Life Prediction Of Machinery Research Articles

A core space gradient projection-based continual learning framework for remaining useful life prediction of machinery under variable operating conditions

Recently, continual learning has received particular attention in machinery remaining useful life (RUL) prediction, which enables prognostics networks to gradually improve performance without laborious retraining. Existing studies, however, have the following limitations: 1) The impacts of variable operating conditions are not explicitly considered during continual learning. 2) The continual learnability of prognostics network is limited by the lack of knowledge compression and de-redundancy. To overcome the abovementioned limitations, a novel continual learning framework is proposed for RUL prediction of machinery under variable operating conditions. First, a multi-kernel swarm convolution block is devised to automatically capture degradation features during continual learning without increasing computational consumption, which combines convolutional operations at various scales and focuses attention on appropriate scales based on operating condition information. Then, core space gradient projection is proposed for continual learning of prognostics networks, which mitigates forgetting by guiding gradient descent along the orthogonal direction of the previously input subspace. This approach also ensures the network learnability during continual learning by knowledge compression and de-redundancy to identify core space. The proposed framework is verified using accelerated degradation datasets of rolling element bearings with variable operating conditions. Experimental results show that the proposed framework is superior to some existing continual learning-based prognostics approaches for RUL prediction under variable operating conditions.

An Optimal-Subdomain Generalization Method for Remaining Useful Life Prediction of Machinery Under Time-Varying Operation Conditions

An Optimal-Subdomain Generalization Method for Remaining Useful Life Prediction of Machinery Under Time-Varying Operation Conditions

Deep multisource parallel bilinear-fusion network for remaining useful life prediction of machinery

Deep multisource parallel bilinear-fusion network for remaining useful life prediction of machinery

Prognostics and Remaining Useful Life Prediction of Machinery: Advances, Opportunities and Challenges

As the fundamental and key technique to ensure the safe and reliable operation of vital systems, prognostics with an emphasis on the remaining useful life (RUL) prediction has attracted great attention in the last decades. In this paper, we briefly discuss the general idea and advances of various prognostics and RUL prediction methods for machinery, mainly including data-driven methods, physics-based methods, hybrid methods, etc. Based on the observations from the state of the art, we provide comprehensive discussions on the possible opportunities and challenges of prognostics and RUL prediction of machinery so as to steer the future development.

Deep Learning-Based Cloud–Edge Collaboration Framework for Remaining Useful Life Prediction of Machinery

In the context of Industry 4.0, intelligent manufacturing services put forward the requirement of rapid response in the remaining useful life (RUL) of machinery. To achieve fast-responding and highly accurate RUL prediction services while mining the intra-kernel correlations of the sensor monitoring data, a deep learning-based cloud–edge collaboration framework was proposed in this article. We encapsulated a cloud prediction engine (Cloud-PE) with a deep prediction model in the cloud service layer and an edge prediction engine (Edge-PE) with a shallow prediction model in the edge service layer. The Cloud-PE assisted the Edge-PE in achieving fast and highly accurate RUL prediction by sharing depth model parameters. Both prediction models were constructed on the basis of a novel blueprint separable convolution neural network. To continuously improve the performance of Edge-PE in a context-aware manner, we adopted an update method for the Edge-PE with the assistance of the Cloud-PE. The experimental results demonstrated that the proposed framework can provide more accurate RUL prediction than existing data-driven prediction methods, and the training time of the prediction model is also significantly reduced.

Feature Fusion based Ensemble Method for remaining useful life prediction of machinery

The signal analysis features and deep representation features have been widely utilized to predict the Remaining Useful Life (RUL) of machinery. However, existing studies rarely fuse these features for RUL prediction to explore their complementarity. Therefore, this paper proposes a Feature Fusion based Ensemble Method (FFEM) that makes full use of the characteristics of signal analysis features and deep representation features. First, features are extracted by signal analysis and deep learning methods, respectively. The time-domain features, frequency-domain features, and time–frequency​ domain features are extracted by different signal analysis methods, while the deep representation features are from the bidirectional long short-term memory networks. Then, an improved random subspace method is proposed, which fuses different types of features based on group-based sparse learning to use the complementarity among features. Furthermore, accurate and diverse base learners are generated and the aggregation strategy, mean rule, is adopted for predicting RUL. To validate the proposed FFEM, experiments on the run-to-failure datasets of bearings are conducted, and the experimental results verify that the proposed method greatly improves the RUL prediction performance and surpasses other existing ensemble learning methods.

Frequency Hoyer attention based convolutional neural network 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.

A deep attention residual neural network-based remaining useful life prediction of machinery

Remaining useful life (RUL) estimation has always been an essential task of prognostics health management (PHM). However, degradation data of machinery is seldom available, which limits the performance of data-driven models. Besides, the prediction performance of the established model may also be awfully affected by working environment of monitored machinery. To over these problem, a novel deep attention residual neural network (DARNN) is proposed by us for RUL prediction of machinery. The proposed DARNN has following advantages: (1) Representations of degradation can be effectively extracted from signals by the proposed DARNN. (2) The prediction performance and self-stability of the proposed model significantly surpassed some existing methods. Two case studies are conducted for the RUL prediction of bearings and turbofan engines respectively to verify the effectiveness of the proposed model.

A BiGRU method for remaining useful life prediction of machinery

Remaining useful life (RUL) prediction, allowing for mechanical prediction maintenance, reduces the unplanned expensive maintenance greatly. Deep learning methods have provided better point estimation for RUL prediction due to their powerful feature extraction capability. Because of the measurement noise and model parameters, the prediction results usually vary greatly. In order to express the uncertainty of prediction, it is necessary to calculate not only the determined RUL prediction value, but also the confidence interval (CI) of RUL. In this paper, a bidirectional gated recurrent unit (BiGRU) RUL prediction method based on bootstrap method is proposed. The confidence interval (CI) of RUL can be obtained through bootstrap method. The validity of the proposed method is demonstrated by ABLT-1A bearing data. Obtaining the uncertainty in the RUL prediction has great significance for the actual production and manufacturing.

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

Deep learning is becoming more appealing in remaining useful life (RUL) prediction of machines, because it is able to automatically build the mapping relationship between the raw data and the corresponding RUL by representation learning. Among deep learning models, convolutional neural networks (CNNs) are gaining special attention because of its powerful ability in dealing with time-series signals, and have achieved promising results in current studies. These studies, however, suffer from the two limitations: (1) The temporal dependencies of different degradation states are not considered during network construction; and (2) The uncertainty of RUL prediction results cannot be obtained. To overcome the above-mentioned limitations, a new framework named recurrent convolutional neural network (RCNN) is proposed in this paper for RUL prediction of machinery. In RCNN, recurrent convolutional layers are first constructed to model the temporal dependencies of different degradation states. Then, variational inference is used to quantify the uncertainty of RCNN in RUL prediction. The proposed RCNN is evaluated using vibration data from accelerated degradation tests of rolling element bearings and sensor data from life testing of milling cutters, and compared with some state-of-the-art prognostics approaches. Experimental results demonstrate the effectiveness and superiority of RCNN in improving the accuracy and convergence of RUL prediction. More importantly, RCNN is able to provide a probabilistic RUL prediction result, which breaks the inherent limitation of CNNs and facilitates maintenance decision making.

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

The growth of the Industrial Internet of Things (IIoT) has generated a renewed emphasis on research of prognostic degradation modeling whereby degradation signals, such as vibration signals, temperature and acoustic emissions, are used to estimate the state-of-health and predict the remaining useful life (RUL). Besides the inherent system state, external operating conditions, such as the rotational speed and load also play a significant role in the behavior of degradation signals. Time-varying operating conditions often cause two major effects on the degradation signals. First, they change the degradation rate of systems. Second, they cause signal jumps at condition change-points. These two factors make RUL prediction more difficult under time-varying operating conditions. This paper proposes a RUL prediction method by introducing these two factors into a state-space model. Changes in the degradation rate are introduced into a state transition function, and jumps in the degradation signals are introduced into a measurement function. The separate analysis of these two factors makes it possible to distinguish their own contributions to RUL prediction, thus avoiding false alarms and improving the prediction accuracy. The effectiveness of the proposed method is demonstrated using both a simulation study and an accelerated degradation test of rolling element bearings.

Remaining Useful Life Prediction of Machinery based on K-S Distance and LSTM Neural Network

Remaining Useful Life Prediction of Machinery based on K-S Distance and LSTM Neural Network

Sparse Optimistic Based on Lasso-LSQR and Minimum Entropy De-Convolution with FARIMA for the Remaining Useful Life Prediction of Machinery.

To reduce the maintenance cost and safeguard machinery operation, remaining useful life (RUL) prediction is very important for long term health monitoring. In this paper, we introduce a novel hybrid method to deal with the RUL prediction for health management. Firstly, the sparse reconstruction algorithm of the optimized Lasso and the Least Square QR-factorization (Lasso-LSQR) is applied to compressed sensing (CS), which can realize the sparse optimization for long term health monitoring data. After the sparse signal is reconstructed, the minimum entropy de-convolution (MED) is used to identify the fault characteristics and to obtain significant fault information from the machinery operation. Health indicators with Skip-over, sample entropy and approximate entropy are then performed to track the degradation of the machinery process. The performance analysis of the Skip-over is superior to other indicators. Finally, Fractal Autoregressive Integrated Moving Average model (FARIMA) is employed to predict the Skip-over using the R/S method. The analysis results evidence that the novel hybrid method yields a good performance, and such method can achieve highly accurate RUL prediction and safeguard machinery operation for long term monitoring.

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

Remaining useful life (RUL) prediction allows for predictive maintenance of machinery, thus reducing costly unscheduled maintenance. Therefore, RUL prediction of machinery appears to be a hot issue attracting more and more attention as well as being of great challenge. This paper proposes a model-based method for predicting RUL of machinery. The method includes two modules, i.e., indicator construction and RUL prediction. In the first module, a new health indicator named weighted minimum quantization error is constructed, which fuses mutual information from multiple features and properly correlates to the degradation processes of machinery. In the second module, model parameters are initialized using the maximum-likelihood estimation algorithm and RUL is predicted using a particle filtering-based algorithm. The proposed method is demonstrated using vibration signals from accelerated degradation tests of rolling element bearings. The prediction result identifies the effectiveness of the proposed method in predicting RUL of machinery.

A Relevance Vector Machine Prediction Method Based on Adaptive Multi-kernel Combination and Its Application to Remaining Useful Life Prediction of Machinery

A Relevance Vector Machine Prediction Method Based on Adaptive Multi-kernel Combination and Its Application to Remaining Useful Life Prediction of Machinery