Life Prediction Of Rolling Bearings Research Articles

Remaining life prediction of rolling bearings with secondary feature selection and BSBiLSTM

Rolling element bearings are critical components in rotating machinery. To tackle the problem of difficult to accurately characterize the operating state of rolling bearings caused by irrelevance and varying sensitivity of multiple features to performance degradation, and introduction of subjective errors in determination of hyperparameters of deep learning models, which can affect the accuracy and efficiency of remaining useful life (RUL) prediction. To address these challenges, this paper proposed a novel RUL prediction method for rolling bearings with secondary feature selection and Bayesian optimization of self-attention mechanisms for bidirectional long short-term memory (BSBiLSTM). Firstly, multi-domain features are extracted from noise-reduced vibration signals. Then, a three-criterion constraint-based feature selection algorithm is used and a secondary selection algorithm with Pearson correlation coefficient is proposed to improve data quality. Next, the 3σ criterion is integrated to determine the first prediction time for rolling bearings and to divide the degradation stage. Subsequently, the BiLSTM model with Bayesian optimization and self-attention mechanism is proposed to predict the RUL of rolling bearings to further improve the algorithm efficiency. Finally, experimental validation is carried out based on the PRONOSTIA platform dataset and the XJTU-SY rolling bearing dataset, and the results show that the method proposed in this paper is better than many mainstream life prediction methods for rolling bearings at present, and the prediction accuracy is higher.

Remaining useful life prediction of rolling bearings based on performance evaluation and multifractional generalized Cauchy model with adaptive drift

The proposed Remaining Useful Life (RUL) prediction framework utilizes several advanced techniques to accurately estimate the remaining service life of rolling bearings. The framework includes early failure assessment, adaptive failure threshold (FT) determination, and a multifractional generalized Cauchy model (MfGC). The early failure assessment is enabled by establishing early FTs and health indicator (HI) curves generated by the Mahalanobis distance cumulative sum (MD-CUSUM) technique. The proposed dynamic fault threshold update method uses the BOX-COX transformation and Chebyshev inequality to determine confidence intervals for evaluating the fault threshold time. The multifractional nature of the MfGC process is characterized by independent, time-varying Hurst indices and fractional dimensions, and the long-range dependence (LRD) characteristics and stochasticity of the process are explained by the diffusion terms generated from the MfGC differential time series. The MfGC model with adaptive drift is constructed for various degenerate trajectories, and a method for estimating the model’s parameters is proposed. The effectiveness of the proposed RUL prediction method is demonstrated using the XJTU-SY bearing dataset.

A novel cross-domain adaption network based on Se-Sk-DenseNet for remaining useful life prediction of rolling bearings under different working conditions

Effectively predicting the remaining useful life (RUL) of rolling bearings can ensure reliability and safety, minimize machine downtime, and reduce the operation and maintenance costs of enterprises. To solve the problems of data distribution discrepancy caused by different working conditions and the collected signals containing a lot of useless information and noise, a novel cross-domain adaption network (CDAN) is proposed in this study. Firstly, a novel feature extractor, squeeze-and-excitation (Se)-selective kernel (Sk)-DenseNet, is developed to extract useful critical features from the input data and remove the ineffective features by embedding Se and Sk attention blocks; besides, a new objective loss function consist of the RUL loss, the multi-kernel maximum mean discrepancy loss, the contrastive loss, and the Kullback–Leibler divergence loss, is proposed to solve the problem of data distribution shift; finally, the effectiveness and superiority of CDAN are proved on the PHM2012 bearings dataset. The results demonstrate that CDAN can extract deep critical features and achieve the high cross-domain RUL prediction accuracy under different working conditions.

Temporal multi-resolution hypergraph attention network for remaining useful life prediction of rolling bearings

Temporal multi-resolution hypergraph attention network for remaining useful life prediction of rolling bearings

Research on the Remaining Life Prediction Method of Rolling Bearings Based on Optimized TPA-LSTM

The whole life cycle degradation data set of rolling bearings has the characteristics of large capacity, diversity, and non-stationarity. As a powerful tool for processing such time series data in deep learning algorithms, LSTM is prone to the loss of important time series information in the process of the life prediction of rolling bearings, which leads to a decline in prediction accuracy. Therefore, a method for predicting the remaining useful life (RUL) of rolling bearings based on the combination of temporal pattern attention mechanism (TPA) and LSTM is proposed. The method firstly combines hierarchical clustering and principal component analysis (PCA) to construct a multi-faceted and multi-scale preferred feature set reflecting the degradation information of rolling bearings, then strengthens the information correlation between hidden layers of the LSTM model through TPA and optimates the parameters of the fusion model of TPA and LSTM by using the gazelle optimization algorithm (GOA). Finally, the model is applied to the experimental data set of rolling bearing degradation. The results show that, compared with the traditional model, this method is more suitable for the remaining life prediction of rolling bearings.

Remaining Useful Life Prediction of Rolling Bearing Based on Multi-Domain Mixed Features and Temporal Convolutional Networks

For the remaining useful life (RUL) prediction of rolling bearing under strong background noise, it is hard to get accurate results based on the non-stationary vibration signals because of complex degradation characteristics and difficult extraction of key features. The framework of RUL prediction for rolling bearing is established by integrating multi-domain mixed features and temporal convolutional network (TCN). The variational mode decomposition method based on the dung beetle optimization algorithm is developed to reduce signal noise by determining the optimal parameters adaptively. To construct a health indicator of rolling bearing effectively, an isometric feature mapping algorithm is introduced to reduce the dimensionality of multi-domain mixed features, integrating time-domain, frequency-domain, and entropy features of vibration signals under non-stationary and nonlinear conditions. By considering the advantages of a multi-head attention mechanism (MA) and bidirectional gated recurrent unit (BiGRU), a TCN-based multi-head attention and bidirectional gate (TCNMABG) is developed to predict the RUL of rolling bearing accurately, whose detailed implementation process of TCNMABG is described based on XJTU-SY dataset. To verify the performance of TCNMABG, the FEMTO-ST dataset is introduced to perform the numerical experiments, and the results show that prediction error is reduced by 65.96% on average.

Life Prediction of Rolling Bearing Based on Optimal Time-Frequency Spectrum and DenseNet-ALSTM.

To address the challenges faced in the prediction of rolling bearing life, where temporal signals are affected by noise, making fault feature extraction difficult and resulting in low prediction accuracy, a method based on optimal time-frequency spectra and the DenseNet-ALSTM network is proposed. Firstly, a signal reconstruction method is introduced to enhance vibration signals. This involves using the CEEMDAN deconvolution method combined with the Teager energy operator for signal reconstruction, aiming to denoise the signals and highlight fault impacts. Subsequently, a method based on the snake optimizer (SO) is proposed to optimize the generalized S-transform (GST) time-frequency spectra of the enhanced signals, obtaining the optimal time-frequency spectra. Finally, all sample data are transformed into the optimal time-frequency spectrum set and input into the DenseNet-ALSTM network for life prediction. The comparison experiment and ablation experiment show that the proposed method has high prediction accuracy and ideal prediction performance. The optimization terms used in different contexts in this paper are due to different optimization methods, specifically the CEEMDAN method.

Digital twin-driven graph domain adaptation neural network for remaining useful life prediction of rolling bearing

Remaining useful life (RUL) prediction is significant for the healthy operation of machinery. In order to accurately identify the bearing degeneration states, it is necessary to collect massive full lifecycle data. However, the bearing lifecycle data is insufficient for effectively training a RUL prediction model in engineering practice. In this paper, a digital twin-driven graph domain adaptation method is proposed. First, a full lifecycle dynamic twin model of bearings is constructed to generate abundant twin data, in which the surface morphology evolution and roller relative slip at different stages are simulated to generate vibration responses. Second, a novel multi-layered cross-domain gated graph convolutional network (MGGCN) is developed, in which a new graph domain adaptation model is designed to solve the problem that traditional domain adaptation methods are not effective in processing the non-Euclidean data. The spatial and temporal features are extracted by multiple nonlinear transformations and previous time-step hidden state incorporation, respectively. In addition, a graph Laplacian regularized maximum mean discrepancy (GLMMD) is designed and applied in the training of model to enhance the capability of discerning graph domain differences. The experimental results confirm that the proposed method can achieve effective performance even in scenarios with limited actual data.

A Multisource Scale Subdomain Adaptation Network With Multihead Hybrid Self-Attention for Remaining Useful Life Prediction of Rolling Bearings

A Multisource Scale Subdomain Adaptation Network With Multihead Hybrid Self-Attention for Remaining Useful Life Prediction of Rolling Bearings

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.

Multivariate Phase Space Warping-Based Degradation Tracking and Remaining Useful Life Prediction of Rolling Bearings

Multivariate Phase Space Warping-Based Degradation Tracking and Remaining Useful Life Prediction of Rolling Bearings

A life prediction method based on MDFF and DITCN-ABiGRU mixed network model

A single network model exhibits limitations in the life prediction of rotating machinery for the various fault types and uncertain fault occurrence. Therefore, a network prediction model combining multi-domain feature fusion (MDFF) and distributed TCN-Attention-BiGRU (DITCN-ABiGRU) is proposed to enable a more accurate life prediction of rotating machinery. Firstly, the features of vibration signals collected from multiple sensors are extracted in the time, frequency, and time-frequency domains. Subsequently, dimensionality reduction optimization is conducted on these multi-domain features to eliminate useless information features. The temporal convolutional network (TCN) model is constructed to capture the critical information reflecting the fault characteristics of rotating machinery through the attention mechanism, and the dependencies of the whole training process are captured by the BiGRU network. Finally, precise prediction of the lifespan of rotating machinery is achieved by constructing a health indicator curve (HI). The proposed methods are verified through the life prediction of rolling bearings from the IEEE PHM Challenge 2012 dataset and ball screw pairs from a designed experiment. The experimental results show that the proposed MDFF and DITCN-ABiGRU model achieves a better score and lower error than the convolutional neural network (CNN) and GRU models.

Remaining useful life prediction across machines using multi-source adversarial online knowledge distillation

Deep transfer learning has been extensively developed in the remaining useful life prediction of rolling bearings because it can decrease the dependence on massive labeled data and robustly extract adaptive features. Most existing studies perform remaining useful life predictions across different working conditions within the same machine. Therefore, these methods are unsuitable for data with severe distribution drifts. Only a few studies investigate the remaining useful life prediction across different devices, but their success depends on the prerequisite that a large amount of labeled data exists. In brief, the models' applicabilities and prediction performances are significantly constrained toward industrial scenarios. To address these drawbacks, a multi-source adversarial online knowledge distillation approach is proposed for rolling bearing remaining useful life prediction across machines. A dual knowledge transfer mechanism, including multi-level domain adaptation and online knowledge distillation with dynamic weighting, is designed in the ensemble learning architecture. The proposed approach allows the more comprehensive extraction and transition of prognostic knowledge of multiple working conditions across different machines, thereby improving the performance of remaining useful life prediction. Experiments on two open-access rolling bearing datasets demonstrate the effectiveness and superiority of the proposed approach in terms of prediction accuracy.

Remaining useful life prediction for bearing based on automatic feature combination extraction and residual multi-Head attention GRU network

Rolling bearings are indispensable parts in mechanical equipment, and predicting their remaining useful life is critical to normal operation and keep equipment in good repair. However, the complex characteristics of bearings make it difficult to describe their degradation characteristics. To address this issue, a novel method that combines an automatic feature combination extraction mechanism with a gated recurrent unit (GRU) network that has a residual multi-head attention mechanism for rolling bearing life prediction is proposed. Firstly, the automatic feature combination extraction mechanism is used to learn the degradation representation of the bearing vibration signal in the time domain, frequency domain, and time–frequency joint domain, and automatically extract the optimal bearing degradation feature combination. Then, the GRU network with residual multi-head attention mechanism is developed to weight and distinguish the learned degradation features, thereby improving the network’s attention to important bearing degradation features. In the end, the proposed method is validated on the prediction and the health management of systems dataset and compared to other advanced approaches. The experimental results show that the proposed method can effectively capture the complex and dynamic features of rolling bearings and has high accuracy and generalization ability in rolling bearing life prediction.

A model for remaining useful life prediction of rolling bearings based on the IBA-FELM algorithm

Rolling bearings are essential supporting components for most rotating machinery and are commonly placed at great risk of sudden failure. Accurate prediction of the remaining service life of rolling bearings is essential for ensuring reliable operation and establishing an effective maintenance strategy. Focusing on the extreme learning machine (ELM) methodology, an innovative predictive model with error feedback neuron integration is established to eliminate the deficiency in model generalization capability. To further improve the predictive accuracy, an improved bat algorithm (IBA) is introduced into the FELM model, in which the Levy flight and frequency influence factor are embedded into the traditional BA algorithm to enhance the parameter searching ability. Inverse hyperbolic function-based statistical indicators are proposed and verified by comparing with the classical RMS curve of full-life data, whose cosine similarity and correlation coefficient both exceed 0.95. Two sets of accelerated life experiments were selected to validate the effectiveness of the proposed IBA-FELM model. The results show that the integrated model can obtain high prediction accuracy and satisfactorily fit the real-life data. The maximal prediction error can be reduced from 1.57 to 0.0401 for experimental Case 1, and from 0.7375 to 0.1492 for Case 2. Compared with the other machine learning models, such as SVR, CNN, and LSTM networks, the IBA-FELM model also presents stronger optimization ability, higher generalization performance, and operation stability.

Residual Life Prediction of Rolling Bearings Based on a CEEMDAN Algorithm Fused with CNN–Attention-Based Bidirectional LSTM Modeling

This paper presents a methodology for predicting the remaining usability of rolling bearings. The method combines a fully adaptive ensemble empirical modal decomposition of noise (CEEMDAN), convolutional neural network (CNN), and attention bidirectional long short-term memory network (ABiLSTM). Firstly, a finite number of intrinsic mode functions (IMFs) are obtained from breaking down the initial vibration signals using CEEMDAN. The IMFs are further screened by combining the correlation criterion and the craggy criterion. Then, time-frequency domain features, which are extracted from the screened IMFs, are reconstructed into a feature set. The SPT is recognized through some features, like the root mean square (RMS), variance, and kurtosis. Secondly, the deterioration character of rolling bearings was extracted using CNN and used to train the ABiLSTM network. Based on the output of the ABiLSTM network, it forecasts how long rolling bearings will last during use. Finally, the XJTU-SY rolling bearing dataset validated the validity of the suggested rolling bearing remaining life prediction method. We compare our algorithm with other algorithms, such as GRU, LSTM, and CNN–BiLSTM, in which the accuracy of MAE, MSE, RMSE, MAPE, and R2_score is significantly improved. Thus, the results of the validation experiments demonstrate that our proposed algorithm has excellent prediction accuracy.

A novel continuous delay hidden layer deep belief network and its application in life prediction of rolling bearings

The traditional rolling bearing trend prediction method is difficult to predict the bearing degradation process comprehensively and scientifically, and the feature extraction is highly dependent on the manual experience method, and the prediction ability of the bearing measurement data with strong dynamic characteristics and nonlinear is very limited. Therefore, how to build a high-precision life prediction model has become an urgent problem and challenge. In this paper, a continuous delay hidden layer deep belief network (CDHLDBN), kernel principal component analysis (KPCA)-CDHLDBN, is proposed based on the KPCA preprocessing method. Firstly, a continuous deep belief network (CDHLDBN) with a delay layer is proposed, which makes the output of the hidden layer more fully consider the influence of the previous moment on the output of this moment, so as to avoid the defect of DBN model which is difficult to process the data with strong dynamic time series. Then, all visible layers of a continuous DBN with a delay layer are de-averaged, and then the simulated bearing sequence data is trained and predicted using the KPCA-CDHLDBN life prediction model. The results show that KPCA-CDHLDBN model can predict nonlinear dynamic time series data better than other deep learning models. Finally, it is applied to the performance degradation life prediction of rolling bearings. The performance of the proposed method was verified by the bearing accelerated life vibration data provided by PRONOSTIA experimental platform, and the performance of the proposed method was compared with that of the traditional DBN and its improved prediction model. The results show that KPCA-CDHLDBN has better prediction accuracy and faster prediction speed.

Multi-representation transferable attention network for remaining useful life prediction of rolling bearings under multiple working conditions

Remaining useful life (RUL) prediction improves equipment reliability by avoiding unexpected breakdowns. However, different operating conditions may cause the performance degradation of the prediction model due to the domain-shift problem. Therefore, a novel method based on the multiple representation transferable attention network (MRTAN) is proposed in this paper for RUL prediction under multiple working conditions. In the MRTAN-based method, multiple convolutional modules are first used to learn the degradation features. Next, the multi-representation adaptation module is utilized to further mine the multiple domain-invariant representations. Then, during the training process, the transferable attention module can activate the representation with high transferability dynamically. Finally, the prognostic model is optimized by multiple optimization objectives and the back-propagation algorithm. Besides, multiple cross-domain RUL prediction tasks are employed to validate the effectiveness of the MRTAN-based model. Experiments demonstrate that the proposed method can provide better prognostic performance and avoid the negative transfer problem.

Remaining useful life prediction of rolling bearings based on TCN-MSA

As a pivotal element within the drive system of mechanical equipment, the remaining useful life (RUL) of rolling bearings not only dictates the lifespan of the equipment’s drive system but also the overall machine. An inaccurate prediction of the RUL of rolling bearings could hinder the formulation of maintenance strategies and lead to a chain of failures stemming from bearing malfunction, culminating in potentially catastrophic accidents. This paper designs a novel temporal convolutional network-multi-head self-attention (TCN-MSA) model for predicting the RUL of rolling bearings. This model considers the intricate non-linearity and complexity of mechanical equipment systems. It captures long-term dependencies using the causally inflated convolutional structure within the temporal convolutional network (TCN) and simultaneously extracts features from the frequency domain signal. Subsequently, by employing the multi-head self-attention (MSA) mechanism, the model discerns the significance of different features throughout the degradation process of rolling bearings by analyzing global information. The final prediction for rolling bearings’ RUL has been successfully attained. To underline the excellence of the method presented in this paper, a comparative analysis was performed with existing methods, such as convolutional neural network, gate recurrent unit, and TCN. The results highlight that the model designed in this paper surpasses other existing methods in predicting the RUL of rolling bearings, demonstrating superior prediction accuracy and robust generalization capability.