A review on deep learning applications in prognostics and health management
Deep learning has attracted intense interest in Prognostics and Health Management (PHM),
because of its enormous representing power, automated feature learning capability and best …
because of its enormous representing power, automated feature learning capability and best …
Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice
E Zio - Reliability Engineering & System Safety, 2022 - Elsevier
We are performing the digital transition of industry, living the 4th industrial revolution,
building a new World in which the digital, physical and human dimensions are interrelated in …
building a new World in which the digital, physical and human dimensions are interrelated in …
Survey on software defect prediction techniques
Recent advancements in technology have emerged the requirements of hardware and
software applications. Along with this technical growth, software industries also have faced …
software applications. Along with this technical growth, software industries also have faced …
The impact of automated parameter optimization on defect prediction models
Defect prediction models-classifiers that identify defect-prone software modules-have
configurable parameters that control their characteristics (eg, the number of trees in a …
configurable parameters that control their characteristics (eg, the number of trees in a …
A new multiple source domain adaptation fault diagnosis method between different rotating machines
Fault diagnosis based on data-driven methods are widely investigated when enough
supervised samples of the target machine are available to build a reliable model. However …
supervised samples of the target machine are available to build a reliable model. However …
[HTML][HTML] Reliability engineering applications in electronic, software, nuclear and aerospace industries: A 20 year review (2000–2020)
A review on reliability engineering applications in 4 industrial domains namely electronic,
software, nuclear and aerospace from the 2000′ s to the present day is compiled. The …
software, nuclear and aerospace from the 2000′ s to the present day is compiled. The …
Joint decision-making of parallel machine scheduling restricted in job-machine release time and preventive maintenance with remaining useful life constraints
X He, Z Wang, Y Li, S Khazhina, W Du, J Wang… - Reliability Engineering & …, 2022 - Elsevier
The machine remaining useful life (RUL), the job-machine release time and the correlation
between the maintenance duration and the machine enlistment age are, in this paper …
between the maintenance duration and the machine enlistment age are, in this paper …
Failure and reliability prediction by support vector machines regression of time series data
Support Vector Machines (SVMs) are kernel-based learning methods, which have been
successfully adopted for regression problems. However, their use in reliability applications …
successfully adopted for regression problems. However, their use in reliability applications …
Software reliability prediction using a deep learning model based on the RNN encoder–decoder
J Wang, C Zhang - Reliability Engineering & System Safety, 2018 - Elsevier
Different software reliability models, such as parameter and non-parameter models, have
been developed in the past four decades to assess software reliability in the software testing …
been developed in the past four decades to assess software reliability in the software testing …
Retraining strategy-based domain adaption network for intelligent fault diagnosis
Industrial Internet of Things (IIoT) obtains big data from industrial facilities. Based on these
data, health conditions for facilities can be predicted using machine learning methods, which …
data, health conditions for facilities can be predicted using machine learning methods, which …