Rolling bearing prognostic analysis for domain adaptation under different operating conditions

Quality & Reliability Eng

et al. 2023

Rolling bearings, an essential fundamental component in machinery and equipment, have been widely used. Predicting the remaining useful life (RUL) of rolling bearings helps maintain the reliability of mechanical systems. Accurate prediction of RUL requires extracting deep features in complex non-linear vibration signals, the prediction results often vary widely. This paper proposes a RUL prediction method based on convolutional neural network (CNN), bi-directional long-short term memory (BiLSTM), and bootstrap method (CNN-BiLSTM-Bootstrap) to model the forecasting uncertainty. The first step is to extract the first prediction time (FPT) of the degradation phase of rolling bearings using an adaptive method for the 3𝜎 intervals of rolling bearing vibration signal kurtosis. The model extracts the spatio-temporal features through CNN and BiLSTM, and combines the bootstrap method to quantify the RUL prediction interval (PI) of rolling bearings. The comparison with existing models verified the effectiveness and generalization of the proposed model.

Section: Introductionmentioning

confidence: 96%

A CNN‐BiLSTM‐Bootstrap integrated method for remaining useful life prediction of rolling bearings

Guo

Quality & Reliability Eng

et al. 2023

“…Results showed that domain adaptation based on MMD is effective for predicting RUL of defect bearings under different working conditions. In order to improve the robustness of the network, Rathore [28] used multi-kernel MMD (MK-MMD) to realize domain adaptation, and verified the superiority of this method by comparing it with existing methods. In addition, adversarial training-based domain adaptation has also received widespread attention, which consists of three sub-networks, namely, feature extractor, RUL predictor and domain discriminator.…”

Section: Introductionmentioning

confidence: 99%

Rolling Element Bearing Degradation Prediction Using Dynamic Model and an Improved Adversarial Domain Adaptation Approach

Xu,

Jiang,

Yang

et al. 2024

IEEE Access

Rolling element bearing degradation prediction is an important issue in rotating machinery. With the rapid development of artificial intelligence, data-driven bearing degradation prediction has aroused extensive attention. However, current methods rely on whole life cycle data, which is quite difficult to acquire in real industrial scenarios. To solve this problem, a rotor-bearing dynamic model is built to generate simulation signals for a range of spall sizes, and an improved domain adversarial neural network is proposed to transfer degradation knowledge from simulation data to experimental data. To be specific, complete simulation data is used to pre-train a network for learning comprehensive degradation knowledge, and guides the extracted high-level features in the adversarial domain adaptation stage to align with it as an additional optimization item. The proposed approach is verified on bearing degradation datasets under different working conditions, and results show that the proposed approach can successfully predict bearing degradation progress with some early stage experimental data.INDEX TERMS Bearing degradation prediction, domain adversarial neural network, rotor-bearing dynamic model, prognostics and health management.

“…The machine learning method first extracts fault-related features from signals, then adopts a support vector machine [13] and artificial neural network [14] to identify the machine state. The machine learning method is an excellent improvement over the signal analysis approach, but it can only extract shallow features, and it is challenging to extract non-linear features [15,16]. It is difficult for the ordinary external model to achieve the ideal effect on variable working condition data.…”

Section: Introductionmentioning

confidence: 99%

A hybrid intelligent rolling bearing fault diagnosis method combining WKN-BiLSTM and attention mechanism

Guo

et al. 2023

Meas. Sci. Technol.

Fault diagnosis of rolling bearings helps ensure mechanical systems’ safety. The characteristics of temporal and interleaved noise in the bearing fault diagnosis data collected in the industrial field are addressed. This paper proposes a hybrid intelligent fault diagnosis method (WKN-BiLSTM-AM) based on WaveletKernelNetwork (WKN) and bidirectional long-short term memory (BiLSTM) network with attention mechanism (AM). The WKN model is introduced to extract the relevant impact components of defects in the vibration signals, reduce the model training parameters and facilitate the processing of signals containing noise. Then, the fusion of spatial-temporal features is achieved by combining BiLSTM networks to compensate for the lack of individual networks that ignore the dependent information between discontinuous sequences. Finally, the AM module is introduced to improve the feature coding performance of BiLSTM and fault diagnosis accuracy. Comparison and validation between the proposed WKN-BiLSTM-AM method and other state-of-the-art models are given on the Case Western Reserve University (CWRU) and Paderborn University (PU) datasets. The experimental results verify the effectiveness of the proposed model in bearing fault diagnosis, and the model’s generalization capability.