A Model-Based Method for Remaining Useful Life Prediction of Machinery

Remaining useful life (RUL) prediction of machinery plays a significant role for predictive maintenance, thus attracting more and more attentions in recent years. Stochastic process model-based methods are widely used in the RUL prediction of machinery. One of the major issues in the stochastic process model-based methods is that how to deal with the unit-to-unit variability during the RUL prediction process. Traditional methods generally handle this issue by introducing a unit-to-unit variability parameter into the model expression and estimate the parameter using the maximum likelihood estimation (MLE) algorithm. There exist two major limitations in the traditional methods. 1) The degradation processes are assumed to be dependent on only the age, which restricts their implementation in the cases of the state-dependent degradation processes. 2) They do not discuss the influence of the unit-to-unit variability in the RUL prediction processes systematically. To deal with these two limitations, a new RUL prediction method based on age- and state-dependent stochastic process models is proposed in this paper. In the proposed method, a generalized expression of the age- and stage-dependent stochastic process models is generated. An enhanced MLE algorithm is developed to estimate the model parameters according to the measurements of the available training units. And the unit-to-unit variability parameter is updated according to the real-time measurements of the testing unit. The effectiveness of the proposed method is demonstrated using a numerical simulation dataset of fatigue crack-growth.

A BiGRU method for remaining useful life prediction of machinery

Remaining useful life (RUL) prediction based on vibration data is a vital part of bearing prognostic and health management, which can be applied to formulate a suitable maintenance strategy. Recently, attention mechanism has been widely studied and applied in the field of prognostics, which can adaptively enhance the features for RUL prediction and weaken the features interfering with the accurate estimation of the condition. However, insufficient priori information has been provided to the deep learning model via the attention mechanism. The domain knowledge of the special structure and the characteristics of the bearing vibration signal is underutilized in the conventional attention mechanism. An innovative attention based RUL prediction model, called frequency Hoyer attention based convolutional neural network (FHA-CNN), is proposed in this study, which combines a deep learning model and signal processing method organically. The 1D convolutional layer and isometric empirical wavelet transform are developed to extract the latent representation of vibration signals from different scales. The proposed FHA is applied to calculate the weight of the feature map adaptively, in which three types of Hoyer index are adopted to comprehensively evaluate the contribution of each frequency part to the degradation of rolling bearings from the frequency domain perspective. To verify the superiority of the proposed method, two run-to-failure experimental dataset case studies are analysed. The obtained results indicate that the proposed FHA-CNN model exhibits a better performance than conventional deep learning-based RUL prediction methods. In addition, the proposed method concentrates on the special structure of the bearing vibration signal and provides a novel insight into the decision-making processes of deep neural networks.

Remaining useful life prediction of machinery under time-varying operating conditions based on a two-factor state-space model

Recurrent convolutional neural network: A new framework for remaining useful life prediction of machinery

Continual learning is gaining special attention in remaining useful life (RUL) prediction of machinery recently, which enables deep prognostics networks to use incremental samples to progressively improve network performance without laborious retraining. Nonetheless, current studies exhibit several constraints: 1) An explicit mechanism is lacking in preventing the loss of pivotal memories after multiple continual learning stages. 2) A sampling-enhanced replay technique is lacking for continual learning-based RUL prediction. To address the abovementioned limitations, this article proposes a complementary continual learning framework for RUL prediction of machinery, which contains two novel characteristics, i.e., long-term potentiation and associative replay. These two characteristics are complementary and coenhanced. The long-term potentiation focuses on multistage continual learning, which is able to prevent deep prognostics networks from forgetting the formerly learned degradation knowledge. The associative replay pays attention to each new continual learning stage, which is able to consolidate typical degradation knowledge into new network learning. The proposed framework is verified using run-to-failure datasets from rolling element bearings, and the framework is also compared with some state-of-the-art methods. Experimental results indicate that the proposed framework can possess lower forgetting and achieve better prognostics performance reinforcement during continual learning.

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.

As the key mechanical component of the power system, remaining useful life (RUL) prediction for bearing has attracted more and more attention. Most of the previous studies are based on the vibration signals. In this paper, RUL prediction is performed based on online oil monitoring parameters and vibration signals. First, several groups of bearing full lifetime test are carried out on the bearing life test bench, and the bearing full lifetime monitoring data such as metal debris, oil viscosity, temperature, density, dielectric constant, water content and vibration signals are collected. After the pretreatment and feature extraction of oil parameters and vibration signals, the support vector machine models for bearing wear status evaluation and RUL prediction are trained and constructed. Secondly, the trigger mechanism of bearing start-up of RUL prediction is analyzed and discussed. The RUL prediction model is verified by full lifetime test data of testing SKF6208 bearings. The results show that the combination characteristics of online oil parameters and vibration signals provides a promising way for accurate evaluation of bearing wear status and effective remaining useful life prediction.

Remaining useful life (RUL) prediction of machinery is a major task in condition-based maintenance, which is able to provide crucial guidance for preventive maintenance. To guarantee the accuracy for the RUL prediction of machinery subjected to two-phase degradation process, the interactive multiple model (IMM) filtering technique has been used because of its capability in estimating the state and the phase dynamically. However, there are two limitations in the IMM based methods. 1) A crucial parameter of the IMM, i.e., the transition probabilities matrix (TPM) of the IMM, is set manually in existing IMM based methods, which often leads to inaccurate state estimation results. 2) The phase estimation is derived as one-step filtering results without considering the overall evolution of the degradation trend, which is unable to describe the phase transition, thus causing inaccurate phase estimation results. To tackle these two limitations, an improved RUL prediction method is proposed in this paper for machinery subjected to two-phase degradation process. In the proposed method, a two-phase degradation model is constructed to describe the degradation process. A nonlinear IMM technique, i.e., the interactive multiple model particle filter (IMMPF) is utilized for the state and the phase estimation, where the TPM is estimated using the numerical-integration TPM estimation (NI-TPME) algorithm instead of being pre-specified manually. The transition point (TP) distribution is adopted to reflect the overall evolution of the degradation trend, and is further used to modify the phase estimation from the IMMPF. Finally, the RUL is predicted by Monte Carlo simulation. The effectiveness of the proposed method is demonstrated by a numerical simulation study.

This paper discusses the significance and interpretation of uncertainty in the remaining useful life (RUL) prediction of components used in several types of engineering applications, and answers certain fundamental questions such as “Why is the RUL prediction uncertain?”, “How to interpret the uncertainty in the RUL prediction?”, and “How to compute the un- certainty in the RUL prediction?”. Prognostics and RUL pre- diction are affected by various sources of uncertainty. In or- der to make meaningful prognostics-based decision-making, it is important to analyze how these sources of uncertainty affect the remaining useful life prediction, and thereby, compute the overall uncertainty in the remaining useful life pre- diction. The classical (frequentist) and Bayesian (subjective) interpretations of uncertainty and their implications on prognostics are explained, and it is argued that the Bayesian interpretation of uncertainty is more suitable for remaining useful life prediction in the context of condition-based monitoring. Finally, it is demonstrated that the calculation of uncertainty in remaining useful life can be posed as an uncertainty propagation problem, and the practical challenges involved in computing the uncertainty in the remaining useful life prediction are discussed.

Remaining useful life (RUL) prediction, allowing for mechanical predictive maintenance, reduces unplanned and expensive maintenance greatly. One of the great challenges of data-driven RUL prediction is to extract the features that describe the actual degradation process. This paper presents a health indicator (HI) construction method based on a sparse auto-encoder with regularization (SAEwR) model for rolling bearings. This paper includes two modules, HI construction and RUL prediction. In the stage of the HI construction, the original features are compressed and extracted by the SAEwR model. The extracted features are sorted according to the trendability, and the features with large trendability are selected to construct the HI by using minimum quantization error. In the module of RUL prediction, the maximum likelihood estimation method is used to estimate the parameters of the prediction model, and a particle filter-based RUL prediction with degradation model is proposed. The proposed method is benchmarked with variational auto-encoder, auto-encoder methods and principal component analysis. The data from PRONOSTIA and ABLT-1A platform support the value of our approach.

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.

Journal bearings are the key components of the nuclear circulating water pump (NCWP), and accurate remaining useful life (RUL) prediction is of great significance for improving the reliability, safety, and maintenance planning of NCWP. However, it is difficult to quantify the uncertainty of bearing RUL based on the current deep learning (DL) model, resulting in a lack of credibility and effective convincing for RUL predicted by the model. Meanwhile, all existing hybrid models are basically simple combinations, and they cannot solve the uncertainty quantification problem of RUL predicted by DL. Hence, the bearing RUL prediction method based on a dynamic interactive hybrid model is proposed. Firstly, a degradation model based on a nonlinear enhanced generalized Wiener process (EGWP) is proposed, which combines gated neural networks and time-varying drift coefficients to describe the nonlinear degradation process of bearing. Then, a corrective gated recurrent unit (CGRU) network is designed to learn and predict real-time degradation increments, and the parameters of the degradation model are dynamically updated through the history and prediction of degradation increments. Finally, the bearing RUL prediction is given by the CGRU network, and the probability density function (PDF) of RUL is given by the proposed hybrid model. The performance of the proposed method is evaluated using the PHM 2012 bearing dataset and the NCWP journal bearing dataset. The results show that our proposed method can effectively predict bearing RUL and its uncertainty.

Degradation modeling and remaining useful life (RUL) prediction for products with multiple degradation features are hot topics in the prognostic and health management. The key to this problem is to describe the dependence among multiple degradation features effectively. In this paper, a multivariate degradation modeling approach based on the Bayesian dynamic linear model (BDLM) is proposed to calculate the RULs of degradation features, and the Copula function is employed to capture the dependence among RUL distributions. A combined BDLM is used to establish the multivariate degradation model, which includes two typical BDLMs, namely, the linear growth model and seasonal factors model. After the model parameters get calibrated by the maximum likelihood estimation, the model can predict the degradation process of features. Once the failure thresholds are given, the probability density function and cumulative distribution function (CDF) of RUL for each degradation feature can be obtained. Since these RUL distributions are not independent of each other, the Copula function is adopted herein to couple the CDFs. Finally, some practical testing data of a microwave component, which has two degradation features, are utilized to validate our proposed method. This paper provides a new idea for the multivariate degradation modeling and RUL prediction.

Remaining Useful Life (RUL) prediction of rotating machinery plays a critical role in Prognostics and Health Management (PHM). Data-driven methods for RUL estimation have been widely developed because they don’t depend on much prior knowledge of the system. Recurrent neural network (RNN) is capable of modeling sequential data, which has been investigated for RUL prediction with statistical features of vibration signals in time domain and frequency domain. The drawback of utilizing statistical features is the ignorance of time-frequency information, which is critical in RUL prediction because the vibration signals are non-stationary when the fault occurs. To solve this problem, a novel deep architecture, named deep recurrent convolutional neural network (DRCNN) is proposed. By incorporating convolutional operation in the process of state transition of RNN, the spatial information in time-frequency domain can be automatically learned from the vibration signals, which contributes to the improvement of prediction performance. With convolutional operation in RNN, both spatial information in time-frequency domain and previous information are employed for RUL prediction. Furthermore, by stacking recurrent convolutional neural network layer by layer, the deep architecture can learn high-level features in the time-frequency domain. Finally, experimental analysis of RUL prediction using vibration signals of run-to-failure tests are carried out. Compared with the results of conventional deep RNN method, the proposed method shows its effectiveness and superiority.