Bearing remaining useful life estimation based on time–frequency representation and supervised dimensionality reduction

Zhao, Minghang; Tang, Baoping; Tan, Qian

doi:10.1016/j.measurement.2015.11.047

Cited by 94 publications

(41 citation statements)

References 35 publications

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“…For example, a novel hierarchical diagnosis network that can collect deep belief networks (DBNs) by layer was proposed for the hierarchical identification of mechanical systems . However, although have seen great developments in both theoretical research and practical applications, statistical methods and artificial intelligence still require a large amount of data for training the mathematical models, and the evaluation results are very sensitive to the parameters of the model …”

Section: Introductionmentioning

confidence: 99%

Retracted: Manifold learning‐based fuzzy k‐principal curve similarity evaluation for wind turbine condition monitoring

Yuan

Yang

Kang

et al. 2018

Energy Science & Engineering

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As wind energy is experiencing an unprecedented development in today's world, the condition monitoring of wind turbine systems which can avoid serious accidents and economic losses are gathering more and more attentions. Considering the evaluation methods based on machine learning are complicated and unstable in terms of model training and parameter selection, this paper proposed a novel assessment algorithm based on similarity analysis of fuzzy k‐principal curves (FKPCs) in manifold space. Initially, 38 fusion features containing time‐domain, frequency‐domain, and wavelet node energy features are extracted from the vibration signal of the wind turbine bearing. Then, the nonlinear local algorithm Laplacian eigenmaps was introduced to transform the original features to the projected lower dimensional space and obtain the more typical parameters. Combining fuzzy clustering with the k‐principal curve creatively, smooth principal curves of the feature sets were then extracted according to point distribution in three‐dimensional manifold space. Finally, the Hausdorff distance algorithm was employed to calculate the similarity between the principal curves of healthy and test datasets to assess the running condition of wind turbine. To validate the present method, the experiments of accelerated bearing degradation were carried out. A series of comprehensive and persuasive comparisons and analysis with different feature conversion methods (principal component analysis [PCA] and Isomap), different distance analysis algorithms (Euclidean distance and Mahalanobis distance), and different assessment models (hidden Markov model [HMM] and deep belief network [DBN]) verified that the proposed FKPC technique can monitor the operating status of the machine more accurately without complex model training and parameter selection processes.

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Section: Introductionmentioning

confidence: 99%

Retracted: Manifold learning‐based fuzzy k‐principal curve similarity evaluation for wind turbine condition monitoring

Yuan

Yang

Kang

et al. 2018

Energy Science & Engineering

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“…. , n k (12) where t is the observation number and n k is the total number of observations for turbine k. These polynomials have been represented previously as red lines in Figure 3. The observed data are substituted by the fitted data from here and will be calledx k i .…”

Section: Analysis Of Results and Discussionmentioning

confidence: 98%

“…The prerequisite of the benefit from the prognostics is the correct detection of fault by an efficient diagnostic system because diagnostic decision triggers the prognostic system [8]. The predicted time then becomes the remaining useful life (RUL) [9][10][11][12][13], which is an important concept in decision making for contingency mitigation. Therefore, prognostics is explicitly defined as the estimation of the remaining useful life (RUL) of any equipment.…”

Section: Introductionmentioning

confidence: 99%

“…In this way, reliability is theoretically defined as the probability of failure, the frequency of failures, or in terms of availability, a probability derived from reliability and maintainability. Specifically, the concept of remaining useful life (RUL) is defined here as the number of remaining time units that the equipment has before it reaches the limit for a safe operation [7][8][9][10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

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A New Predictive Model Based on the ABC Optimized Multivariate Adaptive Regression Splines Approach for Predicting the Remaining Useful Life in Aircraft Engines

et al. 2016

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Abstract:Remaining useful life (RUL) estimation is considered as one of the most central points in the prognostics and health management (PHM). The present paper describes a nonlinear hybrid ABC-MARS-based model for the prediction of the remaining useful life of aircraft engines. Indeed, it is well-known that an accurate RUL estimation allows failure prevention in a more controllable way so that the effective maintenance can be carried out in appropriate time to correct impending faults. The proposed hybrid model combines multivariate adaptive regression splines (MARS), which have been successfully adopted for regression problems, with the artificial bee colony (ABC) technique. This optimization technique involves parameter setting in the MARS training procedure, which significantly influences the regression accuracy. However, its use in reliability applications has not yet been widely explored. Bearing this in mind, remaining useful life values have been predicted here by using the hybrid ABC-MARS-based model from the remaining measured parameters (input variables) for aircraft engines with success. A correlation coefficient equal to 0.92 was obtained when this hybrid ABC-MARS-based model was applied to experimental data. The agreement of this model with experimental data confirmed its good performance. The main advantage of this predictive model is that it does not require information about the previous operation states of the aircraft engine.

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“…Soualhi et al [19] analyzed vibration signals using the Hilbert-Huang transform and developed a HI using support vector machine and support vector regression (SVR). Zhao et al [20] extracted textual features of time-frequency representation and predicted RUL by transforming high-dimensional features into low-dimensional features using principal component analysis and linear discriminant analysis (LDA). Lei et al [3] proposed the use of the weighted minimum quantization error (WMQE) as a HI, which is a fusion of 10 time domain features, 16 time-frequency domain features, and two features based on trigonometric functions.…”

Section: Introductionmentioning

confidence: 99%

A Novel Image Feature for the Remaining Useful Lifetime Prediction of Bearings Based on Continuous Wavelet Transform and Convolutional Neural Network

Yoo¹,

Baek²

2018

Applied Sciences

137

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Abstract:In data-driven methods for prognostics, the remaining useful lifetime (RUL) is predicted based on the health indicator (HI). The HI detects the condition of equipment or components by monitoring sensor data such as vibration signals. To construct the HI, multiple features are extracted from signals using time domain, frequency domain, and time-frequency domain analyses, and which are then fused. However, the process of selecting and fusing features for the HI is very complex and labor-intensive. We propose a novel time-frequency image feature to construct HI and predict the RUL. To convert the one-dimensional vibration signals to a two-dimensional (2-D) image, the continuous wavelet transform (CWT) extracts the time-frequency image features, i.e., the wavelet power spectrum. Then, the obtained image features are fed into a 2-D convolutional neural network (CNN) to construct the HI. The estimated HI from the proposed model is used for the RUL prediction. The accuracy of the RUL prediction is improved by using the image features. The proposed method compresses the complex process including feature extraction, selection, and fusion into a single algorithm by adopting a deep learning approach. The proposed method is validated using a bearing dataset provided by PRONOSTIA. The results demonstrate that the proposed method is superior to related studies using the same dataset.

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Bearing remaining useful life estimation based on time–frequency representation and supervised dimensionality reduction

Cited by 94 publications

References 35 publications

Retracted: Manifold learning‐based fuzzy k‐principal curve similarity evaluation for wind turbine condition monitoring

Retracted: Manifold learning‐based fuzzy k‐principal curve similarity evaluation for wind turbine condition monitoring

A New Predictive Model Based on the ABC Optimized Multivariate Adaptive Regression Splines Approach for Predicting the Remaining Useful Life in Aircraft Engines

A Novel Image Feature for the Remaining Useful Lifetime Prediction of Bearings Based on Continuous Wavelet Transform and Convolutional Neural Network

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