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

Entropy ◽  
2019 ◽  
Vol 21 (9) ◽  
pp. 861
Author(s):  
Shenghan Zhou ◽  
Xingxing Xu ◽  
Yiyong Xiao ◽  
Wenbing Chang ◽  
Silin Qian ◽  
...  
Keyword(s):  
Dynamic Programming Algorithm ◽  
Indicator System ◽  
Remaining Useful Life ◽  
Programming Algorithm ◽  
Percentage Error ◽  
Support Vector ◽  
Electrical Machine ◽  
Diesel Generator ◽  
Vibration Signals ◽  
Useful Life

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.

scholarly journals A Hybrid Prognostics Deep Learning Model for Remaining Useful Life Prediction

Electronics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 39
Author(s):  
Zhiyuan Xie ◽  
Shichang Du ◽  
Jun Lv ◽  
Yafei Deng ◽  
Shiyao Jia
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Model ◽  
Recurrent Network ◽  
Remaining Useful Life ◽  
Support Vector ◽  
Second Phase ◽  
Learning Methods ◽  
Useful Life ◽  
Deep Learning Model

Remaining Useful Life (RUL) prediction is significant in indicating the health status of the sophisticated equipment, and it requires historical data because of its complexity. The number and complexity of such environmental parameters as vibration and temperature can cause non-linear states of data, making prediction tremendously difficult. Conventional machine learning models such as support vector machine (SVM), random forest, and back propagation neural network (BPNN), however, have limited capacity to predict accurately. In this paper, a two-phase deep-learning-model attention-convolutional forget-gate recurrent network (AM-ConvFGRNET) for RUL prediction is proposed. The first phase, forget-gate convolutional recurrent network (ConvFGRNET) is proposed based on a one-dimensional analog long short-term memory (LSTM), which removes all the gates except the forget gate and uses chrono-initialized biases. The second phase is the attention mechanism, which ensures the model to extract more specific features for generating an output, compensating the drawbacks of the FGRNET that it is a black box model and improving the interpretability. The performance and effectiveness of AM-ConvFGRNET for RUL prediction is validated by comparing it with other machine learning methods and deep learning methods on the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset and a dataset of ball screw experiment.

scholarly journals A Bayesian least-squares support vector machine method for predicting the remaining useful life of a microwave component

2017 ◽  
Vol 9 (1) ◽  
pp. 168781401668596 ◽  
Author(s):  
Fuqiang Sun ◽  
Xiaoyang Li ◽  
Haitao Liao ◽  
Xiankun Zhang
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Remaining Useful Life ◽  
Lifetime Prediction ◽  
Support Vector ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
Microwave Component ◽  
Useful Life ◽  
Bayesian Least Squares

Rapid and accurate lifetime prediction of critical components in a system is important to maintaining the system’s reliable operation. To this end, many lifetime prediction methods have been developed to handle various failure-related data collected in different situations. Among these methods, machine learning and Bayesian updating are the most popular ones. In this article, a Bayesian least-squares support vector machine method that combines least-squares support vector machine with Bayesian inference is developed for predicting the remaining useful life of a microwave component. A degradation model describing the change in the component’s power gain over time is developed, and the point and interval remaining useful life estimates are obtained considering a predefined failure threshold. In our case study, the radial basis function neural network approach is also implemented for comparison purposes. The results indicate that the Bayesian least-squares support vector machine method is more precise and stable in predicting the remaining useful life of this type of components.

scholarly journals Remaining Useful Life Prediction for Rotating Machinery Based on Optimal Degradation Indicator

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Aisong Qin ◽  
Qinghua Zhang ◽  
Qin Hu ◽  
Guoxi Sun ◽  
Jun He ◽  
...  
Keyword(s):  
Health Management ◽  
Rotating Machinery ◽  
Remaining Useful Life ◽  
Programming Algorithm ◽  
Wiener Model ◽  
New Approach ◽  
Degradation Modeling ◽  
Proposed Model ◽  
Useful Life ◽  
Degradation Indicator

Remaining useful life (RUL) prediction can provide early warnings of failure and has become a key component in the prognostics and health management of systems. Among the existing methods for RUL prediction, the Wiener-process-based method has attracted great attention owing to its favorable properties and flexibility in degradation modeling. However, shortcomings exist in methods of this type; for example, the degradation indicator and the first predicting time (FPT) are selected subjectively, which reduces the prediction accuracy. Toward this end, this paper proposes a new approach for predicting the RUL of rotating machinery based on an optimal degradation indictor. First, a genetic programming algorithm is proposed to construct an optimal degradation indicator using the concept of FPT. Then, a Wiener model based on the obtained optimal degradation indicator is proposed, in which the sensitivities of the dimensionless parameters are utilized to determine the FPT. Finally, the expectation of the predicted RUL is calculated based on the proposed model, and the estimated mean degradation path is explicitly derived. To demonstrate the validity of this model, several experiments on RUL prediction are conducted on rotating machinery. The experimental results indicate that the method can effectively improve the accuracy of RUL prediction.

scholarly journals A Hybrid Remaining Useful Life Prediction Method for Cutting Tool Considering the Wear State

2022 ◽  
Author(s):  
Yifan Li ◽  
Yongyong Xiang ◽  
Baisong Pan ◽  
Luojie Shi
Keyword(s):  
Tool Wear ◽  
High Speed ◽  
Cutting Tool ◽  
Bayesian Framework ◽  
Remaining Useful Life ◽  
Physical Models ◽  
Data Driven ◽  
Support Vector ◽  
Wear Model ◽  
Useful Life

Abstract Accurate cutting tool remaining useful life (RUL) prediction is of significance to guarantee the cutting quality and minimize the production cost. Recently, physics-based and data-driven methods have been widely used in the tool RUL prediction. The physics-based approaches may not accurately describe the time-varying wear process due to a lack of knowledge for underlying physics and simplifications involved in physical models, while the data-driven methods may be easily affected by the quantity and quality of data. To overcome the drawbacks of these two approaches, a hybrid prognostics framework considering tool wear state is developed to achieve an accurate prediction. Firstly, the mapping relationship between the sensor signal and tool wear is established by support vector regression (SVR). Then, the tool wear statuses are recognized by support vector machine (SVM) and the results are put into a Bayesian framework as prior information. Thirdly, based on the constructed Bayesian framework, parameters of the tool wear model are updated iteratively by the sliding time window and particle filter algorithm. Finally, the tool wear state space and RUL can be predicted accordingly using the updating tool wear model. The validity of the proposed method is demonstrated by a high-speed machine tool experiment. The results show that the presented approach can effectively reduce the uncertainty of tool wear state estimation and improve the accuracy of RUL prediction.

scholarly journals Development of a Novel Methodology for Remaining Useful Life Prediction of Industrial Slurry Pumps in the Absence of Run to Failure Data

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8420
Author(s):  
Muhammad Mohsin Khan ◽  
Peter W. Tse ◽  
Amy J.C. Trappey
Keyword(s):  
Life Prediction ◽  
Prediction Models ◽  
Remaining Useful Life ◽  
Condition Based Maintenance ◽  
Vibration Signals ◽  
The Core ◽  
Failure Data ◽  
Periodic Maintenance ◽  
Useful Life ◽  
Nonlinear Autoregressive

Smart remaining useful life (RUL) prognosis methods for condition-based maintenance (CBM) of engineering equipment are getting high popularity nowadays. Current RUL prediction models in the literature are developed with an ideal database, i.e., a combination of a huge “run to failure” and “run to prior failure” data. However, in real-world, run to failure data for rotary machines is difficult to exist since periodic maintenance is continuously practiced to the running machines in industry, to save any production downtime. In such a situation, the maintenance staff only have run to prior failure data of an in operation machine for implementing CBM. In this study, a unique strategy for the RUL prediction of two identical and in-process slurry pumps, having only real-time run to prior failure data, is proposed. The obtained vibration signals from slurry pumps were utilized for generating degradation trends while a hybrid nonlinear autoregressive (NAR)-LSTM-BiLSTM model was developed for RUL prediction. The core of the developed strategy was the usage of the NAR prediction results as the “path to be followed” for the designed LSTM-BiLSTM model. The proposed methodology was also applied on publically available NASA’s C-MAPSS dataset for validating its applicability, and in return, satisfactory results were achieved.

Practical health indicator construction methodology for bearing ensemble remaining useful life prediction with ISOMAP-DE and ELM-WPHM

2021 ◽  
Author(s):  
Yingkui Gu ◽  
Qingpeng Bi ◽  
Guangqi Qiu
Keyword(s):  
Prediction Model ◽  
Weight Coefficient ◽  
Remaining Useful Life ◽  
Support Vector ◽  
Health Indicator ◽  
Training Time ◽  
Optimal Weight ◽  
Useful Life ◽  
Learning Machine ◽  
Health Features

Abstract To improve the accuracy of our previous bearing ensemble Remaining Useful Life (RUL) prediction model using the Genetic Algorithm (GA), Support Vector Regression (SVR), and the Weibull Proportional Hazard Model (WPHM) (see reference [1]), we proposed a more practical Health Indicator (HI) construction methodology for bearing ensemble RUL prediction. A weighted coefficient determination method for four prognostic metrics-monotonicity, robustness, trendability, and consistency-was proposed to select sensitive health features accurately using the Analytic Hierarchy Process (AHP). The selected sensitive health features were fused through isometric feature mapping (ISOMAP), and Differential Evolution (DE) was employed to replace GA for computing the optimal weight coefficients of each input fused feature. One-dimensional HI was constructed by multiplying each input fused feature with the corresponding optimal weight coefficient, and RUL prediction was implemented through an extreme learning machine (ELM) and WPHM. The accuracy and effectiveness of the proposed method were validated by a bearing experiment. The results show that HI construction with ISOMAP-DE has achieved the best performance, and the proposed ELM-WPHM model is compared with BP-WPHM, SVM-WPHM, LSTM-WPHM, and DLSTM-WPHM in terms of RMSE criteria. The minimum error and training time appear in ELM-WPHM, indicating the superiority of the proposed bearing ensemble RUL prediction model.

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