A Self-Aware Machine Platform in Manufacturing Shop Floor Utilizing MTConnect Data

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Linxia Liao Raj Minhas Arvind Rangarajan Tolga Kurtoglu Johan de Kleer

Abstract

We propose a framework of self-aware machines based on data collected using the MTConnect protocol. Beyond exist- ing applications of OEE (Overall Equipment Effectiveness) reporting, the proposed framework integrates multiple sources of information for work-piece and machine condition monitoring, and equipment time to failure prediction in manufacturing processes, and provides feedback to shop supervisor. Firstly, we propose a method to predict component wear and failure based on operational data. ICP (Interactive Closest Point) algorithm is used to find the best matching tool path given a certain tool number to identify similar machining pro- cesses. The result of ICP tool path matching, together with other parameters such as spindle speed, feed rate and tool number, are used to adaptively cluster the machining pro- cesses. For each process cluster, a particle filter based prognostic algorithm is used to predict tool wear and/or spindle bearing failure. Secondly, we propose to use anomaly detection methods to detect changes in normal behavior of the machines. Various machine learning algorithms are utilized to detect anomalies based on real-time data, and a voting mechanism is used to decide when to trigger an alarm. Thirdly, the axes traverse is aggregated to provide a measure of the wear on various axes in the machine, which is correlated to errors in position comparing to the commanded positions and nominal tool paths. Spindle load verse rotating speed is also examined to facilitate shop floor scheduling to avoid damage caused by unintentionally excessive machine usage. The proposed framework has been demonstrated using published data from two Mazak machine tools.

How to Cite

Liao, L. ., Minhas, R. ., Rangarajan, A., Kurtoglu, T. ., & de Kleer, J. . (2014). A Self-Aware Machine Platform in Manufacturing Shop Floor Utilizing MTConnect Data. Annual Conference of the PHM Society, 6(1). https://doi.org/10.36001/phmconf.2014.v6i1.2506
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Keywords

condition monitoring, early fault detection, self-aware machine

References
Barnett, V., & Lewis, T. (Eds.). (1994). Outliers in statistical data. Wiley New York.

Chen, C., Zhang, B., Vachtsevanos, G., & Orchard, M. (2011). Machine condition prediction based on adaptive neuro-fuzzy and high-order particle filtering. IEEE Transactions on Industrial Electronics, 58(9), 4353- 4364.

Coble, J., & Hines, J. W. (2009). Identifying optimal prognostic parameters from data: a genetic algorithms approach. In Proceedings of the annual conference of the prognostics and health management society.

Filzmoser, P., Garrett, R. G., & Reimann, C. (2005). Multivariate outlier detection in exploration geochemistry. Computers & Geosciences, 31(5), 579-587.

Filzmoser, P., Maronna, R., & Werner, M. (2008). Out- lier identification in high dimensions. Computational Statistics & Data Analysis, 52(3), 1694-1711.

Hodge, V. J., & Austin, J. (2004). A survey of outlier detection methodologies. Artificial Intelligence Review,22(2), 85-126.

Kohonen, T. (Ed.). (2001). Self-organizing maps. Springer. Liao, L., Edmondson, Z., & Ludwig, H. (2012). Plug and prognosis - condition monitoring, diagnosis and life time prediction. ATP edition, 54(10), 52-56.

Liao, L., & Pavel, R. (2012). Machine tool feed axis health monitoring using plug-and-prognose technology. In Proceedings of the 2012 conference of the society for
machinery failure prevention technology.

Meinshausen, N. (2006). Quantile regression forests. Journal of Machine Learning Research, 7, 983-999. MTConnect. (2009). Mtconnect standard part 1-overview and protocol, version 1.01 (Tech. Rep.). MTConnect Institute.

Rougier, N., Boniface, Y., & Universit, L. (2011). Dynamic self-organising map. Neurocomputing, 11(74), 1840-1847.

Savoye, Y. (2012). Iterative cage-based registration for dynamic shape capture. In Acm Siggraph 2012 posters.
Section
Technical Papers