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High Performance Remaining Useful Life Prediction for Gearbox
2018 IEEE International Conference on Prognostics and Health Management (ICPHM), 2018Gearbox failure prediction is important for many applications. We present four approaches for predicting the remaining useful life of gearboxes. The first one is rule-based and the second one is a combination of damage curve approach and rule-based approach.
Bulent Ayhan +2 more
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IEEE Sensors Journal
As mission-critical components in aircraft flap actuation systems, aerospace self-lubricating bearings play a pivotal role in ensuring operational safety during critical flight phases.
Yuheng Wang +5 more
semanticscholar +1 more source
As mission-critical components in aircraft flap actuation systems, aerospace self-lubricating bearings play a pivotal role in ensuring operational safety during critical flight phases.
Yuheng Wang +5 more
semanticscholar +1 more source
IEEE Transactions on Instrumentation and Measurement
Facilitated by artificial intelligence (AI) technologies such as deep learning (DL), predictive maintenance (PdM) has emerged as the most cost-effective maintenance approach for significantly enhancing the safety and availability of industrial equipment.
Jiang-Hong Zhou +3 more
semanticscholar +1 more source
Facilitated by artificial intelligence (AI) technologies such as deep learning (DL), predictive maintenance (PdM) has emerged as the most cost-effective maintenance approach for significantly enhancing the safety and availability of industrial equipment.
Jiang-Hong Zhou +3 more
semanticscholar +1 more source
Federated transfer learning for remaining useful life prediction in prognostics with data privacy
Measurement science and technologyCollaborative model training with multiple clients is becoming an effective solution for prognostic problems, due to the scarcity of the machine run-to-failure data in the real industries. However, direct data sharing and centralized learning are usually
Wei Zhang +3 more
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Adaptive Remaining Useful Life Prediction Algorithm for Bearings
2018 IEEE International Conference on Prognostics and Health Management (ICPHM), 2018It is well known that the lifetime of bearings varies a lot even if the bearings are manufactured in the same assembly line. This is because the exposed operating conditions may be very different for each bearing. As a result, an adaptive remaining lifetime prediction algorithm is a more natural choice and is expected to be more accurate than the non ...
Bulent Ayhan +2 more
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Methodologies for system-level remaining useful life prediction
Reliability Engineering & System Safety, 2016Abstract While most prognostics approaches focus on accurate computation of the degradation rate and the remaining useful life (RUL) of individual components, it is the rate at which the performance of subsystems and systems degrade that is of greater interest to the operators and maintenance personnel of these systems.
Hamed Khorasgani +2 more
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Remaining Useful Life Prediction of Ball Screw Using Precision Indicator
IEEE Transactions on Instrumentation and Measurement, 2021Remaining useful life (RUL) prediction has been receiving increasing attention in industry and academia due to its advantages in ensuring safety, reducing costs, and improving efficiency. For industrial manufacturing, the ball screw is an indispensable high-precision servo component.
Hanbo Yang +6 more
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Predicting Remaining Useful Life with Uncertainty Using Recurrent Neural Process
2020 IEEE 20th International Conference on Software Quality, Reliability and Security Companion (QRS-C), 2020Recently deep learning based remaining useful life (RUL) prediction approaches have gained increasing attention due to their scalability and generalization ability. Although deep learning based approaches can obtain promising point prediction performance, it is not easy for them to estimate the uncertainty in RUL prediction.
Guozhen Gao, Zijun Que, Zhengguo Xu
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IEEE Transactions on Instrumentation and Measurement
In the past few years, deep learning (DL) techniques for predicting remaining useful life (RUL) have shown remarkable advancements, but model prediction accuracy and generalization to different data still need to be improved.
Liu-Yang Song +5 more
semanticscholar +1 more source
In the past few years, deep learning (DL) techniques for predicting remaining useful life (RUL) have shown remarkable advancements, but model prediction accuracy and generalization to different data still need to be improved.
Liu-Yang Song +5 more
semanticscholar +1 more source
Why is the Remaining Useful Life Prediction Uncertain?
Annual Conference of the PHM Society, 2013This paper discusses the significance and interpretation of uncertainty in the remaining useful life (RUL) prediction of components used in several types of engineering applications, and answers certain fundamental questions such as “Why is the RUL prediction uncertain?”, “How to interpret the uncertainty in the RUL prediction?”, and “How to ...
Shankar Sankararaman, Kai Goebel
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