Data-driven predictive maintenance method for digital welding machines

Volume Driven Data Distribution for NUMA-Machines

Highly scalable parallel computers, e.g. SCI-coupled workstation clusters, are NUMA architectures. Thus good static locality is essential for high performance and scalability of parallel programs on these machines. This paper describes novel techniques to optimize static locality at compilation time by application of data transformations and data distributions. The metric which guides the optim...

Data-driven Kernels for Support Vector Machines

Kernel functions can map data points to a non-linear feature space implicitly, and thus significantly enhance the effectiveness of some linear models like SVMs. However, in current literature, there is a lack of a proper kernel that can deal with categorical features naturally. In this paper, we present a novel kernel for SVM classification based on data-driven similarity measures to compute th...

A Data-driven Method for Crowd Simulation using a Holonification Model

In this paper, we present a data-driven method for crowd simulation with holonification model. With this extra module, the accuracy of simulation will increase and it generates more realistic behaviors of agents. First, we show how to use the concept of holon in crowd simulation and how effective it is. For this reason, we use simple rules for holonification. Using real-world data, we model the...

Consumables for repair and maintenance welding

Predictive maintenance policy based on process data

a r t i c l e i n f o For the 'under maintained' and 'over maintained' problems of traditional preventive maintenance, a new predictive maintenance policy is developed based on process data in this article to overcome these disadvantages. This predictive maintenance method utilizes results of probabilistic fault prediction, which reveals evolvement of the system's degradation for a gradually de...