Remaining Useful Life Prediction Approach Research Articles

Remaining useful life prediction method for cross- condition tools based on parallel fusion

Abstract The current tool remaining useful life (RUL) prediction models do not fully consider the importance of signals from different sensors and the differences in working conditions, which leads to a decline in the model’s robust performance under new conditions. This paper introduces a novel cross-condition RUL prediction approach that integrates parallel fusion, transfer learning, and deep learning. Considering the characteristics of different data sources, correlated signals are initially selected using correlation analysis and dynamic time warping (DTW). Subsequently, multi-scale Depthwise Separable Convolution and multi-attention mechanisms are combined to capture the importance of features at various scales. Finally, a dynamic domain adaptation technique is employed to adjust the importance weights of conditional distribution discrepancy and marginal distribution discrepancy, enabling the model to adapt to cross-condition scenarios.

A multi-channel fusion variational autoencoder-based RUL prediction approach for multi-sensor systems

Abstract Deep learning (DL)-based approaches have demonstrated remarkable performance in predicting the remaining useful life (RUL) of complex systems, which is beneficial for making timely maintenance decisions. However, the majority of these DL methods suffer from a lack of interpretability, and it is difficult to mine the degradation features in the presence of significant measurement noises. To remedy the deficiency, a multi-channel fusion variational autoencoder (MCFVAE)-based approach is proposed. A feature fusion module is designed to capture and fuse the multi-channel features, which facilitates the disclosure of the degradation information from the multi-sensor data. A variational inference module is further introduced to generate the compressive representations and project them into a latent space as an interpretable component, which can display the degradation degree of the multi-sensor systems. A regressor module is finally utilized to establish the relationship between the compressive representations and the RUL. The superior feature fusion and distribution characteristics learning abilities of the MCFVAE contribute to achieving robust and interpretable RUL prediction. The effectiveness and superiority of the proposed method are experimentally validated through a publicly available Commercial modular aero propulsion system simulation dataset and compared with the existing methods.

Model-data hybrid driven approach for remaining useful life prediction of cutting tool based on improved inverse Gaussian process

In the cutting process, reasonable evaluation of tool remaining useful life (RUL) is essential to ensure machining stability and safety. However, traditional data-driven methods rely on datasets with complete degradation data and prior knowledge, which are usually difficult to obtain in real scenarios. For these problems, this paper proposed a model-data hybrid driven approach for RUL prediction of cutting tool based on improved inverse Gaussian process. The tool wear degradation is considered as a random process, the cutting physical model and the data-driven stochastic process model are fused, so that the prior knowledge from the physical model and the statistical law of measured data are fully utilized to improve the accuracy of tool RUL prediction. The cutting physical model is established, and the prior knowledge of tool wear degradation is obtained based on physical simulation method. A data-driven random effects inverse Gaussian process model is constructed for tool RUL modeling and prediction. According to Bayesian method, the unknown parameters are estimated and updated integrating physical simulated and measured wear data, so as to obtain the probability density function for specific tool RUL, and quantify the prediction uncertainty. Finally, comparative experiments are conducted to validate the performance of the proposed method. The results suggest that the hybrid approach has the best performance in different tool RUL prediction.

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.

A bearing RUL prediction approach of vibration fault signal denoise modeling with Gate-CNN and Conv-transformer encoder

The operating conditions of rolling bearings are complex and variable, and their vibration monitoring signals are filled with strong noise interference, resulting in a low accuracy in remaining useful life (RUL) prediction. For this issue, this paper proposes a denoising method with vibration fault signals modeling, and a novel RUL prediction method with Gate-convolutional neural networks (CNN) and Conv-Transformer encoder. Firstly, the theoretical fault signal is obtained through the vibration fault signal model, and the quality of the extracted features is improved by the wavelet threshold denoising algorithm in the process of feature extraction and selection. Moreover, the CNN is combined with the gating mechanism to construct a feature extractor with the feature evaluation function, and the convolution layers are introduced into the transformer to expand the encoder’s ability to explore local information in temporal data. By using fixed-time step temporal features as the input to the prediction module and minimizing the Huber function as the optimization objective, the relationship between temporal features and RUL is obtained. The comparison with the existing state-of-the-art RUL methods illustrates that the combination of gate control and convolutional structure proposed in this paper can not only reduce the prediction error of the model but also improve its generalization ability and robustness.

Data-model linkage prediction of tool remaining useful life based on deep feature fusion and Wiener process

Accurately predicting the tool remaining useful life (RUL) is critical for maximizing tool utilization and saving machining costs. Various physical model-based or data-driven prediction methods have been developed and successfully applied in different machining operations. However, many uncertain factors affect tool RUL during the cutting process, making it challenging to create a precise physical model to characterize the degradation of tool performance. The success of the purely data-driven technique depends on the amount and quality of the training samples, it does not consider the physical law of tool wear, and the interpretability of the prediction results is poor. This paper presents a data-model linkage approach for tool RUL prediction based on deep feature fusion and Wiener process to address the above limitations. A convolutional stacked bidirectional long short-term memory network with time-space attention mechanism (CSBLSTM-TSAM) is developed in the data-driven module to fuse the multi-sensor signals collected during the cutting process and then obtain the mapping relationship between signal features and tool wear values. In the physical modeling module, a three-stage tool RUL prediction model based on the nonlinear Wiener process is established by considering the evolution law of different wear stages and multi-layer uncertainty, and the corresponding probability density function is derived. The real-time estimated tool wear of the data-driven module is used as the observed value of the physical model, and the model parameters are dynamically updated by the weight-optimized particle filter (WOPF) algorithm under a Bayesian framework, thereby realizing the data-model linkage tool RUL prediction. Milling experiments demonstrate that the proposed method not only improves RUL prediction accuracy, but also has good generalization ability and robustness for prediction tasks under different working conditions.

A Bidirectional Long Short-Term Memory Autoencoder Transformer for Remaining Useful Life Estimation

Estimating the remaining useful life (RUL) of aircraft engines holds a pivotal role in enhancing safety, optimizing operations, and promoting sustainability, thus being a crucial component of modern aviation management. Precise RUL predictions offer valuable insights into an engine’s condition, enabling informed decisions regarding maintenance and crew scheduling. In this context, we propose a novel RUL prediction approach in this paper, harnessing the power of bi-directional LSTM and Transformer architectures, known for their success in sequence modeling, such as natural languages. We adopt the encoder part of the full Transformer as the backbone of our framework, integrating it with a self-supervised denoising autoencoder that utilizes bidirectional LSTM for improved feature extraction. Within our framework, a sequence of multivariate time-series sensor measurements serves as the input, initially processed by the bidirectional LSTM autoencoder to extract essential features. Subsequently, these feature values are fed into our Transformer encoder backbone for RUL prediction. Notably, our approach simultaneously trains the autoencoder and Transformer encoder, different from the naive sequential training method. Through a series of numerical experiments carried out on the C-MAPSS datasets, we demonstrate that the efficacy of our proposed models either surpasses or stands on par with that of other existing methods.

A bearing fault detection and remaining useful life prediction method based on a multi-branch residual feature fusion mechanism and optimized weight allocation

Within the context of rapidly progressing industrial sectors, rolling bearings have become a fundamental component across an array of mechanical systems. Their fault detection and remaining useful life (RUL) estimations are vital for ensuring industrial production safety. Yet, the understated characteristics of early-stage, minor faults in bearing degradation often escape detection. Additionally, numerous existing networks overlook the critical information embedded in multi-scale features, consequently diminishing the accuracy of predictions and classifications. The present study proposes MM-InfoGAN (multi-branch residual feature fusion and multi-objective optimization information maximization generative adversarial network), an innovative approach for intelligent fault detection and RUL prediction to address these issues. MM-InfoGAN augments the network’s ability to extract bearing fault characteristics and RUL data, employing a multi-branch residual feature fusion network structure coupled with an attention mechanism. Moreover, it refines the weight allocation strategy for geometric loss and introduces a novel loss function. This function optimizes weight distribution during the GAN’s training phase, expediting the attainment of network equilibrium. The efficacy of the comprehensive MM-InfoGAN model and its integrated modules was substantiated through comparative and ablation experiments conducted on the XJTU-SY dataset and IMS Bearing dataset.

Ensemble Learning Based Convolutional Neural Networks for Remaining Useful Life Prediction of Aircraft Engines

Remaining useful life (RUL) prediction is an essential task of Prognostics and Health Management (PHM) of aircraft engines performed utilizing the huge data collected from multiple sensors attached to them to ensure their safe operation. While many studies have been reported on RUL prediction for aircraft engines, only a few of them focus on ensemble learning of CNN models for RUL prediction. The success of ensemble learning which is a combination of several base models developed using either same or different machine learning or deep learning algorithms, critically depends on the diversity among the base models generated. This paper proposes a new data-driven approach for RUL prediction of aircraft engines using ensemble learning based convolution neural networks (CNN) by investigating various steps to generate more diverse base models.
 The main objectives and contributions of this paper are as follows:
 
 Explore various CNN model architectures for RUL prediction - After data preprocessing and exploratory data analysis, two different CNN approaches, namely 2D CNN and 1D CNN with multiple channels, are investigated employing time window approach for time-series input preparation for better feature extraction by CNN. Each approach is experimented with multiple architectures to achieve the best possible outcome.
 Investigate engine specific RUL target function - For RUL prediction of turbofan engines using the C-MAPSS dataset, typically two RUL target functions, namely linear and piecewise linear, are used to determine RUL target values. In the piecewise linear RUL target function approach, which yields better performance in the reported studies, the RUL target value is assigned based on a piecewise linear degradation model which assumes a constant (and maximum) RUL target value in the early phase before linearly degrading the RUL targets. In the literature, this maximum RUL target value was chosen same for all the engines by taking a value of 125 or 130 without providing proper rationale. In this study, we adopt an approach based on the widely known health index to determine an engine specific initial (and maximum) RUL target value that can be used with the piecewise linear degradation model to determine RUL target values.
 Investigate hyperparameter optimization of CNN models to generate diverse base models - For the purpose of developing a high performance ensemble CNN model for RUL prediction, hyperparameter optimization of CNN models is performed to optimally determine the network structure (such as # of filters, filter size, stride, padding, # of convolutional, pooling, and dense layers, activation functions etc.) as well as the hyperparameters that determine the network training process (such as optimization method, learning rate, momentum, batch size etc.).
 Investigate ensemble learning to select and combine diverse CNN models for RUL prediction – In order to develop model combiners, diverse CNN models as base learners are selected using multiple performance measures such as RMSE, score function, MAE, and R2 score, and employing the non-negative least squares method, random forest regression, and extreme learning machine (ELM) to train model combiners.
 Evaluate the above proposed approach using the C-MAPSS dataset - To show the effectiveness of the proposed approach, various evaluations for RUL prediction using the popular C-MAPSS dataset (including all the four sub-datasets denoted as FD001, FD002, FD003 and FD004) are carried out and the results will be compared against the state-of-the-art results on the same dataset.
 
 Major emphasis of this proposed approach is on the generation of diverse CNN base models by carrying out various steps as explained above, and it is expected the results of this proposed approach will contribute towards enhancing the RUL prediction performance especially on the sub-datasets FD002 and FD004 which are challenging for the existing state-of-the-art RUL prediction techniques.

Physics-informed multi-state temporal frequency network for RUL prediction of rolling bearings

Accurate prediction of remaining useful life (RUL) has been a key issue in the field of Prognostic and Health Management (PHM), which aims at predictive maintenance to improve equipment reliability and safety. Aiming at the problem that the existing RUL prediction methods are weak in perceiving long-term features and poor in capturing periodic dependence, which leads to inaccurate and seriously lagging RUL prediction results of rolling bearings, this article proposes a physics-informed bearing RUL prediction approach, namely multi-state temporal frequency network (MSTFN). Firstly, a physics-informed dynamic adaptive inverse discrete Fourier transform (IDFT) frequency domain block is constructed, which maps the spectral information from the known domain to the spectral interval of the unknown domain for extracting the periodic features of the sequence. Secondly, a residual self-attention multi-state gated control unit (RSA-MSGCU) is proposed, which incorporates a novel multi-state hierarchical division mechanism in memory cells to enhance the medium- and long-term feature perception capability. Based on RSA-MSGCU, a trend prediction time domain block is built to extract the trend features of the sequence. Finally, the periodic and trend features are fused to achieve compatibility between the IDFT physical model and the data-driven model and then make the final RUL prediction.

Dual-Thread Gated Recurrent Unit for Gear Remaining Useful Life Prediction

Remaining useful life (RUL) prediction can provide a foundation for the operation and maintenance of industrial equipment. In order to improve the predictive ability for the complex degradation trajectory, a new dual-thread gated recurrent unit (DTGRU) is explored. It uses a dual-thread learning strategy to mine the stationary and non-stationary information from the input data and the difference of hidden states at two adjacent time steps. Then the state transition updating formulas of DTGRU are derived. Using the collected gear vibration signals and degradation-trend-constrained variational autoencoder, the gear health indicator (HI) is constructed. Based on the constructed HI and DTGRU, a novel RUL prediction method is developed. Via multiple gear life-cycle datasets, the effectiveness of the DTGRU-based RUL prediction approach is verified. Furthermore, compared with the existing typical prediction methods, the experimental results show that DTGRU has higher predictive ability in terms of HI fitting precision and RUL prediction performance.

Aero-engine remaining useful life prediction method with self-adaptive multimodal data fusion and cluster-ensemble transfer regression

Remaining useful life (RUL) prediction based on multimodal sensing data is indispensable for predictive maintenance of aero-engine under cross-working conditions. Although data-driven methods have emerged as a powerful tool in RUL prediction, it is still limited in industrial applications because the majority of existing methods manually select or fuse multisensory data and ignore the inconsistency of the sensing data collected from different engines. Therefore, an intelligent RUL prediction approach is proposed for aero-engine by integrating multimodal data fusion methodology and ensemble transfer learning technology to dynamically select sensing data and make a robust RUL prediction under cross-working conditions. Specifically, a self-adaptive dynamic clustering approach is developed to select useful multimodal data into different clusters, each of which has a consistent degradation tendency. Furthermore, a cluster-ensemble transfer regression network is constructed by building multiple regressors for different clusters to predict the RUL values of aero-engine under cross-working conditions, where a multi-level feature learning strategy is provided to learn the domain-invariant temporal degradation knowledge. Comparative experiments are conducted on the NCMAPSS dataset released in 2021. The results show that the proposed method outperforms other state-of-the-art RUL prediction methods.

Dual Channel Feature Attention-Based Approach for RUL Prediction Considering the Spatiotemporal Difference of Multisensor Data

The remaining useful life (RUL) prediction has always been the key technology to realize predictive maintenance. An accurate prediction can give decision-makers a reliable reference to develop maintenance schedules and adjust production planning. When dealing with the spatiotemporal data of multisensor system, recent deep learning (DL) methods, however, still remain unexplored to weigh the contributions from both spatial and temporal dimensions. In this article, we propose a novel DL-based approach with dual channel feature attention (DCFA) modules. First, the two-individual feature attention branches are used to automatically weigh the input on both time and spatial domain, which helps the model to focus more attention on the important elements. Then multilayer bidirectional long short-term memory (Bi-LSTM) and convolutional neural networks are used to extract the high-level features. Finally, a fusion network will combine the features to estimate the RUL. Evaluation experiments are conducted on the C-MAPSS dataset to verify the performance of the proposed model. The results show that the proposed model outperforms other state-of-the-art approaches.

Dynamic Model-Assisted Bearing Remaining Useful Life Prediction Using the Cross-Domain Transformer Network

Remaining useful life (RUL) prediction of rolling bearings is of paramount importance to various industrial applications. Recently, intelligent data-driven RUL prediction methods have achieved fruitful results. However, the existing methods heavily rely on the quality and quantity of the available data. For some critical bearings in industrial scenarios, the real run-to-failure data are insufficient, which impair the applicability of data-based methods for industrial practices. To address these issues, this article proposes a novel dynamic model-assisted RUL prediction approach for rolling bearing, in which sufficient simulation data are applied as the training data to solve the problem caused by insufficient real data. More specifically, a dynamic rolling bearing model is introduced for simulating the degradation process of physical structures. Then, a multilayer cross-domain transformer network is developed to implement RUL prediction and adapt the learned prediction knowledge from simulation to the actual measurements. Furthermore, a mutual information loss is utilized to preserve the generalized prediction knowledge of the measured data. The proposed approach can achieve a high RUL prediction accuracy with only limited measured data, which tackles the drawbacks of the existing data-driven methods. The experimental results of the rolling bearing degradation datasets demonstrate the effectiveness and superiority of the proposed RUL prediction approach.

A New Compressor Failure Prognostic Method Using Nonlinear Observers and a Bayesian Algorithm for Heavy-Duty Gas Turbines

Failure prognostic predicts the remaining useful life (RUL) of machine/components, which will allow timely maintenance and repair leading to continuous reliable and safe operating conditions. In this article, a novel hybrid RUL prediction approach is proposed for heavy-duty gas turbines. Two common failures, namely the fouling in the gas turbine compressor and filter defect, are investigated. First, a discrete wavelet transform (DWT) is applied to real-time measurements to reduce the effect of noise. A parallel structure consisting of a Laguerre filter and neuro-fuzzy is then constructed to identify nonlinear failure dynamics and generate residuals. These residuals are then utilized to estimate the failure severity. Following that, Bayesian theory is employed to predict the RUL. A novel feature of the approach is that the Laguerre filter is designed by using orthogonal basis functions (OBFs), which deliver precise estimates. Another benefit is that the proposed parallel configuration accurately identifies failure dynamics and boosts the RUL prediction performance. Experimental test studies on heavy-duty gas turbines indicate the high efficiency of the proposed RUL estimation in comparison to other failure prognostic strategies.

Deep transfer learning-based hierarchical adaptive remaining useful life prediction of bearings considering the correlation of multistage degradation

The multi-stage degradation process of bearings significantly affects the predicted performance of rotating machinery and equipment in long-term operation. However, the inherent correlations between degradation stages, and domain-invariant degradation features of multistage that affect the remaining useful life (RUL) are mostly ignored, leading to RUL prognostics deterioration under cross-working conditions. Therefore, a novel deep transfer learning-based hierarchical adaptive RUL prediction approach is applied to overcome this problem. Firstly, a novel multistage degradation (MD) division method is proposed with a combination of maximum mean discrepancy and statistical process analysis to accurately obtain the varied health indicators (HIs) with MD without setting any threshold. Then, the obtained MD features are fed into the residual network to automatically recognize the HIs and MD in response to multi-variability trend changes. Finally, a novel hierarchical adaptive RUL model based on a deep adaptive-transformer is proposed to transfer the learned domain-invariant degradation features of multistage from one to various working conditions. Meanwhile, hierarchical adaptive tuning (HAT) reduces the RUL model loss to guarantee a more accurate prediction of each degradation stage. To validate the proposed approach, extensive experiments were conducted in seven cross-domain cases on the XJTU-SY dataset to predict RUL. Comparison results indicate that the proposed approach is more accurate than other conventional methods because it considers the RUL of bearing at each stage.

Hybrid Multi-Scale Convolutional Long Short-Term Memory Network for Remaining Useful Life Prediction and Offset Analysis

Abstract Prognostic and health management (PHM) has become increasingly popular due to the requirement of improved maintenance techniques in the industry. Remaining useful life (RUL) estimation is an important parameter through which PHM can be utilized to implement timely and cost-effective maintenance. Due to recent advancements in sensor-based and other Industry 4.0 related technologies, data-driven methods for RUL estimation have become more prevalent and effective. In this paper, a novel data-driven method for sensor-based RUL estimation using a combination of multi-scale convolutional neural network (MS-CNN) and long short-term memory (LSTM) is proposed. The proposed hybrid multi-scale convolutional LSTM (HMCL) model is capable of extracting both spatial features of various scales and temporal features from the input data to provide accurate RUL predictions. L2 regularization and dropout techniques are used to reduce overfitting. The performance of the proposed model is evaluated using the C-MAPSS dataset. It achieves excellent performance as compared to other state-of-the-art methods making it a promising approach for sensor-based RUL prediction. Additionally, to discern the cause for occurrence of offsets, i.e., deviations in the model’s predictions with the true RUL value, an offset analysis is carried out. Through the analysis, an estimate on the location and cause of offsets is established and based on the sensory input data, offsets are identified using an SVM classification model. Despite being a simple classification model, it is able to achieve a decent performance in classifying the offsets.

Remaining Useful Life Prediction of a Semiconductor Manufacturing Equipment Unit*

This paper deals with the surveillance of the health state and the prediction of the Remaining Useful Life (RUL) of an operating equipment unit of the semiconductor manufacturing industry. It aims at improving an existing work performed in this domain. For that, a new framework for RUL prediction is proposed based on modeling the behavior of the Health Indicator (HI). The contribution of this framework is the effective combination of the proposed HI extraction and the RUL prediction approaches. The HI extraction approach is mainly based on the Least Absolute Shrinkage and Selection Operator (LASSO) regression model. The RUL prediction approach relies on the adaptive Wiener process in which the similarity principle is introduced. An application of the proposed framework on real industrial data shows an improvement of the RUL prediction accuracy compared to the existing work.

Capability-based remaining useful life prediction of machining tools considering non-geometry and tolerancing features with a hybrid model

ABSTRACT Machining tools are vital components of intelligent manufacturing systems whose state and remaining useful life (RUL) determine product quality. Specifically, the wearing of tool reduces its capability of production yield and diminishes product quality. Therefore, a capability-based RUL prediction approach is proposed in this paper to thoroughly evaluate the state and RUL of machining tools. First, the connotation of tool capability is discussed, and a framework for quality assurance capability-based RUL prediction is proposed. Product quality, which can be used to assess the capability of tool, is modelled and expanded to consider non-geometric dimensioning and tolerancing (non-GD&T) features based on the classic geometric dimensioning and tolerancing (GD&T) system. Second, a physics-based model of process is developed to estimate the non-GD&T features and calculate tool wear. Third, a hybrid data-driven and physics-based model is developed to quantitatively assess the capability of tool based on the comprehensive quality estimation. Finally, a case study of rolling machining tool is carried out to verify the effectiveness and proactiveness of the proposed framework, and the final result highlights its rationality and accuracy in estimating the RUL of machining tools with better interpretation.

Remaining useful life prediction for rotating machinery based on dynamic graph and spatial–temporal network

Deep learning-based rotating machinery remaining useful life (RUL) prediction approaches rarely consider spatial dependencies and global temporal correlation of monitoring signals simultaneously. Superiorly, graph convolutional networks (GCNs) learn relationship information among graph nodes, considering the spatial dependencies of signals. It is beneficial for constructing high-quality graphs to improve the prediction performance in single-sensor monitoring scenarios. In this paper, an RUL prediction approach for rotating machinery based on a dynamic graph and a spatial–temporal network (STNet) is proposed. Short-time Fourier transform is introduced to obtain node features, and dynamic edge connections are established through node importance weights. Furthermore, an STNet is constructed to learn graph features, in which the GCN is used to mine spatial dependencies of input graphs, and a bi-directional long short-term memory network is applied to capture global temporal correlations. Finally, an autoencoder-based graph readout layer is designed to pass learned graph features. Case studies are conducted to demonstrate its effectiveness.