Deep learning has attracted intense interest in Prognostics and Health Management (PHM),
because of its enormous representing power, automated feature learning capability and best …

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 …

Recent advancements in technology have emerged the requirements of hardware and
software applications. Along with this technical growth, software industries also have faced …

Defect prediction models-classifiers that identify defect-prone software modules-have
configurable parameters that control their characteristics (eg, the number of trees in a …

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 …

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 …

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 …

Support Vector Machines (SVMs) are kernel-based learning methods, which have been
successfully adopted for regression problems. However, their use in reliability applications …

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 …

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 …