Dynamic analysis using finite elements to calculate the critical wear section of the contact wire in suburban railway overhead conductor rails

The purpose of this study is to determine the critical wear levels of the contact wire of the catenary on metropolitan lines. The study has focussed on the zones of contact wire where localised wear is produced, normally associated with the appearance of electric arcs. To this end, a finite element model has been developed to study the dynamics of pantograph-catenary interaction. The model includes a zone of localised wear and a singularity in the contact wire in order to simulate the worst case scenario from the point of view of stresses. In order to consider the different stages in the wire wear process, different depths and widths of the localised wear zone were defined. The results of the dynamic simulations performed for each stage of wear let the area of the minimum resistant section of the contact wire be determined for which stresses are greater than the allowable stress. The maximum tensile stress reached in the contact wire shows a clear sensitivity to the size of the local wear zone, defined by its width and depth. In this way, if the wear measurements taken with an overhead line recording vehicle are analysed, it will be possible to calculate the potential breakage risk of the wire. A strong dependence of the tensile forces of the contact wire has also been observed. These results will allow priorities to be set for replacing the most critical sections of wire, thereby making maintenance much more efficient. The results obtained show that the wire replacement criteria currently borne in mind have turned out to be appropriate, although in some wear scenarios these criteria could be adjusted even more, and so prolong the life cycle of the contact wire.

Tool wear monitoring using neuro-fuzzy techniques: a comparative study in a turning process

Tool wear detection is a key issue for tool condition monitoring. The maximization of useful tool life is frequently related with the optimization of machining processes. This paper presents two model-based approaches for tool wear monitoring on the basis of neuro-fuzzy techniques. The use of a neuro-fuzzy hybridization to design a tool wear monitoring system is aiming at exploiting the synergy of neural networks and fuzzy logic, by combining human reasoning with learning and connectionist structure. The turning process that is a well-known machining process is selected for this case study. A four-input (i.e., time, cutting forces, vibrations and acoustic emissions signals) single-output (tool wear rate) model is designed and implemented on the basis of three neuro-fuzzy approaches (inductive, transductive and evolving neuro-fuzzy systems). The tool wear model is then used for monitoring the turning process. The comparative study demonstrates that the transductive neuro-fuzzy model provides better error-based performance indices for detecting tool wear than the inductive neuro-fuzzy model and than the evolving neuro-fuzzy model.

Hybrid incremental modeling based on least squares and fuzzy K-NN for monitoring tool wear in turning processes

There is now an emerging need for an efficient modeling strategy to develop a new generation of monitoring systems. One method of approaching the modeling of complex processes is to obtain a global model. It should be able to capture the basic or general behavior of the system, by means of a linear or quadratic regression, and then superimpose a local model on it that can capture the localized nonlinearities of the system. In this paper, a novel method based on a hybrid incremental modeling approach is designed and applied for tool wear detection in turning processes. It involves a two-step iterative process that combines a global model with a local model to take advantage of their underlying, complementary capacities. Thus, the first step constructs a global model using a least squares regression. A local model using the fuzzy k-nearest-neighbors smoothing algorithm is obtained in the second step. A comparative study then demonstrates that the hybrid incremental model provides better error-based performance indices for detecting tool wear than a transductive neurofuzzy model and an inductive neurofuzzy model.