Machinery health prognostics: A systematic review from data acquisition to RUL prediction

Lei, Yaguo; Li, Naipeng; Guo, Liang; Li, Ningbo; Yan, Tao; Lin, Jing

doi:10.1016/j.ymssp.2017.11.016

Cited by 1,768 publications

(956 citation statements)

References 300 publications

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“…However, the traditional fault diagnosis features, based on frequency domain analysis, did not work efficiently, because the vibration of bearings was nonstationary and nonlinear. Additionally, the frequency resolution was too low for detailed analysis [7]. In the experiment, 17 bearings were tested under three different operating conditions (i.e., 1800 rpm and 4000 N, 1650 rpm and 4200 N, and 1500 rpm and 5000 N), as shown in Table 1.…”

Section: Extraction Of Time-frequency Image Featuresmentioning

confidence: 99%

“…However, the traditional fault diagnosis features, based on frequency domain analysis, did not work efficiently, because the vibration of bearings was nonstationary and nonlinear. Additionally, the frequency resolution was too low for detailed analysis [7]. Appl In this study, we used the Morlet-based CWT to extract image features from the raw vibration signal.…”

Section: Extraction Of Time-frequency Image Featuresmentioning

confidence: 99%

“…The general framework of data-driven prognostics methods comprises three steps: data acquisition, HI construction, and RUL prediction [7]. The performance of RUL prediction relies on the HI construction step.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Extraction Of Time-frequency Image Featuresmentioning

confidence: 99%

“…However, the traditional fault diagnosis features, based on frequency domain analysis, did not work efficiently, because the vibration of bearings was nonstationary and nonlinear. Additionally, the frequency resolution was too low for detailed analysis [7]. Appl In this study, we used the Morlet-based CWT to extract image features from the raw vibration signal.…”

Section: Extraction Of Time-frequency Image Featuresmentioning

confidence: 99%

See 1 more Smart Citation

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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“…A good review of statistical‐driven approaches for estimating the RUL is provided in Si et al The empirical mode decomposition in the fault diagnosis of a rotating machinery is reviewed in Lei et al Prognostics and health management (PHM) tools for critical machinery components are found in Lee et al Numerous methods of intelligent fault diagnosis and RUL prediction of a rotating machinery are discussed in Lei . Four key processes including acquisition, health indicator (HI) construction, health stage (HS) division, and RUL prediction should be included in a machinery prognostic program . Lei et al also reviewed 274 published papers in 2005 to 2017 for the RUL prediction, and these methods are identified as three categories: physics model‐based approaches, data‐driven approaches including statistical learning, and artificial intelligence (AI) such as machine learning models and deep learning models, and hybrid approaches that combine different approaches.…”

Section: Introductionmentioning

confidence: 99%

Hybrid approach for remaining useful life prediction of ball bearings

Wang

Mamo

2019

Quality & Reliability Eng

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Remaining useful life (RUL) prediction plays an important role in predictive maintenance systems to support decision‐makers for arranging maintenance tasks and related resources. We propose a hybrid approach that is combined an exponential weighted moving average (EWMA) control chart for anomaly detection and machine learning models such as support vector regression (SVR) and random forest regression (RFR) with differential evolution (DE) algorithm to predict the RULs of ball bearings. Here, DE algorithm is used to find the optimal hyperparameters of SVR model. The datasets of ball bearings from the Prognostics Data Repository of NASA are used to compare the prediction performance of different methods. The degradation behavior of training data from the anomaly time to the end of life is used to transfer learning for the testing data in the SVR and RFR models. The results indicate that the proposed methods outperform the other four existing methods in terms of score. Therefore, the proposed hybrid approach is a reliable tool for the RUL prediction of ball bearings.

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“…The model-based approaches can be generally divided into two categories, physics model-based methods and statistical model-based methods [11]. Physics model-based methods construct the models based on the failure mechanisms of equipment [12].…”

Section: Introductionmentioning

confidence: 99%

Bearing Remaining Useful Life Prediction Based on a Nonlinear Wiener Process Model

Wen

Gao

Zhang

2018

Shock and Vibration

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Prognostic is an essential part of condition-based maintenance, which can be employed to enhance the reliability and availability and reduce the maintenance cost of mechanical systems. This paper develops an improved remaining useful life (RUL) prediction method for bearings based on a nonlinear Wiener process model. First, the service life of bearings is divided into two stages in terms of the working condition. Then a new prognostic model is constructed to reflect the relationship between time and bearing health status. Besides, a variety of factors that cause uncertainties toward the degradation path are considered and appropriately managed to obtain reliable RUL prediction results. The particle filtering is utilized to estimate the degradation state, qualify the uncertainties, and predict the RUL. The experimental studies show that the proposed method has a better performance in RUL prediction and uncertainty management than the exponential model and the linear model.

show abstract

Machinery health prognostics: A systematic review from data acquisition to RUL prediction

Cited by 1,768 publications

References 300 publications

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

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

Hybrid approach for remaining useful life prediction of ball bearings

Bearing Remaining Useful Life Prediction Based on a Nonlinear Wiener Process Model

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