A small-sample RUL prediction method based on physics-informed neural network with integrated domain-generalized features

Background Accurate prediction of the remaining useful life (RUL) of assets is fundamental to the development of effective maintenance strategies and overall asset management. Despite significant advancements, there remains a notable gap in integrating fault detection and diagnostics (FDD) with RUL prediction models to create more comprehensive and accurate maintenance systems. One of the key challenges in this field is the limited ability of current models to generalize effectively across different types of equipment and varying operating conditions. This gap emphasizes the need for further research and innovation in developing robust and adaptable RUL prediction methodologies that can be applied broadly across diverse industrial scenarios. Methodology This review systematically evaluates the machine learning (ML) and deep learning (DL) techniques used for anomaly detection and RUL prediction, focusing on their efficacy and practical application. By adhering to the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) criteria, the review identifies and addresses the deficiencies in existing models. It explores a range of machine learning and deep learning methods, including probabilistic approaches, hybrid models that combine multiple machine learning techniques, and neural networks designed to handle large-scale time-series data. The review also examines the potential for synergy between machine learning models and FDD, aiming to enhance the precision of equipment monitoring and the early detection of defects. The challenges of data variability, the irregularity in equipment deterioration, and the interpretability of complex models are highlighted. Results The analysis reveals that while current machine learning and deep learning models have made considerable strides in predicting the RUL of assets, significant challenges remain, particularly in their ability to generalize across various equipment types and operational contexts. Hybrid models and neural networks have shown promise in improving the accuracy of RUL predictions, especially when managing large, complex datasets. However, the irregular nature of equipment wears and tear, coupled with data variability, continues to pose significant challenges. The review highlights the need for more robust and adaptable models that can not only predict RUL more accurately but also integrate seamlessly with FDD systems to provide a more holistic approach to maintenance. Conclusion This comprehensive review focusses on the need for continued research in developing more integrated, generalizable, and efficient predictive maintenance systems. By exploring the application of AI in virtual assistants, the review suggests promising avenues for extending asset longevity and optimizing maintenance schedules. While current models offer valuable insights, they must evolve to address the identified gaps in generalizability and model interpretability.

The online estimation of battery SOH (state of health) and prediction of RUL (remaining useful life) are a prerequisite of ensuring its safe and reliable operation, optimal balance and battery SOC estimation. Considering the actual situation, a relatively stable charging environment is more suitable for accurate estimation and prediction of SOH and RUL compared to uncertain discharge conditions. A health indicator based on charging characteristics is selected to predict battery degradation trends through Pearson and Spearman correlation analysis. The grid search optimisation (GS) algorithm and support vector regression (SVR) are integrated to extract the intrinsic relationship between the health indicator and capacity, combining GS algorithm to optimise SVR kernel parameters. With the Lithium-ion data set provided by NASA, it’s proved that the GS-SVR fusion model is more effective in estimating battery life decay than the SVR model, with a reduction of 52.64% in mean absolute error and 68.51% mean square error. Besides, the R 2 of the GS-SVR model is 97.3%, and the robustness of the model is stronger. The RUL prediction results demonstrate the effectiveness and advantage. Highlights The methods of obtaining indirect health indicators were discussed in detail. Data-driven algorithm based on small samples to realise online model estimation. The estimation results based on GS-SVR model and SVR model were compared. Application to data taken from the Lithium-ion battery cycle aging test of the NASA’s Prognostics Center of Excellence.

Remaining useful life with self-attention assisted physics-informed neural network

Remaining useful life (RUL) prediction of bearings has extraordinary significance for prognostics and health management (PHM) of rotating machinery. RUL prediction approaches based on deep learning have been dedicated to finding a nonlinear mapping relationship between non-stationary monitoring data and RUL. However, most existing approaches pay little attention to the degradation trend of diverse health stages of bearing and lack the discriminative power of crucial degradation features, resulting in the loss of some important information associated with RUL. To address this challenge, this article proposes a novel RUL prediction framework based on degradation-trend-aware deep neural network with attention mechanism (DTADAN). Firstly, the multi-direction features with evident degradation trend are extracted via the analysis of bearing vibration signal from both time domain and time-frequency domain. Next, the deep neural network architecture with attention mechanism is utilized to adaptively learn the critical degradation features beneficial for RUL prediction. Distinct from the existing approaches, the proposed framework is able to dynamically extract key degradation features of the bearing including degradation trend information and effectively fuse multi-direction information to improve RUL prediction accuracy. The performance of the proposed approach is evaluated via case studies on XJTU-SY bearing dataset and PRONOSTIA bearing dataset. Compared with other state-of-the-art approaches, the proposed framework has better predictive accuracy and robustness. Additionally, interpretable analysis is provided to reveal the process of model learning and data characteristics, and the analysis results are helpful in guiding model learning.

Prognostics, in other words, remaining useful life (RUL) estimation is a core task of prognostics and health management (PHM). Reliable RUL predictions can reduce maintenance costs, improve production efficiency, and avoid unexpected downtime. Lots of models for RUL predictions have been proposed; however, noise and the nonlinear nature of degradation phenomena often leads to poor prognostics results, and the acquired engineered system data are usually subject to a high level of uncertainty. This makes the RUL estimation models less than satisfactory. Accurate RUL estimation and prediction not only rely on an accurate model but also depend on the adjustments of model parameters to track the variation. In this paper, an ensemble model combining the health index synthesis (HIS) approach and improved particle filtering (PF) is introduced. HIS approach was used to obtain the synthesized health index (SHI) for an engineered system with multiple sensors, which indicated the system's degradation model, while the improved PF approach was used to adjust the parameters of the degradation model obtained from the HIS approach and optimized the RUL estimation results. The performance of the prognostics approach introduced in this paper was demonstrated by using turbofan engine degradation data sets, which was supplied by NASA Ames, and results were compared with several usually used methods.

Predicting accurate remaining useful life (RUL) is quite necessary in many industrial cases. Recently, data-driven method has been widely used for RUL prediction because of its promising performance and ease of use. However, one practical challenging issue of the data-driven method is lack of labeled data. One way for dealing with this issue is domain adaptation. With domain adaptation technique, a RUL prediction model is built using labeled data of source domain and then the trained model is used to predict RUL of target domain where labeled data is unavailable. In this paper, we propose our method for RUL prediction through domain adaptation. Another practical issue of RUL prediction is that a single RUL prediction value may not be enough for successful maintenance of target systems. In other words, we need to know how uncertain RUL prediction is. In this paper, we study Monte Carlo (MC) dropout-based uncertainty metric for realizing better RUL prediction result. Through experiment, we first show that our domain adaptation technique shows reasonable performance. We also show that MC dropout-based approach generates the uncertainty metric only meaningful for source domain.

Despite the substantive literature on remaining useful life (RUL) prediction, less attention is paid to the influence of epistemic uncertainty and aleatory uncertainty in multiple failure behaviors in the accuracy of RUL. The research question in this study was: can uncertainties be quantified in predicting the RUL of systems with multiple failure modes? The first objective was to quantify the uncertainties in the prediction of RUL, considering known multiple failure modes. This objective used vibration data from accelerated degradation experiments of rolling element bearings. The second objective was to calculate the uncertainties in the prediction of RUL, considering the multiple failure modes as unknown. The experimental data used in this objective were from run-to-failure tests of Li-ion batteries. An analysis was performed on how the uncertainties affect the RUL prediction in systems with known multiple failure modes and systems where the multiple failure modes were unknown. A Bayesian neural network (BNN) was used to quantify epistemic and aleatory uncertainty while predicting RUL. The results of the qualitative uncertainties on RUL in systems with multiple failure modes were presented and discussed. Also, the study yielded an RUL uncertainty quantification model for multiple failure modes. The proposed framework's performance in the RUL prediction was demonstrated. Finally, the epistemic and aleatory uncertainties were quantified in the system's RUL. It was shown that systems that fail due to the same failure mode tend to have similar uncertainty values over time. The results in this paper may lead to the design of more reliable systems that exhibit multiple failure modes.

Remaining useful life (RUL) prediction is crucial for the predictive maintenance and health management of mechanical transmission systems. Although data-driven RUL prediction methods have advanced significantly, three key challenges remain: (i) constructing a physically interpretable HI that is invariant to operating conditions yet sensitive to incipient degradation; (ii) achieving adaptive time to start prediction (TSP) detection that accommodates non-stationarity and controls false and missed alarms; (iii) accurate and generalizable RUL prediction under cross-domain scenarios. To address these issues, a method combining unsupervised anomaly detection and deep feature transfer for TSP determination and RUL prediction is proposed. A residual health indicator is constructed from an expected trajectory learned by Gaussian process regression (GPR) on the healthy segment and a Mahalanobis distance (MD) deviation measure. For the first time extreme value theory is used to create dynamic thresholds for performance degradation analysis and TSP determination. During the RUL prediction phase, transfer component analysis mitigates feature distribution discrepancies between source and target domains. A dual long short-term memory (dual-LSTM) network with L2 regularization is then employed for RUL prediction across two transfer tasks. Experimental validation on a public benchmark dataset and a laboratory run-to-failure bearing dataset demonstrates the superior predictive accuracy and generalization capability of the proposed approach compared to existing methods.

The prediction of the remaining useful life (RUL) of bearings is of great significance for reducing cost and increasing efficiency of mechanical equipment and ensuring healthy operation. Most of the traditional RUL prediction methods based on deep learning only analyze single sensor data, which makes the prediction accuracy fluctuate greatly and the reliability is low, and the contributions of data collected by different sensors to RUL prediction are inconsistent, which makes the low utilization of data lead to poor prediction results. To solve the above problems, a new RUL prediction framework with convolutional attention mechanism and temporal convolutional network (CAMTCN) is proposed. Firstly, the vibration signals collected by multi-sensor are normalized and processed directly as the input of the network. Secondly, an Efficient Adaptive Shrinkage (EAS) model is designed to adaptively eliminate noise interference and improve the accuracy of RUL prediction. Finally, we use the attention mechanism to design the convolutional attention subnet and combine it with the TCN to obtain the contribution degree of bearing degradation features collected by different sensors. Adaptive weight allocation captures more important degradation features to achieve end-to-end RUL prediction. Experiments were conducted on IEEE PHM 2012 dataset and XJTU-SY dataset respectively, and compared with some advanced in-depth learning methods. The experimental results show that this method can predict RUL more accurately on different bearing data sets, and has good generalization.

The remaining useful life (RUL) prediction is critical for the safe and reliable operation of lithium-ion battery (LIB) systems, which characterizes the aging status of the battery and provides early warning for battery replacement. Most existing RUL prediction methods rely on empirical aging models, and the role of the battery mechanism is not considered in the subsequent algorithm settings. The accuracy and stability of data-driven algorithms are severely limited by battery aging data. A new electrochemical-model-based particle filter (PF) framework for LIB RUL prediction is proposed in this paper. Parameters of a simplified electrochemical model (SEM) are used as state variables of the PF algorithm and these parameters can be identified by applying specially designed current excitations to the battery. The SEM-based capacity simulation process is taken as the observation equation in the PF algorithm framework. Therefore, the mechanism of the battery is fully considered when making the RUL prediction. The proposed method is validated through cyclic aging experiment of a cylindrical LFP/graphite LIB of 45Ah. The accuracy of the method is compared with a data-driven-based PF framework for RUL prediction and shows better accuracy and stability, which provides a choice for achieving high-quality RUL prediction.

Due to the strict requirements of satellite systems, accurate remaining useful life (RUL) prediction of the key components is very important to the reliability and security of satellite systems. Otherwise, a failure could lead to catastrophic consequences and enormous economic losses. Because of the complex structure of the satellite and its complex space environment, the factors that affect the satellite systems status are numerous. Moreover, as a result of the healthy historical data of key components in satellite are too few, which makes the traditional methods based on analysis model are not suitable for RUL prediction of key components in satellite. In this paper, in order to solve the RUL prediction problem of Lithium-ion batteries (LIBs) in satellite with incomplete healthy historical data, we propose an efficient RUL prediction method for key components of satellite, which is called Residual Remaining Useful Life Prediction Method (RRULPM), based on the study of Multivariate State Estimation Technique (MSET). The RRULPM is make up of degradation model based on MSET state estimation and criteria of failure based on historical degradation value, which is developed by improving MSET and combining the residuals with life cycle damage (LCD) prediction creatively when lacking healthy historical data. Experimental results demonstrate that the RRULPM is excellent to achieve the RUL prediction problems of LIBs through the actual in orbit telemetry data. Unlike previous RUL prediction methods, RRULPM provides good feasibility and effectiveness. This research can serve as guidance for prognostics and health management (PHM) of key components in satellite.

Fusing Coupled Degradation Mechanisms with Machine Learning: A Multi-fidelity Framework for Lithium-ion Battery Lifespan Prediction

Remaining useful life prediction for multi-sensor systems using a novel end-to-end deep-learning method

Remaining useful life (RUL) prediction is a core technology for equipment operation and health management, and the degradation process of most equipment generally has a long-term correlation. To address this issue, we propose a method to quantify the uncertainty of RUL prediction based on a temporal convolutional network (TCN)-transformer- Wiener Process (WP) degradation model. We give a new generalized RUL prediction framework that employs TCN-Transformer to fit device degradation trends, which can adaptively adjust the fitting of degradation trends. WP is introduced, and the concept of first hitting time (FHT) gives an approximate expression for the probability density function (PDF) of the remaining lifetime of the model, further quantifying the uncertainty of the RUL prediction results. Finally, verification and analysis on the bearing dataset and the tool holder power head degradation dataset, and by comparing with other existing popular methods demonstrated the accuracy and stability of the proposed RUL prediction method. This innovative method provides a robust solution for RUL prediction, offering significant contributions to improving maintenance strategies and operational efficiency.

An effective health assessment guarantees the high accuracy of remaining useful life (RUL) prediction of machinery components. The key to health assessment is the health indicator of machinery components, which are generally constructed by feature fusion from the time and frequency domains in sensorial data. However, existing health indicator construction methods are constrained due to the low-sampling rate and the susceptibility to environmental disturbances of sensorial data. To draw these issues, this paper mainly proposes a health indicator construction method combining condensed image coding and a metrics-constrained deep learning model for improving the accuracy of RUL prediction. Firstly, to extract more information in time series under a low-sampling rate, a recurrence plot-grey (RP-G) image coding is extended from RP images to extract and fuse both the global and local dynamics at multiple scales in time series. Then, a nested residual-convolution autoencoder (NR-CAE) model is proposed to extract degradation-related information from RP-G images containing information disturbed by the environment. Finally, to build a more solid connection between the image-based features and the RUL prediction, a metric-constrained gated recurrent unit (MC-GRU) is proposed by considering characteristics related to RUL prediction and temporal relationships in RP-G image-based features. A commercial case of wind turbine gearboxes in Liaoning, China is investigated and demonstrates that the proposed method owns the ability to construct reliable health indicator of machinery components for accurate RUL prediction, using low-sampling sensorial data under environmental disturbances.