To meet the specifications of low cost, highly reliable electronic devices, fault diagnosis
techniques play an essential role. It is vital to find flaws at an early stage in design …

With the wide application of industrial robots in the field of precision machining, reliability
analysis of positioning accuracy becomes increasingly important for industrial robots. Since …

Composite materials owing to low density and beneficial properties such as high stiffness,
low coefficient of thermal expansion, high mechanical strength, high dimensional stability …

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

Aiming at the problem of low accuracy of reliability prediction, a back propagation neural
network (BPNN) model is developed. In the process of reliability prediction, a dynamic …

This article discusses the problems associated with the redundancy of structures k-out-of-n
as a method of increasing system availability. A parallel k-out-of-n system was considered …

Effectively forecasting the failure data in the usage stage is essential to reasonably make
reliability plans and carry out reliability maintaining activities. Beginning with the historical …

The ability of an artificial neural network model, using a back propagation learning
algorithm, to predict specific fuel consumption and exhaust temperature of a Diesel engine …

We introduce a new family of continuous distributions called the odd Lomax-G class and
provide four special models. We derive explicit expressions for the ordinary and incomplete …

The time series prediction of a practical power system is investigated in this paper. The
radial basis function neural network (RBFNN) with a nonlinear time-varying evolution …