Bearing remnant operational life can be determined by implementing a data-driven prognostics method. In this work, the bearing run-to-failure data from experimentation on test rig is used to extract time-domain features. The sudden change in time domain information signifies the fault inception which led to failure stage promptly. The monotonicity metric is utilized to select the optimal feature set that best represents bearing degradation. PCA (principal component analysis) is employed for dimension reduction and fusion, and a uni-dimensional health indicator (HI) is constructed. Fluctuations of HI are smoothed by fitting it with a Weibull failure rate function (WFRF) and the corresponding parameters are estimated using non-linear least squares method. By inverting the model, the predicted time values are calculated, and hence remnant operational life of bearing is evaluated and compared with the actual life from experimental data. The performance assessment metrics utilized are mean absolute percentage error (MAPE), mean squared error (MSE), root mean squared error (RMSE), and bias. Besides this, an online degradation state classification method using the k-nearest neighbor (KNN) classifier is implemented. The KNN model performance is assessed by constructing ROC (receiver operating characteristics) curve, which indicates the value of AUC (area under the curve) equal to 0.94, representing high accuracy of the KNN. The RUL (remaining useful life) is predicted within 95% confidence limits, and the predicted RUL almost follows the actual one with some fluctuations. The model performance is found promising and can be implemented to evaluate the remaining useful life of bearing.
Accurate estimation of remaining useful life (RUL) becomes a crucial task when bearing operates under dynamic working conditions. The environmental noise, different operating conditions, and multiple fault modes result in the existence of considerable distribution and feature shifts between different domains. To address these issues, a novel framework TSBiLSTM is proposed that utilizes 1DCNN, SBiLSTM, and AM synergically to extract highly abstract feature representation, and domain adaptation is realized using the MK-MMD (multi-kernel maximum mean discrepancy) metric and domain confusion layer. One-dimensional CNN (1DCNN) and stacked bi-directional LSTM (SBiLSTM) are utilized to take advantage of spatio-temporal features with attention mechanism (AM) to selectively process the influential degradation information. MK-MMD provides effective kernel selection along with a domain confusion layer to effectively extract domain invariant features. Both experimentation and comparison studies are conducted to verify the effectiveness and feasibility of the proposed TSBiLSTM model. The generalized performance is demonstrated using IEEE PHM datasets based on RMSE, MAE, absolute percent mean error, and percentage mean error. The promising RUL prediction results validate the superiority and usability of the proposed TSBiLSTM model as a promising prognostic tool for dynamic operating conditions.
In this paper, vibration-based fault diagnostics and response classification have been done for defective high-speed cylindrical bearing operating under unbalance rotor conditions. An experimental study has been performed to capture the vibration signature of faulty bearings in the time domain and for different speeds of the unbalanced rotor. Two-dimensional phase trajectories are generated by estimating the time delay and embedding dimension corresponding to vibration signatures. Qualitative analysis involves the implementation of a Deep Convolutional Neural Network (DCNN) utilizing the phase portraits as input to classify the nonlinear vibration responses. Comparison with state-of-art classifiers such as ANN, DNN, and KNN is presented based on classification accuracy values. Thus, the values obtained are 61.12%, 66.62%, 71.85%, and 98.85% for ANN, DNN, KNN, and DCNN, respectively. Hence, the proposed intelligent classification model accurately identifies the dynamic behavior of bearing under unbalanced rotor conditions.
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