Journal Published Online: 05 June 2023
Volume 7, Issue 1

A Graphical Representation of Sensor Mapping for Machine Tool Fault Monitoring and Prognostics for Smart Manufacturing

CODEN: SSMSCY

Abstract

This paper introduces a graphical representation based on the fusion of several disparate standards to instantiate a sensor wrapper and sensing schema for fault delineation in machine tools and other manufacturing assets. Texas A&M researchers have already developed a sensor wrapper that aims to specify the sensor and the sensing suite based on a systematic consideration of the functionality (based on process dynamics) to derive the configuration and instantiation of a viable sensing suite. Adapting this scheme for real-world machines and manufacturing assets is challenging because of the complexity of the machine tool structure and the diversity of faults within its components. The presented graphical representation method is based on an ontological compliance with MTConnect and International Organization for Standardization/International Electrotechnical Commission standards, and the representation employs the graphical motifs pertaining to the fault tree framework. Such representation is essential for the delineation of failure modes associated with components of a machine tool, thereby making sensor-wrappers viable for the smartification of machine tools. The issues pruning the levels of the graphical representation based on the domain knowledge of the machine tool, and the many-to-many mapping between the components and sensors are discussed. The representation was applied in order to derive suitable sensing schemes for conventional machine tools, i.e., lathe and milling machines and a modern hybrid additive manufacturing machine.

Author Information

Hanchate, Abhishek
Industrial and Systems Engineering Department, Texas A&M University, College Station, TX, USA
Dave, Parth Sanjaybhai
Industrial and Systems Engineering Department, Texas A&M University, College Station, TX, USA
Verma, Ankur
Department of Industrial Engineering, College of Engineering, Pennsylvania State University, University Park, PA, USA
Tiwari, Akash
Industrial and Systems Engineering Department, Texas A&M University, College Station, TX, USA
Mishra, Cyan Subhra
Department of Computer Science and Electrical Engineering, Pennsylvania State University, University Park, PA, USA
Kumara, Soundar R. T.
Department of Industrial Engineering, College of Engineering, Pennsylvania State University, University Park, PA, USA
Srivastava, Anil
Department of Manufacturing and Industrial Engineering, The University of Texas Rio Grande Valley, Edinburg, TX, USA
Yang, Hui
Department of Industrial Engineering, College of Engineering, Pennsylvania State University, University Park, PA, USA
Narayanan, Vijaykrishnan
Department of Computer Science and Electrical Engineering, Pennsylvania State University, University Park, PA, USA
Sampson, John Morgan
Department of Computer Science and Electrical Engineering, Pennsylvania State University, University Park, PA, USA
Kandemir, Mahmut Taylan
Department of Computer Science and Electrical Engineering, Pennsylvania State University, University Park, PA, USA
Lee, Kye-Hwan
Department of Manufacturing and Industrial Engineering, The University of Texas Rio Grande Valley, Edinburg, TX, USA
Pugh, Tanna Marie
Pennsylvania Technical Assistance Program, Penn State College of Engineering, University Park, PA, USA
Jorden, Amy
Pennsylvania Technical Assistance Program, Penn State College of Engineering, University Park, PA, USA
Natarajan, Gautam
Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY, USA
Sagapuram, Dinakar
Industrial and Systems Engineering Department, Texas A&M University, College Station, TX, USA
Bukkapatnam, Satish T. S.
Industrial and Systems Engineering Department, Texas A&M University, College Station, TX, USA
Pages: 29
Price: Free
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Details
Stock #: SSMS20220031
ISSN: 2520-6478
DOI: 10.1520/SSMS20220031