Life Prediction Method Research Articles

Wear life prediction of piston ring and cylinder liner based on integration of experiment and bench test data

The wear data of piston ring-cylinder liner (PRCL) of high-power diesel engines faces the problems of small samples, randomness, and underutilization, which leads to the wear life prediction of PRCL has been a difficult task in the engineering field. A wear life prediction method of PRCL based on the integration of experimental data and bench test data is proposed. First, a large amount of wear data is rapidly obtained through accelerated wear tests on specimens, and the distribution type of wear depth of PRCL is determined. Secondly, based on Rhee's wear equation, the nonlinear relationship between time, load, temperature, and wear depth is considered, and the acceleration models of temperature and load were introduced as the Arrhenius model and the inverse power law model, respectively. The wear life prediction model (WLPM) for specimens and parts with stochasticity is developed. The eigenvalues of WLPM’s parameters of specimens and parts were utilized to construct the mapping relationship from the specimen to the part. Finally, the Bayesian statistical method is used to merge the wear data of specimens and the parts, and the WLPM of specimens and the mapping relationship are taken as the a priori information, the bench data are taken as the new sample information, and the MCMC-Gibbs sampling method is used to estimate the mapping-based WLPM of parts. The results show that through the validation test, the method predicts the wear depth of PRCL with 100 % falling into the 95 % confidence interval better than the WLPM prediction results of a single specimen or part, and the errors of predicting the mean wear life of PRCL are all within 10 %. The method can provide a reference for the quality evaluation and engineering application of PRCL.

Fatigue life prediction of composite bolted joints based on finite element model and machine learning

AbstractThis study proposes a fatigue life prediction method for composite bolted joints, which combines algorithm optimization‐based hybrid neural networks with finite element modeling. First, based on the Hashin failure criterion of physical mechanism, a finite element model for fatigue life prediction of composite bolted joints is established, and the simulation calculations have been conducted using various initial conditions. Then, by integrating the simulation and experiment data, we have established a fatigue life database that serves machine learning training and prediction. Finally, the data undergo a comprehensive process of deep feature extraction through the utilization of a convolutional neural network (CNN). The resulting deep features are utilized as inputs for training the backpropagation neural network (BPNN) to predict fatigue life. The results indicate this synergistic combination of CNN and BPNN results in a substantial improvement in prediction accuracy and has remarkable superiority in predicting the fatigue life of composite bolted joints.

A dual‐channel transferable RUL prediction method integrated with Bayesian deep learning and domain adaptation for rolling bearings

AbstractMany deep learning methods typically assume that the marginal probability distribution between the training and testing bearing data is similar or the same. However, the probability distribution of rolling bearings may deviate significantly under diverse working conditions. To address the above limitations, a novel transferable remaining useful life (RUL) prediction method integrated with Bayesian deep learning and unsupervised domain adaptation (DA) is proposed. First, the signal alignment is executed on the data after the first prediction time to maintain the same granularity and scale across both source and target domains. Second, the multi‐domain features are extracted and sent into the dual‐channel Transformer network (DCTN) incorporating the convolutional block attention module (CBAM) to adequately exploit the abundant degradation information. Then, the DA module is incorporated into the model to mitigate the distribution discrepancies of the extracted high‐level merged features between the source and target domains. Finally, by applying the variational inference method, the DCTN‐CBAM is extended to the Bayesian deep neural network, and the RUL prediction and its corresponding confidence intervals can be conveniently derived. In addition, the generalization capability and effectiveness are validated through six bidirectional transfer RUL prediction tasks across two rolling bearing datasets. The experimental results demonstrate that it could provide a more reliable RUL prediction and efficiently account for the prediction uncertainty.

Reliability-based life prediction method for concrete sewage pipelines under microbial attack

The durability of concrete sewage pipelines decreased for microbial attack. In this study, a method for life prediction of concrete sewage pipes under microbial-induced attack was developed by combining numerical simulation with theoretical analysis. A mathematical model of the corrosion depth of microbial erosion of concrete was constructed based on the cumulative model of sulfate concentration on the surface of sewage pipes and verified the applicability by comparing it with the experimental results. The Monte Carlo (MC) life prediction method and Fuzzy- Monte Carlo (F-MC) life prediction method for concrete sewage pipes was established based on the reliability theory and fuzzy methodology. Several factors are considered as random variables and analyzed parametrically, including hydrogen sulfide concentration (SH2S), diffusion coefficient (D), and thickness of the concrete cover (CC). In addition failure event was consider as a fuzzey event. It was found that: The increasing SH2S and D result in higher failure rates and lower service life, however as the increase of the thickness of CC, there is the opposite effect. The results of the numerical simulations revealed a NR between the three kinds of parameters and the probability of failure of sewer pipes. Compared with the MC model, the F-Mc model produces more conservative but not minimal different results, which confirms the applicability of the F-MC model.

Remaining useful life prediction method for Degradation–Shock dependence: Case of a subsea hydraulic control system

Predicting the remaining useful life (RUL) is crucial for ensuring reliable equipment operation. This study addresses the challenges of RUL prediction in modeling the effects of external shocks and internal degradation, with emphasis on the dependency relationship between the two factors. A novel hybrid method for RUL prediction is proposed. The proposed method combines degradation models and field data to realize RUL assessment under the influence of system degradation and external shocks. A dependency factor is introduced to effectively incorporate the influence of degradation stage and shock intensity on system failure. A comprehensive uncertainty analysis is performed, in consideration of random degradation, unpredictable shock events, and the dynamic evolution of degradation stages and performance degradation processes. Numerical simulations are conducted on a hydraulic control system to demonstrate the effectiveness and accuracy of the proposed method. The robustness and reliability of the proposed approach in predicting the RUL of the hydraulic control system are demonstrated, with particular emphasis on the method's capability to handle the complexities of dependency modeling and uncertainty analysis. Results show that the proposed method can realize dependency modeling of the hydraulic control system, accurately predict its RUL, and analyze the vulnerability of its subsystems.

Fatigue performance analysis of precast segmental assembled concrete beams

In recent years, assembled bridges have become widely utilized in bridge construction, raising concerns about durability-related bridge diseases over time. These issues significantly impact the fatigue life of assembled bridges, necessitating an in-depth exploration of their fatigue performance. While existing research primarily concentrates on the transverse connection of multiple longitudinal beams, there is a notable dearth of studies on longitudinal precast segmental assembled bridges. This paper addresses this gap by establishing a fatigue benchmark finite element model for segmental assembled concrete beams, building upon existing experiments. The study employs numerical simulation to analyze the entire fatigue process, examining stress distribution, damage development, and considering the influence of reinforcement corrosion. Furthermore, a fatigue life prediction method, based on fatigue residual strength (R), is proposed for predicting the fatigue life (N) of concrete in precast segmental assembled beams. Results reveal that prestressed and ordinary reinforcements experience increasing stress with loading cycles, peaking around 100,000 cycles. Throughout fatigue loading, compressive stress in concrete remains low, preventing fatigue compression failure. However, tensile stress near joints gradually rises, initiating cracks at the mid-span beam’s bottom. With continued cyclic loading, these cracks propagate towards the loading point. The upper and lower limits of fatigue life predicted by the fatigue life prediction method closely align with the compressive fatigue test values of concrete, proposed fatigue life prediction method is efficient and accurate.

A non-stationary transformer-based remaining useful life prediction method for proton exchange membrane fuel cells

The remaining useful life (RUL) is one of the critical factors for proton exchange membrane fuel cells (PEMFCs), as it is hindering the commercialization of PEMFCs in various fields. In this research, a novel deep learning (DL) algorithm, i.e. the Non-stationary Transformer is newly applied to the RUL prediction of a PEMFC stack. For better prediction accuracy, the discrete wavelet transform (DWT) is utilized to denoise the original data before the normalization process. Prediction results of the proposed Non-stationary Transformer-based PEMFC RUL prediction method are analyzed under different lengths of training datasets, under different time steps ahead prediction, and for different PEMFC End of Life (EoL) threshold values and compared to those of other DL-based prediction methods including the long short-term memory (LSTM)-based and echo state network (ESN)-based methods. Results show that the proposed method outperforms the LSTM-based and ESN-based methods for all different cases studied in this research.

A hybrid remaining useful life prediction method for lithium-ion batteries based on transfer learning with CDRSN-BiGRU-AM

The prediction of the remaining useful life (RUL) of widely used lithium-ion batteries (LIBs) is of great importance. Existing techniques struggle to balance prediction accuracy with execution time. To achieve accurate RUL prediction quickly, a hybrid RUL prediction method for LIBs has been developed. This method first employs a channel-wise deep residual shrinkage network to adaptively extract features from input data enhancing important information features and suppressing ineffective ones based on the significance of the feature information. Subsequently, a bidirectional gated recurrent unit is used to extract bidirectional temporal features from the processed data, and an attention mechanism is introduced to maximize the extraction of significant temporal mutual information. Finally, a fully connected layer transfer strategy is applied to transition the model from offline training to online prediction, which avoids unstable predictions due to random model initialization and significantly improves the model’s computational efficiency. The simulation results show that the root mean square error of the proposed method did not exceed 1.77% and the mean absolute error did not exceed 1.44% on the NASA dataset. Consequently, the proposed method can achieve accurate online RUL prediction accuracy for LIBs.

A bearing remaining life prediction method under variable operating conditions based on cross-transformer fusioning segmented data cleaning

Bearing remaining useful life (RUL) prediction research based on deep learning mostly emphasizes model performance and effective feature vectors, overlooking different densities of outlier distributions in vibration signals at varying degradation stages. Moreover, forecasting models focus on capturing cross-time dependencies, ignoring the dependencies between different variables. To solve these problems, this paper proposes an unsupervised segmented data cleaning algorithm and a RUL prediction framework adaptable to variable operating conditions. The method consists of four steps: (1) Multi-domain feature extraction and selection establish a feature vector space reflecting degradation trends. (2) Segmented data cleaning divides degradation stages, using different penalty factors for outlier cleaning. (3) Cleaned vibration signals undergo a second round of multidomain feature engineering and degradation-stage division. (4) A two-stage Cross-Transformer model is used for RUL prediction. The method proposed has been validated on the prognostics and health management (PHM) bearing degradation dataset. In the constant condition prediction task, the root mean square error (RMSE) and mean absolute error (MAE) were improved to 1.88 and 5.78, respectively. In the variable condition prediction task, the proposed method outperformed existing methods, with an improvement of 59.10 % in RMSE, demonstrating strong generalization performance and practical application value.

Multi-Stage Hybrid Loading Fatigue Life Prediction Study

Abstract Fatigue is a damage accumulation process in which material properties are continuously deteriorated and degraded under cyclic loading. Damage accumulation analysis plays a key role in preventing the occurrence of fatigue failure, and the damage evolution mechanism is an important parameter of interest for fatigue behavior. The fatigue cumulative damage life prediction model is established by focusing on the damage evolution mechanism, and an improved Manson–Harford cumulative damage nonlinear model is developed to calculate fatigue damage life by considering the effect of load ratio of multi-stage loading on fatigue life. A fatigue life prediction method considering high-low cycles and multi-stage mixed loads is proposed. The fatigue life prediction accuracy of the model is verified by fatigue tests on structural samples. The accuracy of the fatigue life prediction error is the highest among similar models under the same test, the model proposed in this paper.

Research on the Remaining Life Prediction Method of Rolling Bearings Based on Multi-Feature Fusion

Rolling bearings are one of the most important and indispensable components of a mechanical system, and an accurate prediction of their remaining life is essential to ensuring the reliable operation of a mechanical system. In order to effectively utilize the large amount of data collected simultaneously by multiple sensors during equipment monitoring and to solve the problem that global feature information cannot be fully extracted during the feature extraction process, this research presents a technique for forecasting the remaining lifespan of rolling bearings by integrating many features. Firstly, a parallel multi-branch feature learning network is constructed using TCN, LSTM, and Transformer, and a parallel multi-scale attention mechanism is designed to capture both local and global dependencies, enabling adaptive weighted fusing of output features from the three feature extractors. Secondly, the shallow features obtained by the parallel feature extractor are residually connected with the deep features through the attention mechanism to improve the efficiency of utilizing the information of the front and back features. Ultimately, the combined characteristics produce the forecasted findings for the RUL of the bearing using the fully connected layer, and RUL prediction studies were performed with the PHM 2012 bearing dataset and the XJTU-SY bearing accelerated life test dataset, and the experimental results demonstrate that the suggested method can effectively forecast the RUL of various types of bearings with reduced prediction errors.

A feature-level mask self-supervised assisted learning approach based on transformer for remaining useful life prediction

Nowadays, the massive industrial data has effectively improved the performance of the data-driven deep learning Remaining Useful Life (RUL) prediction method. However, there are still problems of assigning fixed weights to features and only coarse-grained consideration at the sequence level. This paper proposes a Transformer-based end-to-end feature-level mask self-supervised learning method for RUL prediction. First, by proposing a fine-grained feature-level mask self-supervised learning method, the data at different time points under all features in a time window is sent to two parallel learning streams with and without random masks. The model can learn more fine-grained degradation information by comparing the information extracted by the two parallel streams. Instead of assigning fixed weights to different features, the abstract information extracted through the above process is invariable correlations between features, which has a good generalization to various situations under different working conditions. Then, the extracted information is encoded and decoded again using an asymmetric structure, and a fully connected network is used to build a mapping between the extracted information and the RUL. We conduct experiments on the public C-MAPSS datasets and show that the proposed method outperforms the other methods, and its advantages are more obvious in complex multi-working conditions.

An entire life-cycle rolling bearing remaining useful life prediction method using new degradation feature evaluation indicators

The rolling bearing remaining useful life (RUL) prediction is a hot topic issue in the field of rail transportation. The existing RUL prediction methods for rolling bearing have problems such as unreasonable division of rolling bearing degradation stages and incomplete extraction of degradation features by feature selection indicators. In order to solve these problems, an entire life-cycle rolling bearing RUL prediction method using new degradation feature evaluation indicators is proposed. Firstly, the degradation feature evaluation indicator is designed to evaluate the stability of the degradation feature. Then, the combination of stability evaluation indicator and correlation evaluation indicator is used as the basis for feature selection. Secondly, the Gaussian Mixture Model (GMM) method is fused with the Support Vector Machine (SVM) to divide the bearing entire life-cycle into three stages: normal stage, early degradation stage, and degradation stage. Finally, the Long Short-Term Memory (LSTM) network model is trained separately to predict the rolling bearing RUL for different rolling bearing degradation stages. The effectiveness of the proposed prediction method based on different degradation stages of rolling bearing in predicting the RUL of rolling bearing is verified through PRONOSTIA bearing dataset. The comparison with existing methods shows that this approach demonstrates superior accuracy and predictive performance. For example, the Mean Square Error (MSE) evaluation metric has decreased by 60%. The Root Mean Square Error (RMSE) evaluation metric has decreased by 36.5%. The Mean Absolute Error (MAE) evaluation metric has decreased by 48.6%. The Mean Absolute Percentage Error (MAPE) evaluation metric has decreased by 36.9%.

An improved J-integral based adhesive joint fatigue life estimation method for automotive structural durability analysis

ABSTRACT To estimate the fatigue life of lightweight automotive structures in compliance with top-down design principles, there is a need for an accurate adhesive joint fatigue life prediction method that is compatible with industrial finite element modeling practices. This paper proposes a method to split the analytical J-integral solution based on the fracture mode of joints. The split mode I and II J-integrals correspond to the opening mode and sliding mode of the bonded joint, respectively. By splitting the J-integral and introducing the concept of the mixed mode ratio, an improved approach for estimating adhesive joint fatigue life is presented, considering the influence of loading modes. The proposed fatigue life prediction method is validated through fatigue tests on a sub-component level bonded structure. The results of the validation demonstrate that the split J-integral analytical solution method, considering the mixed mode ratio, provides better predictions compared to its predecessor approach.

High-cycle and low-cycle fatigue life prediction under random multiaxial loadings without cycle counting

A novel fatigue life prediction method under random multiaxial loadings is proposed in this paper. One unique benefit of the proposed method is that it is based on the time-derivative fatigue crack growth formulation and does not need cycle counting under arbitrary loadings. First, a brief review of subcycle fatigue crack growth (FCG) analysis and the equivalent initial flaw size (EIFS) concept for life prediction is introduced. Next, the existing subcycle fatigue crack growth model is extended to near-threshold conditions. An intrinsic fatigue threshold of the material is introduced, and a hypothesis is proposed that the crack only grows when the local stress intensity factor is beyond the intrinsic threshold. Following this, the analogy of stress intensity factor and strain intensity factor is used to extend the fatigue life prediction model to strain-based fatigue analysis. Correction for large plastic deformation is included to handle low-cycle fatigue life prediction. The novelties of this model are that it considers the FCG at the threshold and near-threshold region and it is able to predict both HCF and LCF conditions using FCG-based life prediction. Following this, extensive in-house and literature data for AL-7075-T6 under uniaxial and multiaxial, constant, and random loadings are used for model validation. Discussions for the effect of model parameters are provided based on parametric analysis. Conclusions and future work are mentioned. Code and data are released in public cloud service for interested readers.

PINN-AFP: A novel C-S curve estimation method for asphalt mixtures fatigue prediction based on physics-informed neural network

The accurate prediction of fatigue life of asphalt mixture is the key to the design of long-lasting durable pavement. Currently, a critical aspect influencing the accuracy of life prediction methods for asphalt mixtures, based on Viscoelastic Continuum Damage Mechanics (VECD), is the precision of the damage characteristic (C-S) curve. Within the VECD framework, the C-S curve of asphalt mixture is regarded as an intrinsic material property. However, it has been observed in engineering applications that the C-S curve of the material exhibits notable sensitivity to varying loading conditions. The nonlinear fitting method based on a small number of experiments is difficult to fully characterize the fatigue performance of the material, while a large number of complete material fatigue tests are expensive in time and money. Based on the above problems, a physics-informed neural network embedded in VECD, PINN-AFP, is proposed. It can accurately predict the complete material C-S curve based on a small amount of pre-fatigue data of the material, thus achieving accurate prediction of the fatigue life of asphalt mixture. The case study uses the fatigue test data of AC-25 as an example, and the results demonstrate that the proposed PINN-AFP has strong generalization ability and prediction accuracy, achieving the state-of-the-art in the mainstream machine learning and deep learning methods with an average 5.2% fatigue life prediction error.

Transfer Learning-Based Remaining Useful Life Prediction Method for Lithium-Ion Batteries Considering Individual Differences

With the wide utilization of lithium-ion batteries in the fields of electronic devices, electric vehicles, aviation, and aerospace, the prediction of remaining useful life (RUL) for lithium batteries is important. Considering the influence of the environment and manufacturing process, the degradation features differ between the historical batteries and the target ones, and such differences are called individual differences. Currently, lithium battery RUL prediction methods generally use the characteristics of a large group of historical samples to represent the target battery. However, these methods may be vulnerable to individual differences between historical batteries and target ones, which leads to poor accuracy. In order to solve the issue, this paper proposes a prediction method based on transfer learning that fully takes individual differences into consideration. It utilizes an extreme learning machine (ELM) twice. In the first stage, the relationship between the capacity degradation rate and the remaining capacity is constructed by an ELM to obtain the adjusting factor. Then, an ELM-based transfer learning method is used to establish the connection between the remaining capacity and the RUL. Finally, the prediction result is adjusted by the adjusting factor obtained in the first stage. Compared with existing typical data-driven models, the proposed method has better accuracy and efficiency.

Pore-induced fatigue failure: A prior progressive fatigue life prediction framework of laser-directed energy deposition Ti-6Al-4V based on machine learning

Pores are major cause of fatigue failure in laser-directed energy deposition (L-DED) titanium alloy. For the safe application of L-DED titanium alloys, it is essential to establish a fatigue life prediction method based on pore-induced fatigue. This paper proposes a prior progressive fatigue life prediction framework based on ridge classification and kernel ridge regression algorithms. The fatigue life prediction was carried out on L-DED Ti-6Al-4V alloy in three steps: critical pore identification, fine granular area existence prediction and final fatigue life prediction. The fatigue life prediction method adopted in the current study outperform the others with a correlation coefficient as high as 0.951, followed by a comparison with the results derived from different machine learning algorithms. The results show that the proposed fatigue life prediction framework can predict the fatigue life of L-DED Ti-6Al-4V alloy based on computed tomography tests and microstructure features. Due to its strong generalization ability and effectiveness, the proposed prediction method is expected to be valuable for fatigue-resistant design of L-DED Ti-6Al-4V alloy.

Remaining Useful Life Prediction of Rolling Bearings Based on ECA-CAE and Autoformer.

In response to the need for multiple complete bearing degradation datasets in traditional deep learning networks to predict the impact on individual bearings, a novel deep learning-based rolling bearing remaining life prediction method is proposed in the absence of fully degraded bearng data. This method involves processing the raw vibration data through Channel-wise Attention Encoder (CAE) from the Encoder-Channel Attention (ECA), extracting features related to mutual correlation and relevance, selecting the desired characteristics, and incorporating the selected features into the constructed Autoformer-based time prediction model to forecast the degradation trend of bearings' remaining time. The feature extraction method proposed in this approach outperforms CAE and multilayer perceptual-Attention Encoder in terms of feature extraction capabilities, resulting in reductions of 0.0059 and 0.0402 in mean square error, respectively. Additionally, the indirect prediction approach for the degradation trend of the target bearing demonstrates higher accuracy compared to Informer and Transformer models, with mean square error reductions of 0.3352 and 0.1174, respectively. This suggests that the combined deep learning model proposed in this paper for predicting rolling bearing life may be a more effective life prediction method deserving further research and application.

A domain adaptation network with feature scale preservation for remaining useful life prediction of rolling bearings under variable operating conditions

Transfer learning and domain adaptation (DA) methods have been utilized in bearing prognostic and health management, but most of the current DA methods do not take into account the feature scale change of degraded features when aligning the feature distribution, and these methods are more suitable for the classification problem, which is more robust to the feature scale change. However, they perform poorly in regression problems. In addition, most of the remaining useful life (RUL) prediction methods require preprocessing such as statistical feature extraction on the signal, which makes the prediction process complicated. To solve the above problems, a DA method based on the representation subspace distance (RSD) is proposed for predicting the bearing RUL under different operating conditions. First, the proposed convolutional neural network (CNN) self-attention (SA) long short term memory network model is utilized to extract the deep features from the original signal, which overcomes the limitations of the CNN in extracting time series. Then, the RSD in the Riemannian geometry of the Grassmann manifold is proposed as a domain transfer loss to learn domain invariant features. The modified method can align the feature distribution of the source domain and the target domain without changing the feature scale. At the same time, the bases mismatch penalization is introduced to avoid destroying the semantic information of the features in the process of domain alignment. Finally, the effectiveness of the proposed method is verified by experiments on four types of transfer tasks, and its superiority is also demonstrated by comparison with other advanced methods.