data-driven methods RUL
209497386
Research Progress on Data Driven based RUL Prediction Methods of Mechanical Equipment
With the development of the industry, the performance of large and complex systems is constantly increasing and the complexity is increasing. In the process of using mechanical equipment, there is often a phenomenon of downtime and the most of the reasons is that the related parts are faulty. As one of the foremost tasks of prognostic and health management (PHM) and condition based maintenance (CBM) the prediction of remaining useful life (RUL) for mechanical equipment is receiving more and more attention. By knowing the RUL of the equipment, it can play an important role in maintaining related equipment in advance. It is more effective than the traditional regular maintenance and post repair maintenance, thus avoiding the occurrence of malfunctions and the reduction of property loss. This paper focuses on the AI based RUL prediction methods and explains the strengths and weaknesses of each of these methods and summarizes the latest literature on various methods in the last few years. Finally, the present methods and future trends are discussed and hot spots for the future are given.
2019 Prognostics and System Health Management Conference (PHM Qingdao)
Yuefeng Liu Gong Zhang Chenrong Zhang Yuhui Yang Lina Zhang
4677822
A Case for the Use of Data driven Methods in Gas Turbine Prognostics
The goal of data driven methods is to remove dependence on classical models of structured expert judgment and draw insights to causal relationships directly from the data. This paper investigates the potential of using data driven methods, namely uni variate multiple linear regression, k nearest neighbors, feed forward neural networks, random forests and linear support vector regression to predict the end of life (EOL) and remaining useful life (RUL) of engineering systems. The algorithms are demonstrated on a real world largescale dataset consisting of a multidimensional time series of health monitoring indicators collected from a set of commercial aircraft gas turbine engines. A stratified version of 10 fold cross validation is used to compare the prognostics performance of the five prognostics models. An experiencebased Weibull model is chosen as the baseline method. Models are evaluated according to established metrics in the field including median absolute error, median absolute deviation and relative accuracy. The prediction results indicate that support vector regression and random forests are the most accurate models. Neural networks and k nearest neighbors also show improved forecast skill compared to the baseline model while beating the more traditional technique of linear regression. In regards to error spread, results are not as expressive even though all the selected data driven methods provide good results, outperforming the baseline.
Marcia Lourenco Baptista Cairo L Nascimento Helmut Prendinger Elsa M P Henriques
222222946
Data driven RUL Prediction of High speed Railway Traction System Based on Similarity of Degradation Feature
The remaining useful life (RUL) prediction of high speed railway traction system is of great significance for ensuring the safe and efficient driving of high speed railway trains. Due to the complex structure of high speed railway traction system, it is difficult to reveal system level degradation mechanism; thus, a data driven RUL prediction method based on similarity of degradation features is proposed in this paper. The seq2seq structure of the Long Short Term Memory (LSTM) is adopted to extract the multivariate features of the degradation trajectory. Based on these features, a similarity based RUL prediction method is utilized to compute the RUL of the system. Experiments are conducted on the semi physical platform of the CRH2 traction system. Results can show that the proposed method can extract reasonable degradation features; and the prediction accuracy is greatly improved compared with several existing methods.
2019 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS)
Kaiqiang Zhu Chuanyu Zhang Ningyun Lu Bin Jiang
211243957
A Comparative Study on the Data driven Based Prognostic Approaches for RUL of Rolling Bearings
With the condition monitoring equipment becoming more sophisticated, data driven based prognostic approaches for remaining useful life (RUL) are emerging. This paper introduces three classical prognostic approaches and verifies the effectiveness through the whole life cycle experimental data of degenerated rolling bearings. The result shows that the prediction of the methods based on probability statistics will be greatly affected, if the prior parameters are inaccurate. And the degradation model cannot be adapted to individual bearing accurately. The prognostic method based on artificial intelligence and condition monitoring is more accurate in the case of a small number of training samples, and it will output a remaining useful life prediction interval with higher reliability. Therefore, by combining with other models, improving the intelligent algorithm to enhance the accuracy of its RUL prediction is the key to solve the problem about online prognostic.
2019 IEEE Symposium Series on Computational Intelligence (SSCI)
Xiaojie Zhai Xiukun Wei Jihong Yang
110184494
Health diagnosis and remaining useful life prognostics of lithium ion batteries using data driven methods
Abstract The accurate estimation of state of health (SOH) and a reliable prediction of the remaining useful life (RUL) of Lithium ion (Li ion) batteries in hybrid and electrical vehicles are indispensable for safe and lifetime optimized operation. The SOH is indicated by internal battery parameters like the actual capacity value. Furthermore, this value changes within the battery lifetime, so it has to be monitored on board the vehicle. In this contribution, a new data driven approach for embedding diagnosis and prognostics of battery health in alternative power trains is proposed. For the estimation of SOH and RUL, the support vector machine (SVM) as a well known machine learning method is used. As the estimation of SOH and RUL is highly influenced by environmental and load conditions, the SVM is combined with a new method for training and testing data processing based on load collectives. For this approach, an intensive measurement investigation was carried out on Li ion power cells aged to different degrees ensuring a large amount of data.
A Nuhic Tarik Terzimehic T Soczka-Guth Michael Buchholz Klaus C J Dietmayer
37399926
Prognostics of lithium ion batteries using model based and data driven methods
This paper presents an integrated approach to predict remaining useful life (RUL) of lithium ion batteries using model based and data driven methods. An empirical model is adopted to emulate the battery degradation trend; real time measurements are employed to update the model. In order to better deal with prognostics uncertainties arising from many sources in the prediction such as battery unit to unit variations, an online model update scheme is proposed in a particle filtering based framework. Filtered data within a moving window are used to adjust the model's parameter values in a real time manner based on nonlinear least squares optimization. The proposed approach is studied via experimental data, and the results are discussed.
Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM 2012 Beijing)
Chaochao Chen Michael G Pecht
155108882
Data Driven Remaining Useful Life Prediction Considering Sensor Anomaly Detection and Data Recovery
Prognostics and health management (PHM) is being adopted more and more in the modern engineering systems. As one of the most important technologies in the PHM domain, remaining useful life (RUL) prediction has attracted much attention from the researchers in the scholar and industrial field. Although many methods have been proposed to improve the prediction result, the problem of sensor anomaly detection and data recovery has not been considered together. To achieve this object, the data driven RUL prediction framework considering sensor anomaly detection and data recovery is proposed, which is expected to improve the performance of RUL prediction caused by sensor anomaly. The selected sensor data are first detected to decide whether they are anomalous. If the data of this selected sensor are normal, they are continuously adopted as the input of the RUL prediction algorithm. But, if the data are anomalous, they will be recovered by the related algorithm. The recovered data will be utilized as the input of the RUL prediction algorithm. In the proposed framework, mutual information, Kernel principal component analysis (KPCA) least square support vector machine (LS SVM) and Gaussian process regression (GPR) are utilized. Both simulation data and practical data are used to evaluate the performance of the proposed method. Compared with abandoning the anomalous sensor data, the recovered data can indeed help to enhance the RUL prediction result.
IEEE Access
Liansheng Liu Qing Guo Datong Liu Yu Peng
25045775
A data driven prognostics approach for RUL based on principle component and instance learning
The research of Remaining Useful Life (RUL) estimation is one of the most common tasks of Prognostics and Health Management (PHM) This paper presents a data driven approach for estimating RUL using principle component and instance learning. The approach is especially suitable for situations in which abundant run to failure (RtF) data are available. Firstly, the principal component analysis (PCA) is used to find the low dimensional principal components (PCs) from the statistical features of the measured signals. Then, the health indicators (HI) can be obtained by using weighted Euclid distance (WED) and regressed by the data driven methods or model based methods. Finally, the method based on instance learning is employed to estimate the RUL of the machine under operation. The performance of the prognostics approach introduced in this paper is demonstrated by using turbofan engine degradation simulation data set, which is supplied by NASA Ames.
2016 IEEE International Conference on Prognostics and Health Management (ICPHM)
Liu Yongxiang Shi Jianming Wang Gong Liu Xiaodong
4692777
Prediction Capability Assessment of Data Driven Prognostic Methods for Railway Applications
In the development of Prognostics and Health Management (PHM) for industrial applications, the question of which predictive method to use is fundamental. The choice is typically driven by the data and/or the physics based models available, and the cost benefit considerations related to PHM implementation, wherein prediction capability plays an important role. By prediction capability of a prognostic method we refer to its ability to provide trustable predictions of the Remaining Useful Life (RUL) of a component or system, with the characteristics required by the given application. A set of Prognostic Performance Indicators (PPIs) is used to guide the choice of the method to be implemented. These PPIs measure different characteristics of a prognostic method and need to be aggregated to enable a final choice of prognostic method, based on its overall performance. We propose an aggregation strategy to identify the prognostic method with the best compromise performance on all PPIs. The strategy is exemplified on a case study with real data taken from industry, whose structure is general and, therefore, applicable to railway industry.
Francesco Di Maio Pietro Turati Enrico Zio
212909899
Comparison of Data driven Prognostics Models: A Process Perspective
Remaining useful life (RUL) prediction is crucial for the implementation of Prognostics and Health Management (PHM) systems, enabling application of predictive maintenance strategies for critical systems (e.g. in aviation, power, railway) Existing literature addresses aspects of data driven prognostic approaches, with a predominant focus on introducing and testing various novel prediction techniques which are purposed towards improving prediction accuracy performance. However, a relative lack of research can be identified when considering a comparative evaluation of competing for data driven approaches. In particular, the contributing process elements and characteristics of data driven prognostics methods are typically not compared in detail. To overcome these drawbacks, this paper aims to evaluate the underlying technical processes for statistical and artificial neural networks (ANN) methods for prognostics. A case study is conducted to implement both approaches on the PHM08 Challenge Data Set for comparison. This research comprehensively compares the statistical and ANN prognostic methods in a systematic manner, covering and comparing their respective technical processes, and evaluates the results with respect to prediction accuracy
Rui Li Wim J C Verhagen Richard Curran