Remaining Useful Life Prediction Model Research Articles

Real-Time Prediction of Remaining Useful Life and Preventive Maintenance Strategy Based on Digital Twin

AbstractThe functional parts of a machine tool determine its reliability level to a great extent. The failure prediction of the functional part is helpful to prepare the maintenance scheme in time, in order to ensure a stable manufacturing process and the required production quality. Due to the rise of digital twin (DT), which has the characteristics of virtual reality interaction and real-time mapping, a DT-based real-time prediction method of the remaining useful life (RUL) and preventive maintenance scheme is proposed in this study. In this method, a DT model of the manufacturing workshop is established based on real-time perceptual information obtained by the proposed acquisition method. Subsequently, the real-time RUL of the functional part is predicted by establishing an RUL prediction model based on the nonlinear-drifted Brownian motion, which takes the working conditions and measurement errors into consideration. On this basis, the optimal preventive maintenance scheme can be determined and fed back to the manufacturing workshop, in order to guide the maintenance of relevant parts. Finally, an example case study is presented to illustrate the feasibility and effectiveness of the proposed method.

Remaining Useful Life Prediction of High-Frequency Swing Self-Lubricating Liner

The remaining useful life (RUL) prediction of self-lubricating spherical plain bearings is essential for replacement decision-making and the reliability of high-end equipment. The high-frequency swing self-lubricating liner (HSLL) is the key component of self-lubricating spherical plain bearings under high-frequency oscillation conditions. In this study, a RUL prediction method was proposed based on the Wiener process and grey system theory. First, the predictive processing of the wear depth was carried out using the grey model GM(1,1) to reduce the randomness and enhance the inherent regularity of the life test data. A degradation process model was established and the RUL was predicted online with the model parameter estimates based on the Bayesian updating strategy. Finally, examples were provided to elaborate the RUL prediction of the HSLL. The results show that the prediction accuracy of the proposed RUL prediction model is higher than that of the simple Wiener process during the entire residual life cycle of the HSLL. Based on the original wear data, the prediction accuracy of the RUL exhibited a strong dependence on prior samples and was relatively low owing to the larger deviation of the wear rate between the test sample and prior samples.

Early and robust remaining useful life prediction of supercapacitors using BOHB optimized Deep Belief Network

The lifespan, power density, and transient response make supercapacitors a component of choice for the electric vehicle and renewable energy industry. Supercapacitors’ long lifecycle often makes it difficult for designers to assess the system’s reliability over the complete product cycle. In the existing literature, the remaining useful life (RUL) estimations utilize up to 50% state of health (SOH) degradation data to successfully predict the RUL of the supercapacitors with reasonable accuracy, making them impractical in terms of time and resources required to collect the data. The time to acquire data imposes restrictions on developing a data-driven RUL prediction model for the supercapacitors. The objective of this study is to reliably predict the SOH degradation curve of the supercapacitors with the availability of less than 10% degradation data to avoid time and cost-consuming lifecycle testing. This study presents a novel combination of deep learning algorithm-Deep Belief Network (DBN) with Bayesian Optimization and HyperBand (BOHB) to predict the RUL of the supercapacitors in the early phases of degradation. The proposed method successfully predicts the degradation curve using the data of the initial 15 thousand cycles (less than 6% data for training in most of the cases), which is very promising since the supercapacitor has yet to show much degradation at this stage, thus reducing up to 54% time for the development of the RUL prediction model. The proposed model shows good accuracy with percent error and root mean squared error (RMSE) ranging from 0.05% to 2.2% and 0.8851 to 1.6326, respectively. The robustness of the model is also tested by injecting noise in the training data during training.

A Genetic Algorithm Optimized RNN-LSTM Model for Remaining Useful Life Prediction of Turbofan Engine

Understanding the remaining useful life (RUL) of equipment is crucial for optimal predictive maintenance (PdM). This addresses the issues of equipment downtime and unnecessary maintenance checks in run-to-failure maintenance and preventive maintenance. Both feature extraction and prediction algorithm have played crucial roles on the performance of RUL prediction models. A benchmark dataset, namely Turbofan Engine Degradation Simulation Dataset, was selected for performance analysis and evaluation. The proposal of the combination of complete ensemble empirical mode decomposition and wavelet packet transform for feature extraction could reduce the average root-mean-square error (RMSE) by 5.14–27.15% compared with six approaches. When it comes to the prediction algorithm, the results of the RUL prediction model could be that the equipment needs to be repaired or replaced within a shorter or a longer period of time. Incorporating this characteristic could enhance the performance of the RUL prediction model. In this paper, we have proposed the RUL prediction algorithm in combination with recurrent neural network (RNN) and long short-term memory (LSTM). The former takes the advantages of short-term prediction whereas the latter manages better in long-term prediction. The weights to combine RNN and LSTM were designed by non-dominated sorting genetic algorithm II (NSGA-II). It achieved average RMSE of 17.2. It improved the RMSE by 6.07–14.72% compared with baseline models, stand-alone RNN, and stand-alone LSTM. Compared with existing works, the RMSE improvement by proposed work is 12.95–39.32%.

A Remaining Useful Life Prognosis of Turbofan Engine Using Temporal and Spatial Feature Fusion.

The prognosis of the remaining useful life (RUL) of turbofan engine provides an important basis for predictive maintenance and remanufacturing, and plays a major role in reducing failure rate and maintenance costs. The main problem of traditional methods based on the single neural network of shallow machine learning is the RUL prognosis based on single feature extraction, and the prediction accuracy is generally not high, a method for predicting RUL based on the combination of one-dimensional convolutional neural networks with full convolutional layer (1-FCLCNN) and long short-term memory (LSTM) is proposed. In this method, LSTM and 1- FCLCNN are adopted to extract temporal and spatial features of FD001 andFD003 datasets generated by turbofan engine respectively. The fusion of these two kinds of features is for the input of the next convolutional neural networks (CNN) to obtain the target RUL. Compared with the currently popular RUL prediction models, the results show that the model proposed has higher prediction accuracy than other models in RUL prediction. The final evaluation index also shows the effectiveness and superiority of the model.

Feature Extraction for Data-Driven Remaining Useful Life Prediction of Rolling Bearings

A variety of data-driven methods have been proposed to predict remaining useful life (RUL) of key component for rolling bearings. The accuracy of data-driven RUL prediction model largely depends on the extraction method of performance degradation features. The individual heterogeneity and working condition difference of rolling bearings lead to the different performance degradation curves of rolling bearings, which result in the mismatch between the established RUL prediction model by the training rolling bearings and the test rolling bearings. If a feature is found, which can reflect the consistency of the performance degradation curve of each rolling bearings, and give the indicator to determine the node and predictable interval of the declining period, the accuracy of the RUL prediction model will be improved. To solve this problem, a new feature extraction method based on the data-driven method, namely, Fitting Curve Derivative Method of Maximum Power Spectrum Density (FDMPD), is proposed to extract the performance degradation features of the same or similar rolling bearings from the historical state monitoring data in this article. The FDMPD can make the performance degradation feature curves of life cycle, which takes on consistency trend for different rolling bearings, and the starting point of the rolling bearings to enter the degenerating period is defined and the working stage of rolling bearings is divided. Based on this, the kernel extreme learning machine (KELM) and weight application to failure times (WAFT) are combined with FDMPD to establish a new RUL prediction model of rolling bearings, which can effectively realize the RUL prediction of rolling bearings. The whole life cycle data of rolling bearings are used to verify the validity of the RUL prediction model. The experimental results show that the established RUL prediction model can accurately predict the RUL of rolling bearings. It has the advantages of rapidity, stability, and applicability.

Prediction of Bearings Remaining Useful Life Across Working Conditions Based on Transfer Learning and Time Series Clustering

Recently, data-driven remaining useful life (RUL) prediction has become a promising tool in prognostics and health management for rolling bearings. In many actual applications, however, it is not easy to collect whole-life degradation data of bearings, while training with an insufficient amount of data would result in a biased RUL prediction model. If the monitoring data historically accumulated under different working conditions are introduced to facilitate model training, it probably tends to get inadequate prediction performance due to the violation of the precondition of independent and identical distribution ( ${i.i.d.}$ ). To solve this problem, a new bearing RUL prediction approach is proposed by utilizing the transfer learning strategy. First, a new time series clustering algorithm is proposed to exploit clusters of different degradation series. By integrating phase space warping and dynamic time warping, this algorithm is able to measure the similarity by using global degradation information and then get better clustering performance. Second, a new temporal domain adaptation method is proposed to obtain the pivot feature set of different bearings based on meta-degradation information which is represented by the principal curve of each cluster. Finally, support vector machine is run to establish the prediction model by means of these features, and RUL of the target bearing can be predicted via the same feature adaptation. A theoretical analysis is also provided to prove that the proposed approach has an upper bound of information loss in the transfer process. Experimental results on the IEEE PHM challenge 2012 dataset demonstrate the effectiveness of the proposed approach.

A Novel Method for Remaining Useful Life Prediction of Roller Bearings Involving the Discrepancy and Similarity of Degradation Trajectories.

Accurate remaining useful life (RUL) prediction of bearings is the key to effective decision-making for predictive maintenance (PdM) of rotating machinery. However, the individual heterogeneity and different working conditions of bearings make the degradation trajectories of bearings different, resulting in the mismatch between the RUL prediction model established by the full-life training bearing and the testing bearings. To address this challenge, this paper proposes a novel RUL prediction method for roller bearings that considers the difference and similarity of degradation trajectories. In this method, a feature extraction method based on continuous wavelet transform (CWT) and convolutional autoencoder (CAE) is proposed to extract the deep features associated with bearing performance degradation before the degradation indicator (DI) is obtained by applying the self-organizing maps (SOM) method. Next, a dynamic time warping (DTW) based method is applied to perform the similarity matching of degradation trajectories of the training and testing bearings. Driven by the historical DIs of the given bearing, the grey forecasting model with full-order time power terms (FOTP-GM) is applied to model the degradation trajectory using a parameter optimization method. Then, the failure threshold of the given testing bearing can be determined using a data-driven method without manual intervention. Finally, the RUL of the given testing bearing can be estimated using the preset failure threshold and the optimized degradation trajectory model of the given testing bearing. The experimental results show that the proposed method retains the individual differences of bearing degradation trend, realizes the independent and reasonable bearing failure threshold setting, and improves the prediction accuracy of RUL.

Battery remaining useful life prediction using improved mutated particle filter

AbstractAccurate prediction of lithium‐ion battery remaining useful life (RUL) is of great significance for battery health management. Particle filter (PF) is used to predict the RUL effectively, but it suffers from particle degeneracy and impoverishment during the estimation. To solve this problem, this paper proposes a new RUL prediction model based on improved mutated particle filter (IMPF) approach. In the IMPF algorithm, the mutant particles are generated from the prior particles, which not only represent the high probability region but also accelerate the convergence speed of particles. Then an improved polynomial resampling algorithm is proposed to avoid the loss of particle diversity during the resampling process. The simulation results on benchmark function demonstrate the effectiveness of the proposed IMPF algorithm. The method is also applied successfully to a battery capacity degradation data set provided by NASA Ames Research Center. Research results show that the proposed RUL prediction approach has better performance than other related PF techniques.

A generalized cauchy method for remaining useful life prediction of wind turbine gearboxes

The accurate estimate of the Remaining Useful Life (RUL) of mechanical tools is a fundamental problem in Engineering. This prediction often implies the knowledge and application of sophisticated mathematical methods based on fractal and Long-Range Dependence (LRD) stochastic processes. However, the existing RUL prediction methods based on stochastic model cannot simultaneously consider the fractal and LRD characteristics of the equipment degradation process. This paper describes a new RUL prediction model based on the Generalized Cauchy (GC) process, which is a stochastic process with independent parameters. That is, the GC process uses the fractal dimension D and Hurst index H to describe the fractal and LRD characteristics of the degradation sequence, respectively. Then, the GC process is taken as the diffusion term, describing the uncertainty of the degradation sequence, to establish the GC degradation model, and the power law and exponential forms are used to describe the nonlinear drift of the degradation sequence. The stochastic volatility of the degradation sequence causes the equipment RUL unable to be predicted for a long time. This article uses the largest Lyapunov index to reveal the maximum prediction range of RUL. The analysis of actual equipment degradation verifies the effectiveness of the degradation model based on power law drift and GC process. The prediction results of the comparative case show that the prediction performance of the GC degradation model is better than Brownian motion, fractional Brownian motion, and long short-term memory neural network.

Multi-Sensor Data Fusion for Remaining Useful Life Prediction of Machining Tools by IABC-BPNN in Dry Milling Operations.

Inefficient remaining useful life (RUL) estimation may cause unpredictable failures and unscheduled maintenance of machining tools. Multi-sensor data fusion will improve the RUL prediction reliability by fusing more sensor information related to the machining process of tools. In this paper, a multi-sensor data fusion system for online RUL prediction of machining tools is proposed. The system integrates multi-sensor signal collection, signal preprocess by a complementary ensemble empirical mode decomposition, feature extraction in time domain, frequency domain and time-frequency domain by such methods as statistical analysis, power spectrum density analysis and Hilbert-Huang transform, feature selection by a Light Gradient Boosting Machine method, feature fusion by a tool wear prediction model based on back propagation neural network optimized by improved artificial bee colony (IABC-BPNN) algorithm, and the online RUL prediction model by a polynomial curve fitting method. An example is used to verify whether if the prediction performance of the proposed system is stable and reliable, and the results show that it is superior to its rivals.

Long-term gear life prediction based on ordered neurons LSTM neural networks

• A new gear RUL prediction model based on ON-LSTM is proposed. • The physical explanation of ON-LSTM is firstly achieved. • The frequency center index is found to be suitable for the gear health index. • This method has better long-term prediction ability than other traditional methods. • This method can implement RUL prediction in the case of small samples. Gear failure may affect the operation of mechanical equipment, and even cause the catastrophic break of machine and even casualties. Thus, the remaining useful life (RUL) estimation of the gear has important significance. This paper proposes a gear RUL prediction model based on ordered neurons long short-term memory (ON-LSTM) networks. The proposed methodology consists of two parts: firstly, extract the health index by computing frequency-domain features of raw signals; secondly, the ON-LSTM network model is constructed for generating the target output of the RUL prediction. Unlike the traditional LSTM neural network, the developed model integrating tree structures into LSTM to use the sequential information between neurons, so it has enhanced predictive ability. In comparative experiments, the Scores of ON-LSTM, LSTM, GRU, DLSTM and DNN are 0.398, 0.129, 0.07, 0.029 and 0.102, respectively; and ON-LSTM successfully fulfils twenty-three tasks while LSTM just fulfils five tasks in long-term prediction. Moreover, ON-LSTN only requires about 400 iterations for convergence, which is much faster than other RNNs. Experimental results show that ON-LSTM network achieved the best accuracy of short-term and long-term prediction, and it has the best robustness and convergence speed. And it can be effectively applied to the RUL prediction of gears.

A Composite Failure Precursor for Condition Monitoring and Remaining Useful Life Prediction of Discrete Power Devices

In order to prevent catastrophic failures in power electronic systems, multiple failure precursors have been identified to characterize the degradation of power devices. However, there are some practical challenges in determining the suitable failure precursor, which supports the high-accuracy prediction of remaining useful life (RUL). This article proposes a method to formulate a composite failure precursor (CFP) by taking full advantage of potential failure precursors (PFPs), where CFP is directly optimized in terms of the degradation model to improve the prediction performance. The RUL estimations of the degradation model are explicitly derived to facilitate the precursor quality calculation. For CFP formulation, a genetic programming method is applied to integrate the PFPs in a nonlinear way. As a result, a framework that can formulate a superior failure precursor for the given RUL prediction model is elaborated. The proposed method is validated with the power cycling testing results of SiC MOSFETs.

A Data-Driven Method with Feature Enhancement and Adaptive Optimization for Lithium-Ion Battery Remaining Useful Life Prediction

Data-driven methods are widely applied to predict the remaining useful life (RUL) of lithium-ion batteries, but they generally suffer from two limitations: (i) the potentials of features are not fully exploited, and (ii) the parameters of the prediction model are difficult to determine. To address this challenge, this paper proposes a new data-driven method using feature enhancement and adaptive optimization. First, the features of battery aging are extracted online. Then, the feature enhancement technologies, including the box-cox transformation and the time window processing, are used to fully exploit the potential of features. The box-cox transformation can improve the correlation between the features and the aging status of the battery, and the time window processing can effectively exploit the time information hidden in the historical features sequence. Based on this, gradient boosting decision trees are used to establish the RUL prediction model, and the particle swarm optimization is used to adaptively optimize the model parameters. This method was applied on actual lithium-ion battery degradation data, and the experimental results show that the proposed model is superior to traditional prediction methods in terms of accuracy.

Remaining Useful Life Assessment of Slewing Bearing Based on Spatial-Temporal Sequence

Slewing bearing is one of key components in the large size machinery and its remaining useful life (RUL) prediction is required to schedule a future action to avoid catastrophic events, extend life cycles, etc. The vibration-based method has been widely used in the RUL prediction. However, the spurious fluctuation usually exists in the vibration signal when the machines are operated under complex conditions. In order to enhance performance of RUL prediction model, two kinds of new health indicators are constructed by the spatial-temporal (ST) information firstly. One is the temporal indicators, which are derived by using the smoothing mean values of positive and negative vibration signal. Another is the spatial indicator, which is defined by fusing the multi-features extracted from the balance position information of vibration signal. During this process, a new data processing method proposed in this paper improves the quality of the vibration data and increases the number of samples. And then, the RUL prediction model is presented by combing the ST indicators and long-short-term memory network (LSTM) to establish the relationship between the ST indicators and the RUL of slewing bearings and overcome the sparsity of data. Moreover, in order to accelerate the adjustment of ST-LSTM model, a fine-tuning ST-LSTM model is further proposed by incorporating the generative adversarial networks (GAN) into the ST-LSTM. Experimental results verify that the proposed RUL prediction model can well estimate the RUL of slewing bearings and its performance is superior to some existing methods.

A Bayesian Optimization AdaBN-DCNN Method With Self-Optimized Structure and Hyperparameters for Domain Adaptation Remaining Useful Life Prediction

The prediction of remaining useful life (RUL) of mechanical equipment provides a timely understanding of the equipment degradation and is critical for predictive maintenance of the equipment. In recent years, the applications of deep learning (DL) methods to predict equipment RUL have attracted much attention. There are two major challenges when applying the DL methods for RUL prediction: (1) It is difficult to select the prediction model structure and hyperparameters such as network depth, learning rate, batch size, and etc. (2) The developed prediction model is domain dependent, i.e., it can only give good prediction performance in one data domain (one particular type of working conditions and fault modes). In order to meet the challenges, a novel RUL prediction method developed using a deep convolutional neural network (DCNN) combined with Bayesian optimization and adaptive batch normalization (AdaBN) is presented in this paper. The proposed RUL prediction model is validated by the turbofan engine degradation simulation dataset provided by NASA. The prediction results show that the proposed prediction model provides better prediction results than model structures obtained by random search and grid search. The results also show that the domain adaptation capability of the prediction model has been improved.

A Prognostic Model Based on DBN and Diffusion Process for Degrading Bearing

Remaining useful life (RUL) prediction is extremely significant to ensure the safe and reliable operation for bearing suffering from the deterioration. The main focus of the RUL prediction is to accurately predict the future failure event, and thus, how to quantify the prediction uncertainty will be a major concern. However, current deep learning based RUL prediction methods are difficult to reflect the uncertainty of the RUL prediction results. Toward this end, we propose a RUL prediction model based on the deep belief network (DBN) and diffusion process (DP) in this article. The proposed method consists of two parts: feature extraction combining DBN and locally linear embedding (LLE), DP-based RUL prediction. In the first part, DBN is used to extract deep hidden features behind the monitoring signals, and then the features with higher tendency are screened as the input of LLE. The health index that can truly reflect the bearing health condition is further determined through LLE. In the second part, a health index evolving model based on DP is presented and the probability density function (PDF) of the predicted RUL is accordingly derived in the sense of the first hitting time (FHT). As such, the proposed method holds promise to improve the prediction accuracy and facilitate the prognostic uncertainty. Finally, experimental studies on the bearing degradation data and the associated comparative analysis verify the effectiveness and superiority of the proposed method.

Remaining Useful Life Prediction with Similarity Fusion of Multi-Parameter and Multi-Sample Based on the Vibration Signals of Diesel Generator Gearbox

The prediction of electrical machines’ Remaining Useful Life (RUL) can facilitate making electrical machine maintenance policies, which is important for improving their security and extending their life span. This paper proposes an RUL prediction model with similarity fusion of multi-parameter and multi-sample. Firstly, based on the time domain and frequency domain extraction of vibration signals, the performance damage indicator system of a gearbox is established to select the optimal damage indicators for RUL prediction. Low-pass filtering based on approximate entropy variance (Aev) is introduced in this process because of its stability. Secondly, this paper constructs Dynamic Time Warping Distance (DTWD) as a similarity measurement function, which belongs to the nonlinear dynamic programming algorithm. It performed better than the traditional Euclidean distance. Thirdly, based on DTWD, similarity fusion of multi-parameter and multi-sample methods is proposed here to achieve RUL prediction. Next, the performance evaluation indicator Q is adopted to evaluate the RUL prediction accuracy of different methods. Finally, the proposed method is verified by experiments, and the Multivariable Support Vector Machine (MSVM) and Principal Component Analysis (PCA) are introduced for comparative studies. The results show that the Mean Absolute Percentage Error (MAPE) of the similarity fusion of multi-parameter and multi-sample methods proposed here is below 14%, which is lower than MSVM’s and PCA’s. Additionally, the RUL prediction based on the DTWD function in multi-sample similarity fusion exhibits the best accuracy.

Model predykcji pozostałego czasu pracy stacji kosmicznej

Space station is a very complex system, and its remaining useful life will be affected by the key equipment, cosmonauts’ maintenance activities as well as space environments. It is important for the operation management of a space station to predict its remaining useful life (RUL). A valid RUL prediction model is the key foundation for this issue, which motivates the research presented in this paper. Firstly, different types of space station life are defined. Secondly, the function and performance requirements as well as the operation mission program of the space station are analysed, which are further used to confirm the model development precondition. A life prediction model is then proposed by synthetically taking account of the safety, reliability and maintainability restrictions. Finally, the data requirement for supporting the RUL prediction is determined. Based on this work, a comprehensive procedure for RUL prediction model development is constructed for the operation management engineers of the space station. If the data of the development and operation is adequate, RUL prediction of the space station can be well implemented, and can be further leveraged to support the space station operation management.

Predicting Remaining Useful Life of Rolling Bearings Based on Deep Feature Representation and Transfer Learning

For the data-driven remaining useful life (RUL) prediction for rolling bearings, the traditional machine learning-based methods generally provide insufficient feature representation and adaptive extraction. Although deep learning-based RUL prediction methods can solve these problems to some extent, they still do not yield satisfactory predictive results due to less degradation data and inconsistent data distribution among different bearings. To solve these problems, a new RUL prediction method based on deep feature representation and transfer learning is proposed in this paper. This method includes an off-line stage and an online stage. In the off-line stage, the Hilbert–Huang transform marginal spectra of the raw vibration signal of auxiliary bearings are first calculated as the input, and then contractive denoising autoencoder is introduced to extract deep features with good and stable fault representation. Second, by using the obtained deep features and Pearson’s correlation coefficient, a new health condition assessment method is proposed to divide the whole life of each bearing into a normal state and a fast-degradation state. Finally, using the extracted deep features and their RUL values, an RUL prediction model for the fast-degradation state is trained by means of a least-square support vector machine. In the online stage, a kind of transfer learning algorithm, i.e., transfer component analysis, is introduced to sequentially adjust the features of target bearing from auxiliary bearings, and then the corresponding RUL is predicted using the corrected features. Results using the PHM Challenging 2012 data set show a significant performance improvement when using the proposed method in terms of predictive accuracy and numerical stability.