Predictive Maintenance on the Machining Process and Machine Tool

This paper presents the process required to implement a data driven Predictive Maintenance (PdM) not only in the machine decision making, but also in data acquisition and processing. A short review of the different approaches and techniques in maintenance is given. The main contribution of this paper is a solution for the predictive maintenance problem in a real machining process. Several steps are needed to reach the solution, which are carefully explained. The obtained results show that the Preventive Maintenance (PM), which was carried out in a real machining process, could be changed into a PdM approach. A decision making application was developed to provide a visual analysis of the Remaining Useful Life (RUL) of the machining tool. This work is a proof of concept of the methodology presented in one process, but replicable for most of the process for serial productions of pieces

Geophysics, Vol. 70, No. 3 (may-June 2005); P. T45--T56, 14 Figs.

Riemannian spaces are described by nonorthogonal curvilinear coordinates. We generalize one-way wavefield extrapolation to semiorthogonal Riemannian coordinate systems that include, but are not limited to, ray coordinate systems. We obtain a one-way wavefield extrapolation method that can be used for waves propagating in arbitrary directions, in contrast to downward continuation, which is used for waves propagating mainly in the vertical direction. Ray coordinate systems can be initiated in many different ways; for example, from point sources or from plane waves incident at various angles. Since wavefield propagation happens mostly along the extrapolation direction, we can use inexpensive finite-difference or mixed-domain extrapolators to achieve high angle accuracy. The main applications of our method include imaging of steeply dipping or overturning reflections

Limitations Of Multibunch Feedback Systems And

In recent years, multibunch feedback systems which can handle bunches with 10-ns spacing (or shorter) are working at several accelerators around the world. The number of bunches stored in some of these accelerators is very large, on the order of 1000. Due to the large scale and fast processing requirements, the designs of the feedback systems are quite different from those of 10 years ago. In this report, we review the features of these feedback systems and give several examples. In addition, we give some examples of powerful beam-diagnostics system based on these feedback systems