LIFE PREDICTION OF HIGH TEMPERATURE STRUCTURAL COMPONENT BY STRAIN RANGE PARTITIONING

The s recent works on the improvement of the Strain Range Partitioning(SRP) method and its application to the life prediction of high temperature structural components are summarized. Examined components are divided into three groups, that is, components in the steel production plants, in the automobile and in the fossil power plants. Based on the results of the inelastic analysis and the creepfatigue properties of the material, which were obtained by IJ(=PP,PC, CP, CC) tests, the effects of the material properties, operating conditions and configuration of components were quantitatively evaluated to select the most effective measures for the thermal fatigue life extension. The SRP has been successfully applied until now to the life prediction and extension of the actual structural components subjected to thermal cycling by the s.

Remaining Useful Life Prediction of Rail Based on Improved Pulse Separable Convolution Enhanced Transformer Encoder

In order to prevent possible casualties and economic loss, it is critical to accurate prediction of the Remaining Useful Life (RUL) in rail prognostics health management. However, the traditional neural networks is difficult to capture the long-term dependency relationship of the time series in the modeling of the long time series of rail damage, due to the coupling relationship of multi-channel data from multiple sensors. Here, in this paper, a novel RUL prediction model with an enhanced pulse separable convolution is used to solve this issue. Firstly, a coding module based on the improved pulse separable convolutional network is established to effectively model the relationship between the data. To enhance the network, an alternate gradient back propagation method is implemented. And an efficient channel attention (ECA) mechanism is developed for better emphasizing the useful pulse characteristics. Secondly, an optimized Transformer encoder was designed to serve as the backbone of the model. It has the ability to efficiently understand relationship between the data itself and each other at each time step of long time series with a full life cycle. More importantly, the Transformer encoder is improved by integrating pulse maximum pooling to retain more pulse timing characteristics. Finally, based on the characteristics of the front layer, the final predicted RUL value was provided and served as the end-to-end solution. The empirical findings validate the efficacy of the suggested approach in forecasting the rail RUL, surpassing various existing data-driven prognostication techniques. Meanwhile, the proposed method also shows good generalization performance on PHM2012 bearing data set.

Remaining Useful Life Prediction With Partial Sensor Malfunctions Using Deep Adversarial Networks

In recent years,intelligent data-driven prognostic methods have been successfully developed,and good machinery health assessment performance has been achieved through explorations of data from multiple sensors.However,existing datafusion prognostic approaches generally rely on the data availability of all sensors,and are vulnerable to potential sensor malfunctions,which are likely to occur in real industries especially for machines in harsh operating environments.In this paper,a deep learning-based remaining useful life(RUL)prediction method is proposed to address the sensor malfunction problem.A global feature extraction scheme is adopted to fully exploit information of different sensors.Adversarial learning is further introduced to extract generalized sensor-invariant features.Through explorations of both global and shared features,promising and robust RUL prediction performance can be achieved by the proposed method in the testing scenarios with sensor malfunctions.The experimental results suggest the proposed approach is well suited for real industrial applications.

Remaining useful life prediction based on nonlinear random coefficient regression model with fusing failure time data

Remaining useful life(RUL) prediction is one of the most crucial elements in prognostics and health management(PHM). Aiming at the imperfect prior information, this paper proposes an RUL prediction method based on a nonlinear random coefficient regression(RCR) model with fusing failure time data.Firstly, some interesting natures of parameters estimation based on the nonlinear RCR model are given. Based on these natures,the failure time data can be fused as the prior information reasonably. Specifically, the fixed parameters are calculated by the field degradation data of the evaluated equipment and the prior information of random coefficient is estimated with fusing the failure time data of congeneric equipment. Then, the prior information of the random coefficient is updated online under the Bayesian framework, the probability density function(PDF) of the RUL with considering the limitation of the failure threshold is performed. Finally, two case studies are used for experimental verification. Compared with the traditional Bayesian method, the proposed method can effectively reduce the influence of imperfect prior information and improve the accuracy of RUL prediction.

Characterization,identification and life prediction of acoustic emission signals of tensile damage for HSR gearbox housing material

Purpose-This study aims to ensure the operation safety of high speed trains,it is necessary to carry out nondestructive monitoring of the tensile damage of the gearbox housing material in rail time,yet the traditional tests of mechanical property can hardly meet this requirement.Design/methodology/approach-In this study the acoustic emission(AE)technology is applied in the tensile tests of the gearbox housing material of an high-speed rail(HSR)train,during which the acoustic signatures are acquired for parameter analysis.Afterward,the support vector machine(SVM)classifier is introduced to identify and classify the characteristic parameters extracted,on which basis the SVM is improved and the weighted support vector machine(WSVM)method is applied to effectively reduce the misidentification of the SVM classifier.Through the study of the law of relations between the characteristic values and the tensile life,a degradation model of the gearbox housing material amid tensile is built.Findings-The results show that the growth rate of the logarithmic hit count of AE signals and that of logarithmic amplitude can well characterize the stage of the material tensile process,and the WSVM method can improve the classification accuracy of the imbalanced data to above 94%.The degradation model built can identify the damage occurred to the HSR gearbox housing material amid the tensile process and predict the service life remains.Originality/value-The results of this study provide new concepts for the life prediction of tensile samples,and more further tests should be conducted to verify the conclusion of this research.

Research on mechanical wear life feature fusion prediction method based on temporal pattern attention mechanism

In order to solve the problem of low prediction accuracy when only vibration or oil signal is used to predict the remaining life of gear wear,a gear wear life feature fusion prediction method based on temporal pattern attention mechanism is proposed.Firstly,deep residual shrinkage network(DRSN)is used to extract the features of the original vibration time series signals with low signal-tonoise ratio,and the vibration features associated with gear wear evolution are obtained.Secondly,the extracted vibration features and the oil monitoring data that can intuitively reflect the wear process information are jointly input into the bi-directional long short-term memory neural network based on temporal pattern attention mechanism(TPA-BiLSTM),the complex nonlinear relationship between vibration features,oil features and gear wear process evolution is further explored to improve the prediction accuracy.The gear life cycle dynamic response and wear process signals are obtained based on the gear numerical simulation model,and the feasibility of the proposed method is verified.Finally,the proposed method is applied to the residual life prediction of gear on a test bench,and the comparison between different methods proved the validity of the proposed method.

Fatigue behavior and life prediction of A7N01 aluminium alloy welded joint

Fatigue characteristics of A7N01 aluminium alloy welded joint were investigated and a fatigue crack initiation life-based model was proposed. The difference of fatigue crack initiation life among base metal, weld metal and heat affected zone (HAZ) is slight. Furthermore, the ratio of fatigue crack initiation life (Ni) to fatigue life to failure(Nf) is a material dependent parameter, 26.32%, 40.21% and 60.67% for base metal, HAZ and weld metal, respectively. Total fatigue life predicted using the presented model is in good agreement with the experimental data and that using Basquin’s model. The observation results of fatigue fracture surfaces, using scanning electron microscope (SEM), demonstrate that fatigue crack initiates from smooth surface due to welding process for weld metal, blowhole in HAZ causes fatigue crack initiation, and the crushed second phase particles play an important part in fatigue crack initiation in base metal.

Cycle life prediction and match detection in retired electric vehicle batteries

The lifespan models of commercial 18650-type lithium ion batteries （nominal capacity of 1150 mA-h） were presented. The lifespan was extrapolated based on this model. The results indicate that the relationship of capacity retention and cycle number can be expressed by Gaussian function. The selecting function and optimal precision were verified through actual match detection and a range of alternating current impedance testing. The cycle life model with high precision （〉99%） is beneficial to shortening the orediction time and cutting the prediction cost.

Corrosion Fatigue Life Prediction of Aircraft Structure Based on Fuzzy Reliability Approach

Material performance of LY12CZ aluminum is greatly degraded because of corrosion and corrosion fatigue, which severely affect the integrity and safety of aircraft structure, especially those of lbe navy aircraft structure. The corrosion and corrosion fatigue failure process of aircraft structure are directly concerned with many factors, such as load, material characteristics, corrosive environment and so on. The damage mechanism is very complicated, and there are both randomness and fuzziness in the failure process. With consideration of the limitation of those conventional probabilistic approaches for prediction of corrosion fatigue life of aircraft structure at present, and based on the operational load spectrum obtained through investigating service status of the aircraft in naval aviation force, a fuzzy reliability approach is proposed, which is more reasonable and closer to the fact. The effects of the pit aspect ratio, the crack aspect ratio and all fuzzy factors on corrosion fatigue life of aircraft structure are discussed. The results demonstrate that the approach can be applied to predict the corrosion fatigue life of aircraft structure.

Grain size based low cycle fatigue life prediction model for nickel-based superalloy

Nickel-based superalloys are easy to produce low cycle fatigue(LCF)damage when they are subjected to high temperature and mechanical stresses.Fatigue life prediction of nickel-based superalloys is of great importance for their reliable practical application.To investigate the effects of total strain and grain size on LCF behavior,the high temperature LCF tests were carried out for a nickel-based superalloy.The results show that the fatigue lives decreased with the increase of strain amplitude and grain size.A new LCF life prediction model was established considering the effect of grain size on fatigue life.Error analyses indicate that the prediction accuracy of the new LCF life model is higher than those of Manson-Coffin relationship and Ostergren energy method.

Fatigue life prediction of aviation aluminium alloy based on quantitative pre-corrosion damage analysis

A new method of quantitative pre-corrosion damage of aviation aluminium(Al-Cu-Mg)alloy was proposed,whichregarded corrosion pits as equivalent semi-elliptical surface cracks.An analytical model was formulated to describe the entire regionof fatigue crack propagation(FCP).The relationship between the model parameters and the fatigue testing data obtained in thepre-corroded experiments,crack propagation experiments and S-N fatigue experiments was discussed.The equivalent crack sizesand the FCP equation were used to calculate the fatigue life through numerical integration based on MATLAB/GUI.The resultsconfirm that the sigmoidal curve fitted by the FCP model expresses the whole change from Region I to Region III.In addition,thepredicted curves indicate the actual trend of fatigue life and the conservative result of fatigue limit.Thus,the new analytical methodcan estimate the residual life of pre-corroded Al-Cu-Mg alloy,especially smooth specimens.

LIFE PREDICTION APPROACH FOR RANDOM MULTIAXIAL FATIGUE

According to the concept of critical plane, a life prediction approach forrandom multiaxial fatigue is presented. First, the critical plane under the multiaxial randomloading is determined based on the concept of the weight-averaged maximum shear strain direction.Then the shear and normal strain histories on the determined critical plane are calculated and takenas the subject of multiaxial load simplifying and multiaxial cycle counting. Furthermore, amultiaxial fatigue life prediction model including the parameters resulted from multiaxial cyclecounting is presented and applied to calculating the fatigue damage generated from each cycle.Finally, the cumulative damage is added up using Miner's linear rule, and the fatigue predictionlife is given. The experiments under multiaxial loading blocks are used for the verification of theproposed method. The prediction has a good correction with the experimental results.

Casing life prediction using Borda and support vector machine methods

Eight casing failure modes and 32 risk factors in oil and gas wells are given in this paper. According to the quantitative analysis of the influence degree and occurrence probability of risk factors, the Borda counts for failure modes are obtained with the Borda method. The risk indexes of failure modes are derived from the Borda matrix. Based on the support vector machine （SVM）, a casing life prediction model is established. In the prediction model, eight risk indexes are defined as input vectors and casing life is defined as the output vector. The ideal model parameters are determined with the training set from 19 wells with casing failure. The casing life prediction software is developed with the SVM model as a predictor. The residual life of 60 wells with casing failure is predicted with the software, and then compared with the actual casing life. The comparison results show that the casing life prediction software with the SVM model has high accuracy.

An Integrated Approach to Fatigue Life Prediction of Whole System for Offshore Platforms

The failure of one or even more components usually does riot lead to the collapse of the whole structure. Most of the analysis of fatigue is centered on only a single component which the researchers are interested in or Much attention should be paid to. However, the collapse of a structure is the result of failure of a series of components in a specific order or path. This paper proposes an integrated approach to fatigue life prediction of whole structural system for offshore platforms, mainly describing the basic principles and prediction method. A method is presented for determining the failure path of the whole structure system and calculating the fatigue life in the determined failure path, The corresponding final collapse criteria for the whole structure system are discussed, A simple method of equivalent fatigue stress range calculation and a mathematical model of structural component fatigue life estimation in consideration of sea wave and sea ice loads are provided. As an application of the proposed approach, a fixed production platform Bohai No. 8 is chosen for the predication of fatigue life of the whole structure system by means of the software OSFAC developed based on the present methods.

Life Prediction Based on Transient Dynamics Analysis of Van Semi-trailer with Air Suspension System

The early fatigue damage in the van-body of the semi-trailer is often caused by the unique mechanical characteristics and the dynamic impact of the loads.The traditional finite element method with static strength analysis cannot support the fatigue design of van-body;thus,the dynamics analysis should be adopted for the endurance performance.The accurate dynamics model to describe the transient impacts of all kinds of uneven road and the proper system transfer functions to calculate the load transfer effects from tire to van-body are two critical factors for transient dynamics analysis.In order to evaluate the dynamic performance,the dynamics model of the trailer with the air suspension is brought forward.Then the analysis method of the power spectral density （PSD） is set up to study the transient responses of the road dynamic impacts.The transient responses transferred from axles to van-body are calculated,such as dynamic stress,dynamic RMS acceleration,and dynamic load factors.Based on the above dynamic responses,the fatigue life of van-body is predicted with the finite element analysis （FEA） method.Applying the test parameters of the trailer with air suspension,the simulation system with Matlab/Simulink is constructed to describe the dynamic responses of the impacts of the tested PSD of the vehicle axles,and then the fatigue life is predicted with FEA method.The simulated results show that the vibration level of the van-body with air suspension is reduced and the fatigue life is improved.The real vehicle tests on different roads are carried out,and the test results validate the accuracy of the simulation system.The proposed fatigue life prediction method is effective for the virtual design of auto-body.

Prediction of low-cycle crack initiation life of powder superalloy FGH96 with inclusions based on damage mechanics

The effects of inclusions in powder superalloy FGH96 on low-cycle fatigue life were studied, and a low-cycle crack initiation life prediction model based on the theory of damage mechanics was proposed. The damage characterization parameter was proposed after the construction of damage evolution equations. Fatigue tests of the powder superalloy specimens with and without inclusion were conducted at 530 and 600 ℃, and the model verification was carried out for specimens with elliptical, semi-elliptical, polygon and strip-shaped surface/subsurface inclusion. The stress analysis was performed by finite element simulation and the predicted life was calculated. The results showed a satisfying agreement between predicted and experimental life.

Fatigue Life Prediction of Rolling Bearings Based on Modified SWT Mean Stress Correction

The existing engineering empirical life analysis models are not capable of considering the constitutive behavior of materials under contact loads;as a consequence,these methods may not be accurate to predict fatigue lives of roll-ing bearings.In addition,the contact stress of bearing in operation is cyclically pulsating,it also means that the bear-ing undergo non-symmetrical fatigue loadings.Since the mean stress has great effects on fatigue life,in this work,a novel fatigue life prediction model based on the modified SWT mean stress correction is proposed as a basis of which to estimate the fatigue life of rolling bearings,in which,takes sensitivity of materials and mean stress into account.A compensation factor is introduced to overcome the inaccurate predictions resulted from the Smith,Watson,and Topper(SWT)model that considers the mean stress effect and sensitivity while assuming the sensitivity coefficient of all materials to be 0.5.Moreover,the validation of the model is finalized by several practical experimental data and the comparison to the conventional SWT model.The results show the better performance of the proposed model,especially in the accuracy than the existing SWT model.This research will shed light on a new direction for predicting the fatigue life of rolling bearings.

Mold Wear During Die Forging Based on Variance Analysis and Prediction of Die Life

A process parameter optimization method for mold wear during die forging process is proposed and a mold life prediction method based on polynomial fitting is presented,by combining the variance analysis method in the orthogonal test with the finite element simulation test in the forging process.The process parameters with the greatest influence on the mold wear during the die forging process and the optimal solution of the process parameters to minimize the wear depth of the mold are derived.The hot die forging process is taken as an example,and a mold wear correction model for hot forging processes is derived based on the Archard wear model.Finite element simulation analysis of die wear process in hot die forging based on deform software is performed to study the relationship between the wear depth of the mold working surface and the die forging process parameters during hot forging process.The optimized process parameters suitable for hot forging are derived by orthogonal experimental design and analysis of variance.The average wear amount of the mold during the die forging process is derived by calculating the wear depth of a plurality of key nodes on the mold surface.Mold life for the entire production process is predicted based on average mold wear depth and polynomial fitting.

Position Encoding Based Convolutional Neural Networks for Machine Remaining Useful Life Prediction

Accurate remaining useful life(RUL)prediction is important in industrial systems.It prevents machines from working under failure conditions,and ensures that the industrial system works reliably and efficiently.Recently,many deep learning based methods have been proposed to predict RUL.Among these methods,recurrent neural network(RNN)based approaches show a strong capability of capturing sequential information.This allows RNN based methods to perform better than convolutional neural network(CNN)based approaches on the RUL prediction task.In this paper,we question this common paradigm and argue that existing CNN based approaches are not designed according to the classic principles of CNN,which reduces their performances.Additionally,the capacity of capturing sequential information is highly affected by the receptive field of CNN,which is neglected by existing CNN based methods.To solve these problems,we propose a series of new CNNs,which show competitive results to RNN based methods.Compared with RNN,CNN processes the input signals in parallel so that the temporal sequence is not easily determined.To alleviate this issue,a position encoding scheme is developed to enhance the sequential information encoded by a CNN.Hence,our proposed position encoding based CNN called PE-Net is further improved and even performs better than RNN based methods.Extensive experiments are conducted on the C-MAPSS dataset,where our PE-Net shows state-of-the-art performance.

Deep Neural Networks Based Approach for Battery Life Prediction

The Internet of Things(IoT)and related applications have witnessed enormous growth since its inception.The diversity of connecting devices and relevant applications have enabled the use of IoT devices in every domain.Although the applicability of these applications are predominant,battery life remains to be a major challenge for IoT devices,wherein unreliability and shortened life would make an IoT application completely useless.In this work,an optimized deep neural networks based model is used to predict the battery life of the IoT systems.The present study uses the Chicago Park Beach dataset collected from the publicly available data repository for the experimentation of the proposed methodology.The dataset is pre-processed using the attribute mean technique eliminating the missing values and then One-Hot encoding technique is implemented to convert it to numerical format.This processed data is normalized using the Standard Scaler technique.Moth Flame Optimization(MFO)Algorithm is then implemented for selecting the optimal features in the dataset.These optimal features are finally fed into the DNN model and the results generated are evaluated against the stateof-the-art models,which justify the superiority of the proposed MFO-DNN model.