In the mechatronic system, rolling bearing is a frequently used mechanical part, and its failure may result in serious accident and major economic loss. Therefore, the remaining useful life (RUL) prediction of rolling bearing is greatly indispensable. To accurately predict the RUL of the rolling bearing, a new kind of gated recurrent unit neural network with dual attention gates, namely, gated dual attention unit (GDAU), is proposed. With the acquired life-cycle vibration data of a rolling bearing, a series of root mean squares at different time instants are calculated as the health indicator (HI) vector. Next, the to-be HI sequence is predicted by GDAU according to the existing HI vector, and then the RUL of the rolling bearing is estimated. The experimental results show that the proposed GDAU can effectively predict the RULs of rolling bearings, and it has higher prediction accuracy and convergence speed than the conventional prediction methods.

Gated Dual Attention Unit Neural Networks for Remaining Useful Life Prediction of Rolling Bearings

Multicellular LSTM-based deep learning model for aero-engine remaining useful life prediction

Optimization of VMD using kernel-based mutual information for the extraction of weak features to detect bearing defects

As one of the most important components of machinery, once the bearing has a failure, serious catastrophe may happen. Hence, for avoiding the catastrophe, it is valuable to predict the remaining useful life (RUL) of bearing. Health indicators (HIs) construction plays a greatly important role in the data-driven RUL prediction. Unfortunately, most of the existing HIs construction methods need prior knowledge and few of them construct HIs from raw vibration signals. For dealing with the above issues, a novel quadratic function-based deep convolutional auto-encoder is developed in this work. The raw bearing vibration signals are first preprocessed by low-pass filtering. Then the cleaned vibration signals are input into the quadratic function-based DCAE neural networks for constructing HIs of bearings. Compared with AE, DNN, KPCA, ISOMAP, PCA and VAE, it is revealed that the proposed methodology can construct a better HI from the raw bearing vibration signal in terms of comprehensive performance. Several comparative experiments have been implemented, and the results indicate that the HI constructed by quadratic function-based DCAE neural network has stronger predictive power than the traditional data-driven HIs.

Health indicator construction by quadratic function-based deep convolutional auto-encoder and its application into bearing RUL prediction.

An inconsistent distribution between training and testing data caused by complicated and changeable machine working conditions hinders wide applications of traditional deep learning for machine fault diagnosis In a target domain, in which labeled samples are not available (testing data), transfer learning can adopt a relevant source domain (training data) to identify the similarity between the two domains and subsequently mitigate the negative effects of a domain shift Previous studies on transfer learning mainly focused on decreasing the marginal distribution distance of two different domains or narrowing the conditional distribution distance even though marginal and conditional distributions provide different contributions to transfer tasks The relative importance of the two distributions is difficult to dynamically and quantitatively assess To align the two distributions (joint distribution) of two different domains, in this article, we propose a dynamic joint distribution alignment network (DJDAN) to evaluate the relative importance of marginal and conditional distributions dynamically and quantitatively Furthermore, compared with common metrics that use pseudo labels to calculate the conditional distribution distance, the proposed DJDAN uses soft pseudo labels to more accurately measure the conditional distribution discrepancy between different domains Extensive experiments reveal the superiority and generalization of the proposed DJDAN for bearing fault diagnosis under different working conditions

Dynamic Joint Distribution Alignment Network for Bearing Fault Diagnosis Under Variable Working Conditions

A fault dynamic model of high-speed angular contact ball bearings

Multiple-degree-of-freedom dynamic model of rolling bearing with a localized surface defect

\n Bearings are vital parts of many mechanical equipment, the vibration signal analysis of bearings with local defects is important in guiding the fault diagnosis. In this paper, a dynamic analysis method is proposed to investigate the vibration response of the deep groove ball bearings (DGBBs) with local defect using a new displacement excitation function based on the Hertz contact theory and Newton's second law. The DGBB is modeled as a two degrees-of-freedom system, and an additional friction force in the defect zone, the influence of centrifugal force, the gravity of rolling elements, and lubrication traction/slip force between rolling elements and raceway are considered. And this model is used to study the dynamic signals of DGBB under different fault sizes and rotation speeds. Results indicate that the simulation signal has many continuous impacts and change over the time which is closer to the actual situation compared with the one-shot impulse function such as rectangular or half-sine or piecewise function when the rolling elements passed through the defect zone. Finally, the validity of the proposed model is verified by experiments. The simulated and experimental results indicate that the proposed model would achieve a more appropriate and accurate dynamic simulation.

Chengcheng Li

Papers

A fault dynamic model of high-speed angular contact ball bearings

Multiple-degree-of-freedom dynamic model of rolling bearing with a localized surface defect

Vibration Analysis of Deep Groove Ball Bearings With Local Defect Using a New Displacement Excitation Function