Zobrazeno 1 - 10
of 73
pro vyhledávání: '"Ming J. Zuo"'
Publikováno v:
IEEE/ASME Transactions on Mechatronics. :1-11
Publikováno v:
IEEE Transactions on Reliability. 71:1567-1576
In this article, we propose an active kriging-based learning method for hybrid reliability analysis (HRA) with random and interval variables. An improved sampling strategy is proposed to target the sampling areas. Samples with maximum responses great
Publikováno v:
Measurement. 217:113016
Publikováno v:
IEEE Transactions on Industrial Electronics. 68:8777-8788
In this study, a novel time–frequency (TF) analysis method, referred to as the scaling-basis chirplet transform (SBCT), is developed by extending the conventional chirplet transform. This method includes a replacement kernel function that can vary
Publikováno v:
IEEE Transactions on Reliability. 70:2-12
As an extension of binary system model, multistate systems (MSSs) are more flexible for modeling reliabilities of real-life engineering systems. In the conventional MSS theory, it is usually assumed that the performance of the system and components c
Publikováno v:
IEEE Transactions on Industrial Informatics. 16:4949-4960
The critical issue for fault diagnosis of wheel-set bearings in high-speed trains is to extract fault features from vibration signals. To handle high complexity, strong coupling, and low signal-to-noise ratio of the vibration signals, this article pr
Publikováno v:
IEEE Transactions on Instrumentation and Measurement. 69:1594-1608
For the data-driven remaining useful life (RUL) prediction for rolling bearings, the traditional machine learning-based methods generally provide insufficient feature representation and adaptive extraction. Although deep learning-based RUL prediction
Publikováno v:
IEEE Sensors Journal. 20:2659-2670
Prognostics of bearing degeneration play a crucial role in implementation of systems maintenance strategies. Although numerous classical prognostic models have been developed, these models are carried out based on channel-wise processing and therefor
Publikováno v:
IEEE Transactions on Reliability. :1-17
Accurately predicting the degradation trend of a pivotal component is essential for scheduling maintenance decisions, avoiding abrupt shutdowns of mechanical equipment, and improving overall systems reliability. This article proposes a hybrid data-dr
Publikováno v:
IEEE Transactions on Reliability. :1-13
In a big data environment, the data from long-term monitoring are of great importance in intelligent diagnoses, prognostic and health management of industrial components. However, in practice, it is difficult to ensure high reconstructive accuracy ma