Abstract
Prognostics and health management (PHM) is a framework to identify damage prior to its occurrence which leads to the reduction of both maintenance costs and safety hazards. Based on the data collected in condition monitoring, the degradation of the part is predicted. Studies show that most failures are caused by faults in rolling element bearing, which highlights that a bearing is one of the most important mechanical components of any machine. Thus, it becomes important to monitor bearing degradation to make sure that it is utilized properly. Generally, machine learning (ML) or deep learning (DL) techniques are utilized to predict bearing degradation using a data-driven approach, where signals are captured from the machine. There should be a large amount of data to apply either ML or DL techniques, but it is difficult to collect that amount of data directly from any machine. In this study, health assessment is carried out using the correlation coefficient to divide the bearing life into two degradation stages. The raw signal is processed using discrete wavelet transform (DWT), where mutual information (MI) is used to rank and select the base wavelet, after which tabular generative adversarial networks (TGAN) are used to generate the artificial coefficients. Statistical features are calculated from the real data (DWT coefficients) and the artificial data (generated from TGAN). The constructed feature vector is then used as an input to train machine learning models, namely ensemble bagged tree (EBT) and Gaussian process regression with the squared exponential kernel function (SEGPR), to estimate bearing degradation conditions. Both the machine learning models were validated on the publicly available experimental data of FEMTO bearing. Obtained results showed that the developed EBT and SEGPR models accurately predicted the bearing degradation conditions with the average lowest RMSE value of 0.0045 and MAE value of 0.0037.
Highlights
In the past decade, prognostics and health management (PHM) methods have received wide attention due to an increase in prediction accuracy, which results in a reduction of maintenance costs [1,2]
In the present study, bearing degradation is divided into two stages using correlation coefficients, and discrete wavelet transform is applied to raw vibration signals to calculate statistical features
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Summary
Prognostics and health management (PHM) methods have received wide attention due to an increase in prediction accuracy, which results in a reduction of maintenance costs [1,2]. Wentao Mao et al [18] proposed an LSTM model on time–frequency images generated using the Hilbert–Huang transform for predicting bearing degradation. Biao Wang et al [20] proposed a hybrid prognostic approach where degradation data are sparsely presented using relevance vector machine regression and bearing degradation is estimated using the exponential degradation model. [22] implemented a non-linear regression model for analyzing the condition of rolling bearings Testing results of their used model showed a prediction error within the limits. In the present study, bearing degradation is divided into two stages using correlation coefficients, and discrete wavelet transform is applied to raw vibration signals to calculate statistical features.
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