Remaining Useful Life Prediction Research Articles

A Remaining Useful Life Prediction Method of Mechanical Equipment Based on Particle Swarm Optimization-Convolutional Neural Network-Bidirectional Long Short-Term Memory

In industry, forecast prediction and health management (PHM) is used to improve system reliability and efficiency. In PHM, remaining useful life (RUL) prediction plays a key role in preventing machine failures and reducing operating costs, especially for reliability requirements such as critical components in aviation as well as for costly equipment. With the development of deep learning techniques, many RUL prediction methods employ convolutional neural network (CNN) and long short-term memory (LSTM) networks and demonstrate superior performance. In this paper, a novel two-stream network based on a bidirectional long short-term memory neural network (BiLSTM) is proposed to establish a two-stage residual life prediction model for mechanical devices using CNN as the feature extractor and BiLSTM as the timing processor, and finally, a particle swarm optimization (PSO) algorithm is used to adjust and optimize the network structural parameters for the initial data. Under the condition of lack of professional knowledge, the adaptive extraction of the features of the data accumulated by the enterprise and the effective processing of a large amount of timing data are achieved. Comparing the prediction results with other models through examples, it shows that the model established in this paper significantly improves the accuracy and efficiency of equipment remaining life prediction.

Ultra-Long-Term Spatiotemporal Feature Fusion Network for Bearing Remaining Life Prediction under Strong Interference

Abstract Under high noise conditions and random impacts, which constitute strong interference, models often exhibit limited capability in capturing long-term dependencies, leading to lower accuracy in predicting the Remaining Useful Life (RUL) of bearings. To address this issue, a spatiotemporal fusion network capable of ultra-long-term feature analysis is proposed to enhance the accuracy of bearing RUL prediction under substantial interference. This network utilizes a dilated convolution-based lightweight vision transformer encoder to extract spatial features reflecting the short-term degradation state of the bearing. Then, these features are sequentially fed into an adaptive tiered memory unit, based on the multiple attention mechanism and the neuron layering mechanism, to analyze temporal features indicative of long-term degradation. Subsequently, short-term spatial and long-term temporal features are fused for RUL prediction. To validate the robustness and predictive accuracy of the proposed approach under strong interference, a gearbox-rolling bearing accelerated platform is constructed, simulating high noise and random impact conditions. Experiments confirm the high robustness and predictive accuracy of the proposed method under strong interference conditions.

AsdinNorm: A Single-Source Domain Generalization Method for the Remaining Useful Life Prediction of Bearings

AsdinNorm: A Single-Source Domain Generalization Method for the Remaining Useful Life Prediction of Bearings

Joint learning strategy of multi-scale multi-task convolutional neural network for aero-engine prognosis

Remaining useful life (RUL) prediction and health status (HS) assessment are two key tasks in aero-engine prognostics and health management (PHM) system. However, existing deep learning-based prognostic models perform RUL prediction and HS assessment tasks separately, without considering the correlation between these two tasks. Secondly, traditional deep learning can only extract single-scale features, which limits the ability to extract complex degradation features from high-dimensional condition monitoring data. Therefore, this work proposes a multi-scale and multi-task convolutional neural network for joint learning of aero-engine RUL prediction and HS assessment. Firstly, multi-sensor data with multiple cycles are converted into image samples to integrate more condition monitoring information that is beneficial to prognosis. Then, the multi-scale feature fusion block is designed as the shared network for multi-task, utilizing convolutional layers with filters of different sizes to enhance the ability to extract complex degradation features from high-dimensional condition monitoring data. And a multi-layer concatenation block is constructed to integrate multi-scale features at different levels to fully utilize the important information at different levels. On this basis, a multi-task joint learning block is constructed and a joint loss function is developed for joint learning of RUL prediction and HS assessment. Finally, experiments on two engine degradation datasets, CMAPSS and N-CMAPSS, demonstrate that the proposed network has excellent RUL prediction and HS assessment performance, and outperforms other state-of-the-art methods.

Rolling Bearing Remaining Useful Life Prediction Based on CNN-VAE-MBiLSTM

Ensuring precise prediction of the remaining useful life (RUL) for bearings in rolling machinery is crucial for preventing sudden machine failures and optimizing equipment maintenance strategies. Since the significant interference encountered in real industrial environments and the high complexity of the machining process, accurate and robust RUL prediction of rolling bearings is of tremendous research importance. Hence, a novel RUL prediction model called CNN-VAE-MBiLSTM is proposed in this paper by integrating advantages of convolutional neural network (CNN), variational autoencoder (VAE), and multiple bi-directional long short-term memory (MBiLSTM). The proposed approach includes a CNN-VAE model and a MBiLSTM model. The CNN-VAE model performs well for automatically extracting low-dimensional features from time–frequency spectrum of multi-axis signals, which simplifies the construction of features and minimizes the subjective bias of designers. Based on these features, the MBiLSTM model achieves a commendable performance in the prediction of RUL for bearings, which independently captures sequential characteristics of features in each axis and further obtains differences among multi-axis features. The performance of the proposed approach is validated through an industrial case, and the result indicates that it exhibits a higher accuracy and a better anti-noise capacity in RUL predictions than comparable methods.

An adaptive multi-scale feature fusion and adaptive mixture-of-experts multi-task model for industrial equipment health status assessment and remaining useful life prediction

Health status (HS) assessment and remaining useful life (RUL) prediction are two essential tasks in prognostics and health management. Though HS assessment and RUL prediction based on multi-task learning have achieved considerable progress, the following three challenges still exist. Firstly, it is assumed that these two tasks are related, and the inter-task correlation is ignored. Secondly, it is difficult to extract complex degradation features using the traditional deep network as the shared-bottom structure. Thirdly, inter-task heterogeneity is not considered, which limits the model's generalization ability to different tasks. To overcome these challenges, this work proposes an adaptive multi-scale feature fusion and adaptive mixture-of-experts multi-task model for HS assessment and RUL prediction. By exploiting inter-task affinity to measure the correlation between tasks, it provides guidance for multi-task modeling of industrial equipment. Then, an adaptive multi-scale feature fusion mechanism is designed as the shared-bottom structure to improve the feature extraction ability of the multi-task model. Besides, considering inter-task heterogeneity, an adaptive mixture-of-experts mechanism is developed to adaptively fuse expert features for specific tasks, which enhances the model's adaptability and generalization to different tasks. Finally, experiments on engine degradation data and tool wear data demonstrate that the proposed model outperforms other state-of-the-art baselines.

A Bayesian adversarial probsparse Transformer model for long-term remaining useful life prediction

Long-term remaining useful life (RUL) prediction is essential for the maintenance of safety-crucial engineering assets. Deep learning (DL) models, especially Transformer-based models have achieved outstanding performance in long-term RUL prediction. However, existing Transformer models neglect the impact of discrepancy loss in model training. The accumulation of the discrepancy loss during the inference will hamper the generalization of prediction model, resulting in an overfitting problem. To address the problem, this paper proposes a Bayesian Adversarial Probsparse Transformer (BAPT) model for long-term RUL prediction. Firstly, the adversarial learning method is leveraged to mitigate the impact of accumulated discrepancy loss caused by varying working conditions in long-term prediction, thus diminishing the error accumulation. Secondly, the Probsparse multi-head attention is adopted to enhance the efficiency of feature extraction. The Probsparse multi-head attention focuses on the significant degradation features in long time-series to reduce the computation complexity. Lastly, the Bayesian neural network is introduced to quantify the uncertainty in RUL prediction. The effectiveness of the proposed model is verified using two commercial aircraft turbofan engine datasets. The results indicate that BAPT model for long-term RUL prediction demonstrates better performance than the existing state-of-the-art models.

Similarity indicator and CG-CGAN prediction model for remaining useful life of rolling bearings

To tackle the challenges of performing early fault warning and improving the prediction accuracy for the remaining useful life (RUL) of rolling bearings, this paper proposes a similarity health indicator and a predictive model of CG-conditional generative adversarial network (CGAN), which relies on a CGAN that combines one-dimensional convolutional neural network (CNN) with a bidirectional gate recurrent unit (Bi-GRU). This framework provides a comprehensive theoretical foundation for RUL prediction of rolling bearings. The similarity health indicator allows for early fault warning of rolling bearings without expert knowledge. Within the CGAN framework, the inclusion of constraints guides the generation of samples in a more targeted manner. Additionally, the proposed CG-CGAN model incorporates Bi-GRU to consider both forward and backward information, thus improving the precision of RUL forecasting. Firstly, the similarity indicator between the vibration signals of the rolling bearing over its full life span and the standard vibration signals (healthy status) is calculated. This indicator helps to determine the early deterioration points of the rolling bearings. Secondly, the feature matrix composed of traditional health indicators and similarity health indicator, is utilized to train and test the proposed CG-CGAN model for RUL prediction. Finally, to corroborate the efficacy of the proposed method, two sets of real experiment data of rolling bearing accelerated life from the Intelligent Maintenance Systems (IMS) are utilized. Experimental findings substantiate that the proposed similarity health indicator offers early fault alerts and precisely delineates the performance diminution of the rolling bearing. Furthermore, the put-forward CG-CGAN model achieves high-precision RUL prediction of rolling bearing.

A novel transformer-based DL model enhanced by position-sensitive attention and gated hierarchical LSTM for aero-engine RUL prediction

Accurate prediction of remaining useful life (RUL) for aircraft engines is essential for proactive maintenance and safety assurance. However, existing methods such as physics-based models, classical recurrent neural networks, and convolutional neural networks face limitations in capturing long-term dependencies and modeling complex degradation patterns. In this study, we propose a novel deep-learning model based on the Transformer architecture to address these limitations. Specifically, to address the issue of insensitivity to local context in the attention mechanism employed by the Transformer encoder, we introduce a position-sensitive self-attention (PSA) unit to enhance the model's ability to incorporate local context by attending to the positional relationships of the input data at each time step. Additionally, a gated hierarchical long short-term memory network (GHLSTM) is designed to perform regression prediction at different time scales on the latent features, thereby improving the accuracy of RUL estimation for mechanical equipment. Experiments on the C-MAPSS dataset demonstrate that the proposed model outperforms existing methods in RUL prediction, showcasing its effectiveness in modeling complex degradation patterns and long-term dependencies.

Contrastive BiLSTM-enabled Health Representation Learning for Remaining Useful Life Prediction

Remaining useful life (RUL) prediction is of vital significance in prognostics health management tasks. Due to powerful learning capabilities, deep learning methods, particularly long short-term memory (LSTM) have been widely applied in RUL prediction. However, many existing deep learning approaches overlook the inherent ordered relationship between samples in the direct mapping from sliced data to RUL pattern. To capture the faithful and ordered health representation of a given system, a Contrastive Bidirectional LSTM-enabled Health Representation Learning (CBHRL) framework is proposed. Firstly, the supervised contrastive regression loss (SupCR) is implemented to extract continuous health representation. The SupCR is designed to rank the similarity among health representations from different samples, prompting them highly correlated with linear RUL label. Among the process of contrastive learning, the series odd-even decomposition (SOED) method is devised to construct multi-view degradation data, which improves generalization ability. Finally, since the health representation is constructed on basis of similarity, a new similarity prediction method is proposed as the complement of regression prediction method. Experimental results show the health representations extracted by CBHRL achieve improved ratio ranging from a minimum of 17.19% to a maximum of 291.30% in monotonicity, smoothness and trendability.

Improved adaptive war strategy optimization algorithm assisted-adaptive multi-head graph attention mechanism network for remaining useful life of complex equipment

The remaining useful life (RUL) of complex equipment is an important criterion to ensure stable operation. In recent years, deep learning-based methods for predicting the RUL of complex equipment have attracted wide attention. However, it is only able to obtain the potential information in the Euclidean space, which hinders their ability to capture the deeply degradation information. Thus, graph neural networks have gradually entered the researchers’ field of vision. Despite the fact that graph neural networks are able to accomplish the task of RUL for complex equipment, there are still limitations that restrict the prediction performance in practical engineering. To address this challenge, an improved adaptive war strategy optimization algorithm assisted-adaptive multi-head graph attention mechanism network (IWSO-LMGAT) is proposed. For one thing, a learnable attention mechanism is proposed to adjust the weights of different heads dynamical and improve the limitation of GAT in obtaining deep degradation information. In addition, since hyperparameters are essential elements affecting the predicted result, inspired by the “no-free lunch” principle, an improved mathematical expression is described to avoid the issue such as precocity, fall into local optimums for WSO so that the optimal hyperparameters of the LMGAT could be obtained. The effectiveness and advancement of IWSO-LMGAT are validated on the CMAPSS dataset, and experimental results show that the proposed method could provide competitive forecasted results compared to traditional methods, that is, R2 = 0.9939, RMSE = 4.3638, and MAPE = 0.0137; this illustrates the IWSO-LMGAT’s potential for the RUL prediction of complex equipment.

DLformer: A Dynamic Length Transformer-Based Network for Efficient Feature Representation in Remaining Useful Life Prediction.

Representation learning-based remaining useful life (RUL) prediction plays a crucial role in improving the security and reducing the maintenance cost of complex systems. Despite the superior performance, the high computational cost of deep networks hinders deploying the models on low-compute platforms. A significant reason for the high cost is the computation of representing long sequences. In contrast to most RUL prediction methods that learn features of the same sequence length, we consider that each time series has its characteristics and the sequence length should be adjusted adaptively. Our motivation is that an "easy" sample with representative characteristics can be correctly predicted even when short feature representation is provided, while "hard" samples need complete feature representation. Therefore, we focus on sequence length and propose a dynamic length transformer (DLformer) that can adaptively learn sequence representation of different lengths. Then, a feature reuse mechanism is developed to utilize previously learned features to reduce redundant computation. Finally, in order to achieve dynamic feature representation, a particular confidence strategy is designed to calculate the confidence level for the prediction results. Regarding interpretability, the dynamic architecture can help human understand which part of the model is activated. Experiments on multiple datasets show that DLformer can increase up to 90% inference speed, with less than 5% degradation in model accuracy.

A Remaining Useful Life Indirect Prediction Method for Lithium-Ion Batteries Based on SA-DBN

To solve the issue that the battery capacity cannot be directly measured in practice, a more practical indirect remaining useful life (RUL) prediction method is proposed. First, the battery state characteristic parameters are analyzed and the time interval of equal discharge voltage drop (TIE-DVD) is selected as the indirect health factor. Second, the degradation relationship model is established by using back propagation (BP) neural network. Then, the deep belief network (DBN) model is used to establish the indirect health factor prediction model. Finally, to verify the adaptability of the proposed method to different types of batteries with different aging levels, both datasets from our own laboratory and the datasets from NASA Ames Research Center are used for experimental validations. The comparative experiments demonstrate that the proposed RUL prediction method is simple, accurate, and practical.

The seasonal-trend disentangle based prognostic framework for PEM fuel cells

Prognostics and Health Management (PHM) offer a potential solution to improve the durability of Proton Exchange Membrane Fuel Cells (PEMFCs), where degradation prognostic is the key component of the PHM system. However, limited by the dynamic operating environment, accurate and efficient prognostic algorithms still required for real applications. In this paper, the Seasonal-Trend Disentangle (STD) based prognostic framework is proposed to enhance the Remaining Useful Life (RUL) prediction of PEMFC system. Specifically, a lightweight parameter estimation neural network is introduced to estimate the aging parameters of the FC degradation model directly from PEMFC measurements. Besides, the integral of the FC degradation model at the specified current density range serves as the comprehensive Health Index (HI), which could combine different aging parameters and mitigate the effect of load dynamics. The STD-based prognostic model effectively captures the time-invariant features in the HI sequence from trend and seasonal phases, improving Remaining Useful Life (RUL) prediction performance. The proposed prognostic model is compared with RNN-family, ESN, and Dilated CNN methods using three distinct durability datasets from FCs operating under static, quasi-dynamic, and cyclically varying dynamic current loads. Experimental results demonstrate that the proposed algorithm outperforms the reference methods in terms of RMSE and MAPE metrics in the aging prognostic, and the proposed method could obtain satisfactory RUL prediction results 100–150 h earlier than the reference methods.

Nonlinear slow-varying dynamics-assisted temporal graph transformer network for remaining useful life prediction

Remaining useful life (RUL) plays an important role in the prognostics and health management of mechanical systems. Recently, deep learning-based methods have been widely applied in the field of RUL prediction. However, there still suffer from two limitations. One is that the existing RUL prediction methods cannot capture spatial dependencies and long-term temporal dependencies. The other is that nonlinear slow-varying dynamics related to the degradation behavior have not been explored in the RUL prediction. To break these limitations, a nonlinear slow-varying dynamics-assisted temporal graph Transformer network (NSD-TGTN) is proposed in this paper for RUL prediction. NSD-TGTN can simultaneously capture and model spatiotemporal graphs and nonlinear slow-varying dynamics to achieve RUL prediction. Herein, the TGTN is developed to mine both spatial and long-term temporal dependencies for constructing the spatiotemporal features. And, nonlinear slow-varying features are built and introduced into the TGTN to enhance the RUL prediction capacity. Two datasets are utilized to validate the effectiveness and superiority of the proposed method. Compared with existing advanced methods, the average prediction accuracies of the NSD-TGTN on the C-MAPSS dataset and the wear dataset are improved by 1.70 % and 8.22 %, respectively.

Prediction of the Remaining Useful Life of Lithium-Ion Batteries Based on the 1D CNN-BLSTM Neural Network

Lithium-ion batteries are currently widely employed in a variety of applications. Precise estimation of the remaining useful life (RUL) of lithium-ion batteries holds significant function in intelligent battery management systems (BMS). Therefore, in order to increase the fidelity and stabilization of predicting the RUL of lithium-ion batteries, in this paper, an innovative strategy for RUL prediction is proposed by integrating a one-dimensional convolutional neural network (1D CNN) and a bilayer long short-term memory (BLSTM) neural network. Feature extraction is carried out through the input capacity data of the model using 1D CNN, and these deep features are used as the input of the BLSTM. The memory function of the BLSTM is applied to retain key information in the database and to better understand the coupling relationship among consecutive time series data along the time axis, thereby effectively predicting the RUL trends of lithium-ion batteries. Two different types of lithium-ion battery datasets from NASA and CALCE were used to verify the effectiveness of the proposed method. The results show that the proposed method achieves higher prediction accuracy, demonstrates stronger generalization capabilities, and effectively reduces prediction errors compared to other methods.

A Novel Method for Remaining Useful Life Prediction of RF Circuits Based on the Gated Recurrent Unit-Convolutional Neural Network Model.

The remaining useful life (RUL) prediction of RF circuits is an important tool for circuit reliability. Data-driven-based approaches do not require knowledge of the failure mechanism and reduce the dependence on knowledge of complex circuits, and thus can effectively realize RUL prediction. This manuscript proposes a novel RUL prediction method based on a gated recurrent unit-convolutional neural network (GRU-CNN). Firstly, the data are normalized to improve the efficiency of the algorithm; secondly, the degradation of the circuit is evaluated using the hybrid health score based on the Euclidean and Manhattan distances; then, the life cycle of the RF circuits is segmented based on the hybrid health scores; and finally, an RUL prediction is carried out for the circuits at each stage using the GRU-CNN model. The results show that the RMSE of the GRU-CNN model in the normal operation stage is only 3/5 of that of the GRU and CNN models, while the prediction uncertainty is minimized.

The Remaining Life Prediction of Rails Based on Convolutional Bi-Directional Long and Short-Term Memory Neural Network with Residual Self-Attention Mechanism

In the railway industry, the rail is the basic load-bearing structure of railway tracks. The prediction of the remaining useful life (RUL) for rails is important to avoid unexpected system failures and reduce the cost of maintaining the system. However, the existing detection of rail flaws is difficult, the rail deterioration mechanisms are diverse, and the traditional data-driven methods have insufficient feature extraction. This causes low prediction accuracy. With objectives set in relation to the problems outlined above, a rail RUL prediction approach based on a convolutional bidirectional long- and short-term memory neural network with a residual self-attention (CNNBiLSTM-RSA) mechanism is designed. Firstly, the pre-processed vibration data are taken as the input for the convolutional bi-directional long- and short-term memory neural network (CNNBiLSTM) to extract the forward and backward dependencies and features of the rail data. Secondly, the RSA mechanism is introduced in order to obtain the contributions of the features at different moments during the degradation process of the rail. Finally, an end-to-end RUL prediction implementation based on the convolutional bi-directional long- and short-term memory neural network with the residual self-attention mechanism is established. The experiments were carried out using the full life-cycle data of rails collected at the railway site. The results show that the method achieves a higher accuracy in the RUL prediction of rails.

Enhanced stochastic recurrent hybrid model for RUL Predictions via Semi-supervised learning

Deep learning (DL) methods based on semi-supervised learning (SSL) have risen in popularity to achieve accurate remaining useful life (RUL) predictions when the volume of labeled sensor data is limited. The key to the method performance is the extraction of meaningful latent variables which can be served as health indicators (HIs). In this work, an enhanced stochastic recurrent hybrid model (ESRHM) is proposed through multi-task learning of RUL prediction, sensor data generation, and future sensor data prediction tasks. The extracted time-dependent HIs can contain both deterministic and stochastic information to characterize both the commonalities and individualities of different degradation behaviors via latent variables sharing. The proposed ESRHM is evaluated on the C-MAPSS and the lithium-ion batteries datasets to demonstrate its effectiveness in HIs construction and RUL prediction when coping with limited volume of labeled data.

Using transformer and a reweighting technique to develop a remaining useful life estimation method for turbofan engines

Estimating the Remaining Useful Life (RUL) of industrial machinery is an important task in Prognostics and Health Management (PHM). Accurate RUL prediction based on current health status and real-time sensor measurements can help establish efficient maintenance strategies in advance, thus improving the productivity and efficiency of machinery operations. However, considering the nature of industrial machinery, a data imbalance problem, which exists in current RUL estimation datasets like the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset, hinders data-driven approaches. To address the detrimental data imbalance problem and improve estimation performance, this research proposes an Adaptive RUL-wise Reweighting (ARR) technique. ARR uses kernel smoothing to maintain the inherent continuity of RUL and a reweighting method to rebalance the effects of samples with different ground-truth RULs. In addition, this research proposes a novel RUL estimation method that uses a transformer architecture, which has not yet been widely applied for PHM tasks. Using a transformer-based estimation model with high expressive power capable of handling multivariate time-series data has proven effective in RUL estimation. Comprehensive experiments using the C-MAPSS dataset based on cross validation are conducted to find optimal hyperparameter configurations and to validate the proposed method’s effectiveness in RUL estimation. For subsets FD001 and FD003 of the C-MAPSS dataset, the proposed method provides state-of-the-art estimation performance with the lowest Root Mean Squared Error (RMSE) values of 11.36 and 11.28 and score values of 192.22 and 133.41, respectively. The proposed method also shows highly competitive performance on subsets FD002 and FD004 of the C-MAPSS dataset.