Jokin Munoa

Ball bearing damage detection using traditional signal processing algorithms

Fault detection and diagnosis of ball bearings has always been a challenge when monitoring rotating machinery. Specifically, bearing diagnostics have seen extensive research in the field of fault detection and diagnosis. This article reviews traditional algorithms used to detect and diagnose faulty bearings in heavy-duty milling machine tool spindle heads. Different kinds of faults have been created deliberately on the bearings of a test spindle head. The prediction effectiveness of several detection methods are tested when faults are in different stages of development.

INTERACTION BETWEEN MULTIPLE MODES IN MILLING PROCESSES

The productivity of many industrial cutting processes is limited by high amplitude chatter vibrations. An optimization technique based on the use of the stability lobes helps to increase the productivity of these processes, improving the life of machine elements and reducing the tool wear as well. The best-known lobes correspond to Hopf bifurcations. However, in case of interrupted cutting, additional lobes appear due to period doubling or flip bifurcation. When the system has more than one dominant vibration mode, important variations can appear in stability due to interaction between modes. The basic mathematics for the appearance of these new lobes are shown in this article. The frequency domain study shows that lobes related to flip bifurcation are a special case of the interaction between modes. The results of these interactions are verified by comparison with semi-discretization method and time domain simulations, respectively.

An integrated system for machine tool spindle head ball bearing fault detection and diagnosis

Automatic detection and diagnosis systems have always attracted considerable interest in control engineering due to their positive effects of increasing safety and product quality in machinery condition monitoring and maintenance applications. Implementing automated detection and diagnosis has always been a challenge in rotating machines. In this article, we present the development of a strategy to detect and diagnose faulty bearings in a heavy-duty milling machine tools spindle head and its implementation in a real machine. First, a comparison study of advanced methods for ball bearing fault detection in machine tool spindle heads is presented. Then, two automatic diagnosis procedures are compared: a fuzzy classifier and a neural network, which deal with different implementation questions involving the use of a priori knowledge, the computation cost, and the decision making process. The challenge is not only to be capable of diagnosing automatically but also to generalize the process regardless of the measured signals. Two actions are taken to achieve some kind of generalization of the application target: the use of normalized signals and the study of the Basis Pursuit feature extraction procedure. Finally, automatic monitoring system implementation on a real milling machine tool is presented.

Fixed Boundaries Receptance Coupling Substructure Analysis for Tool Point Dynamics Prediction

In many applications, chatter free machining is limited by the flexibility of the tool. Estimation of that capacity requires to obtain the dynamic transfer function at the tool tip. Experimental calculation of that Frequency Response Function (FRF) is a time consuming process, because it must be done using an impact test for any combination of tool, toolholder and machine. The bibliography proposes the Receptance Coupling Substructure Analysis (RCSA) to reduce the number of experimental test. A new approach consisting of calculating the fixed boundary dynamic behaviour of the tool is proposed in the paper. This way the number of modes that have to be considered is low, just one or two for each bending plane, and it supposes an important improvement in the application of the RCSA to the calculation of stability diagrams. The predictions of this new method have been verified experimentally.