR]