scholarly journals Remaining Useful Life Prediction of Bearing with Vibration Signals Based on a Novel Indicator

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Bo Wu ◽  
Wei Li ◽  
Ming-quan Qiu
Keyword(s):  
Neural Network ◽  
Health Management ◽  
Rbf Neural Network ◽  
Degradation Process ◽  
Rate Function ◽  
Remaining Useful Life ◽  
Maintenance Cost ◽  
Failure Rate Function ◽  
Useful Life ◽  
The Time Domain

Aiming at reducing the production downtime and maintenance cost, prognostics and health management (PHM) of rotating machinery often includes the remaining useful life (RUL) prediction of bearings. In this paper, a method combining the generalized Weibull failure rate function (WFRF) and radial basis function (RBF) neural network is developed to deal with the RUL prediction of bearings. A novel indicator, namely, the power value on the sensitive frequency band (SFB), is proposed to track bearing degradation process. Generalized WFRF is used to fit the degradation indicator series to reduce the effect of noise and avoid areas of fluctuation in the time domain. RBF neural network is employed to predict the RUL of bearings with times and fitted power values at present and previous inspections as input. Meanwhile, the life percentage is selected as output. The performance of the proposed method is validated by an accelerated bearing run-to-failure experiment, and the results demonstrate the advantage of this method in achieving more accurate RUL prediction.

An improved deep convolution neural network for predicting the remaining useful life of rolling bearings

2020 ◽  
pp. 1-9
Author(s):  
Yiming Guo ◽  
Hui Zhang ◽  
Zhijie Xia ◽  
Chang Dong ◽  
Zhisheng Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Degradation Process ◽  
Rolling Bearing ◽  
Remaining Useful Life ◽  
Convolution Neural Network ◽  
Dual Channel ◽  
Time Domain Data ◽  
Channel Input ◽  
Deep Convolution Neural Network ◽  
Useful Life

The rolling bearing is the crucial component in the rotating machinery. The degradation process monitoring and remaining useful life prediction of the bearing are necessary for the condition-based maintenance. The commonly used deep learning methods use the raw or processed time domain data as the input. However, the feature extracted by these approaches is insufficient and incomprehensive. To tackle this problem, this paper proposed an improved Deep Convolution Neural Network with the dual-channel input from the time and frequency domain in parallel. The proposed methodology consists of two stages: the incipient failure identification and the degradation process fitting. To verify the effectiveness of the method, the IEEE PHM 2012 dataset is adopted to compare the proposed method and other commonly used approaches. The results show that the improved Deep Convolution Neural Network can effectively describe the degradation process for the rolling bearing.

scholarly journals Predicting remaining useful life of rolling bearings based on deep feature representation and long short-term memory neural network

2018 ◽  
Vol 10 (12) ◽  
pp. 168781401881718 ◽  
Author(s):  
Wentao Mao ◽  
Jianliang He ◽  
Jiamei Tang ◽  
Yuan Li
Keyword(s):  
Neural Network ◽  
Life Prediction ◽  
Short Term Memory ◽  
Degradation Process ◽  
Remaining Useful Life ◽  
Feature Representation ◽  
Short Term ◽  
Term Memory ◽  
Useful Life ◽  
Long Short Term Memory

For bearing remaining useful life prediction problem, the traditional machine-learning-based methods are generally short of feature representation ability and incapable of adaptive feature extraction. Although deep-learning-based remaining useful life prediction methods proposed in recent years can effectively extract discriminative features for bearing fault, these methods tend to less consider temporal information of fault degradation process. To solve this problem, a new remaining useful life prediction approach based on deep feature representation and long short-term memory neural network is proposed in this article. First, a new criterion, named support vector data normalized correlation coefficient, is proposed to automatically divide the whole bearing life as normal state and fast degradation state. Second, deep features of bearing fault with good representation ability can be obtained from convolutional neural network by means of the marginal spectrum in Hilbert–Huang transform of raw vibration signals and health state label. Finally, by considering the temporal information of degradation process, these features are fed into a long short-term memory neural network to construct a remaining useful life prediction model. Experiments are conducted on bearing data sets of IEEE PHM Challenge 2012. The results show the significance of performance improvement of the proposed method in terms of predictive accuracy and numerical stability.

Identifying Parameters for Nonlinear Degradation-Based Prognostics

2019 ◽  
pp. 237-260
Author(s):  
Feng Yang ◽  
Mohamed Salahuddin
Keyword(s):  
Parameter Identification ◽  
Induction Motors ◽  
Health Management ◽  
Degradation Process ◽  
Remaining Useful Life ◽  
Prognostics And Health Management ◽  
Health Index ◽  
Identification Method ◽  
Useful Life ◽  
Parameter Values

Prognostics and health management (PHM) methodologies are increasingly playing active roles in improving the availability, reliability, efficiency, productivity, and safety of systems in many industries. In predicting the remaining useful life (RUL), this chapter introduces a prognostics framework with health index (HI) formulation, with specific emphasis on incorporating and validating nonlinear HI degradations. The key issue to the success of this framework is how to identify appropriate parameters in describing the behavior of the nonlinear HI degradations. Using exponential HI degradation as an example in predicting the RULs of induction motors, this chapter discusses three different explorations in verifying the existence of good parameter values as well as identifying the appropriate parameters automatically. Comprehensive experiments were carried out with degradation process (DP) data from eight induction motors, and it was discovered that good parameters can be automatically determined with the proposed parameter identification method.

Remaining Useful Life Predictions for Turbofan Engine Degradation Using Online Long Short-Term Memory Network

2019 ◽  
Author(s):  
Pallabi Kakati ◽  
Devendra Dandotiya ◽  
Bhaskar Pal
Keyword(s):  
Neural Network ◽  
Time Domain ◽  
Short Term Memory ◽  
Markov Models ◽  
Health Management ◽  
Remaining Useful Life ◽  
Short Term ◽  
Term Memory ◽  
Useful Life ◽  
Long Short Term Memory

Abstract In any industrial system, accurate prediction of Remaining Useful Life (RUL) is important for Prognostics and Health Management (PHM), so as to detect breakdown of system well in advance and take proper measures. Different methods are available in the literature that have been proposed for prediction of RUL. Among these methods, the data driven method is accepted to be the most reliable by many researchers, due to the use of real time sensor based vibrational and/or pressure data. These data are acquired in time domain. Methods such as Recurrent Neural Networks (RNNs), Convolutional Neural Network (CNN), Hidden Markov Models (HMMs) are generally applied in this area. Nevertheless, all these methods have issues while dealing with dependencies in these data. In this context, Long Short-Term Memory (LSTM) neural network has been proposed to deal with these dependencies while predicting RUL of any system. The LSTM model has the advantage of retaining time domain information for a long duration of time. However, with the arrival of new data, the model needs to be updated. In this regard, a new online method based on LSTM network is proposed in this paper. The use of online technique offers us to retrain the model as new data arrives, which helps in improving the accuracy of the estimated RUL. To illustrate the application of the proposed online LSTM method, we have used the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) turbofan dataset. The results show an improved efficiency compared to the previously proposed methods for RUL estimation.

Statistical Health Diagnostics for Water-Cooled Power Generator Stator Winding Against Water Absorption

2014 ◽  
Author(s):  
Kyung Min Park ◽  
Byeng D. Youn ◽  
Joung Taek Yoon ◽  
Hee Soo Kim ◽  
Beom Chan Jang
Keyword(s):  
Water Absorption ◽  
Health Management ◽  
Health Condition ◽  
Remaining Useful Life ◽  
Maintenance Cost ◽  
Stator Winding ◽  
Power Generator ◽  
Reasoning System ◽  
Useful Life ◽  
Generator Stator

One of most important components in power generator is a stator winding since an unexpected failure of the water absorbed-winding leads to plant shut-down and substantial loss. Typically the stator winding is maintained with a time- or usage-based strategy, which could result in substantial waste of remaining life, high maintenance cost and low plant availability. Recently, the field of prognostics and health management offers general diagnostic and prognostic techniques to precisely assess the health condition and robustly predict the remaining useful life of an engineered system, with an aim to address the aforementioned deficiencies. This research aims at developing health reasoning system of power generator stator winding with physical and statistical analysis against water absorption. And it is based upon the capacitance measurements on winding insulations. In particular, a new health measure, Directional Mahalanobis Distance (DMD), is proposed to quantify the health condition. In addition, the empirical health grade system based upon the proposed technique, DMD, is carried out with the maintenance history. The smart health reasoning system is validated using eight years’ field data from eight generators, each of which contains forty two windings.

scholarly journals Remaining Useful Life Prediction of Bearings Using Ensemble Learning: The Impact of Diversity in Base Learners and Features

2020 ◽  
Vol 21 (2) ◽  
Author(s):  
Junchuan Shi ◽  
Tianyu Yu ◽  
Kai Goebel ◽  
Dazhong Wu
Keyword(s):  
Ensemble Learning ◽  
Health Management ◽  
Degradation Process ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Learning Methods ◽  
Vibration Data ◽  
Model Based ◽  
Useful Life ◽  
The Impact

Abstract Prognostics and health management (PHM) of bearings is crucial for reducing the risk of failure and the cost of maintenance for rotating machinery. Model-based prognostic methods develop closed-form mathematical models based on underlying physics. However, the physics of complex bearing failures under varying operating conditions is not well understood yet. To complement model-based prognostics, data-driven methods have been increasingly used to predict the remaining useful life (RUL) of bearings. As opposed to other machine learning methods, ensemble learning methods can achieve higher prediction accuracy by combining multiple learning algorithms of different types. The rationale behind ensemble learning is that higher performance can be achieved by combining base learners that overestimate and underestimate the RUL of bearings. However, building an effective ensemble remains a challenge. To address this issue, the impact of diversity in base learners and extracted features in different degradation stages on the performance of ensemble learning is investigated. The degradation process of bearings is classified into three stages, including normal wear, smooth wear, and severe wear, based on the root-mean-square (RMS) of vibration signals. To evaluate the impact of diversity on prediction performance, vibration data collected from rolling element bearings was used to train predictive models. Experimental results have shown that the performance of the proposed ensemble learning method is significantly improved by selecting diverse features and base learners in different degradation stages.

scholarly journals A Reliable Prognosis Approach for Degradation Evaluation of Rolling Bearing Using MCLSTM

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1864 ◽  
Author(s):  
Gangjin Huang ◽  
Hongkun Li ◽  
Jiayu Ou ◽  
Yuanliang Zhang ◽  
Mingliang Zhang
Keyword(s):  
Short Term Memory ◽  
Health Management ◽  
Degradation Process ◽  
Rolling Bearing ◽  
Remaining Useful Life ◽  
Rolling Bearings ◽  
Variable Model ◽  
Management Technology ◽  
Useful Life ◽  
Degradation Evaluation

Prognostics and health management technology (PHM), a measure to ensure the reliability and safety of the operation of industrial machinery, has attracted attention and application adequately. However, how to use the monitored information to evaluate the degradation of rolling bearings is a significant issue for its predictive maintenance and autonomic logistics. This work presents a reliable health prognosis approach to estimate the health indicator (HI) and remaining useful life (RUL) of rolling bearings. Firstly, to accurately capture the degradation process, a novel health index (HI) is constructed based on correlation kurtosis for different iteration periods and a Gaussian process latency variable model (GPLVM). Then, a multiple convolutional long short-term memory (MCLSTM) network is proposed to predict HI values and RUL values. Finally, we perform experimental datasets of rolling bearings, demonstrating that the presented method surpasses other state-of-the-art prognosis approaches. The results also confirm the feasibility of the presented method in industrial machinery.

An improved prognostics model with its application to the remaining useful life of turbofan engine

2021 ◽  
pp. 095441002110504
Author(s):  
Jia-Jun He ◽  
Yong-Ping Zhao
Keyword(s):  
Prior Knowledge ◽  
Health Management ◽  
Remaining Useful Life ◽  
Maintenance Cost ◽  
Actual System ◽  
Health States ◽  
Estimation Errors ◽  
Turbofan Engine ◽  
Useful Life

Machinery prognostics play a crucial role in upgrading machinery service and optimizing machinery operation and maintenance schedule by forecasting the remaining useful life (RUL) of the monitored equipment, which has become more and more popular in recent years. The safety of aviation is one of the issues that people are most concerned about in the field of transportation, since it might cause disastrous loss of life and property once accident happened. The turbofan engine is an important part of the aircraft that provides thrust for plane. With aging, the turbofan engine becomes prone to failures. As a result, it would be worth studying prognostics in turbofan engine to improve the reliability of machinery and reduce unnecessary maintenance cost. Recently, a data-driven prognostics modeling strategy called the classification of predictions strategy (CPS) was proposed, in which the continuous signal and the discrete modes of an actual system come together to achieve RUL estimation. However, machine health states measured from classification rarely have just one potential situation, and this strategy cannot determine whether the fault occurs or not by a certain probability which comes closer to reality. Moreover, since there is no information and prior knowledge of prognostics application, it is hard to obtain the probability of various situations from raw measured data. Hence, based on previous work, this paper proposes an improved prognostics modeling method named the classification of predictions strategy with decision probability (CPS-DP), whose key innovations mainly include three parts: (1) decision probability process (DPP) where each step of multi-step prediction obeys geometric distribution and can judge whether the failure state occurs using the decision probability; (2) decision probability calculation (DPC) algorithm, which is first proposed by this paper and can calculate the values of decision probability without prior knowledge of prognostics application; and (3) withdrawal mechanism optimizer (WMO), which is specially designed to compensate for the shortcomings of DPP and further enhance the performance of the prognostics model. In brief, first, CPS is used to build a basic prognostics model to acquire RUL estimation results, in which the information applied to find the probability has been contained. Later, the mean of RUL estimation errors is figured from the results, which is further employed to calculate the probability using DPC algorithm. Then, CPS-DP can be achieved by means of integrating two parts: DPP and CPS. Furthermore, to further improve the performance, WMO is utilized to optimize CPS-DP with rolling back predictions. Ultimately, an enhanced prognostic model based on CPS-DP is set up through uniting CPS, DPP, and WMO. To validate the proposed method, experimental results on the turbofan engine in 2008 prognostics and health management competition are investigated.

Remaining useful life estimation: a review on stochastic process-based approaches

2020 ◽  
Vol 14 ◽  
Author(s):  
Dangbo Du ◽  
Jianxun Zhang ◽  
Xiaosheng Si ◽  
Changhua Hu
Keyword(s):  
Stochastic Process ◽  
Health Management ◽  
Remaining Useful Life ◽  
Future Trends ◽  
Prognostics And Health Management ◽  
Inverse Gaussian ◽  
Life Estimation ◽  
Inverse Gaussian Process ◽  
Pros And Cons ◽  
Useful Life

Background: Remaining useful life (RUL) estimation is the central mission to the complex systems’ prognostics and health management. During last decades, numbers of developments and applications of the RUL estimation have proliferated. Objective: As one of the most popular approaches, stochastic process-based approach has been widely used for characterizing the degradation trajectories and estimating RULs. This paper aimed at reviewing the latest methods and patents on this topic. Methods: The review is concentrated on four common stochastic processes for degradation modelling and RUL estimation, i.e., Gamma process, Wiener process, inverse Gaussian process and Markov chain. Results: After a briefly review of these four models, we pointed out the pros and cons of them, as well as the improvement direction of each method. Conclusion: For better implementation, the applications of these four approaches on maintenance and decision-making are systematically introduced. Finally, the possible future trends are concluded tentatively.

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