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

Abhishek Hanchate, Parth Sanjaybhai Dave, Ankur Verma, Akash Tiwari, Cyan Subhra Mishra, Soundar R.T. Kumara, Anil Srivastava, Hui Yang, Vijaykrishnan Narayanan, John Morgan Sampson, Mahmut Taylan Kandemir, Kye Hwan Lee, Tanna Marie Pugh, Amy Jorden, Gautam Natarajan, Dinakar Sagapuram, Satish T.S. Bukkapatnam

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)82-110
Number of pages29
JournalSmart and Sustainable Manufacturing Systems
Issue number1
StatePublished - Jun 5 2023

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science Applications
  • Industrial and Manufacturing Engineering

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