CNC machine tool's wear diagnostic and prognostic by using dynamic Bayesian networks

Abstract

The failure of critical components in industrial systems may have negative consequences on the availability, the productivity, the security and the environment. To avoid such situations, the health condition of the physical system, and particularly of its critical components, can be constantly assessed by using the monitoring data to perform on-line system diagnostics and prognostics. The present paper is a contribution on the assessment of the health condition of a Computer Numerical Control (CNC) tool machine and the estimation of its Remaining Useful Life (RUL). The proposed method relies on two main phases: an off-line phase and an on-line phase. During the first phase, the raw data provided by the sensors are processed to extract reliable features. These latter are used as inputs of learning algorithms in order to generate the models that represent the wear's behavior of the cutting tool. Then, in the second phase, which is an assessment one, the constructed models are exploited to identify the tool's current health state, predict its RUL and the associated confidence bounds. The proposed method is applied on a benchmark of condition monitoring data gathered during several cuts of a CNC tool. Simulation results are obtained and discussed at the end of the paper.

An Approach to Integrating Tactical Decision-Making in Industrial Maintenance Balance Scorecards Using Principal Components Analysis and Machine Learning

“…Hierarchical clustering is used, as is Neuronal Network of Self-Organizing Map (SOM). Both algorithms identify groups of individuals by similar behaviors from individual data and have been used effectively both to identify the stages of wear in industrial environments [34] and to characterize the energy in electrical supply networks [23]. Hierarchical clustering makes it possible to show the natural grouping structure of the data as a function of the metric that is set as a criterion of proximity, whereas SOM decomposes the data into a set number of groups.…”

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“…Kilundu et al [21] studied tool wear by extracting features associated with tool wear from three different frequency bands, applying a windowed singular spectrum analysis and several machine learning techniques. Tobon-Mejia et al [22] measured cutting forces, acoustic emissions and vibrations to train a 'mixture of Gaussians Hidden Markov Model' to build a model to predict the useful lifetime of a milling cutter. Yen et al [23] trained a neural network (NN) with the frequency spectrum of acoustic emissions and applied a self-organization feature map to make the method more robust with respect to influence of noise.…”

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“…Some works were developed recently in this field [2]. We can cite for instance the contribution of Tobon-Mejia et al [6]. The authors used Dynamic Bayesian Network, after a learning phase, to estimate the RUL of an equipment.…”