Non-intrusive condition monitoring for manufacturing systems

Non-intrusive condition monitoring for manufacturing systems. Suzuki, R., Kohmoto, S., & Ogatsu, T. In 2017 25th European Signal Processing Conference (EUSIPCO), pages 1390-1394, Aug, 2017.

Paper doi abstract bibtex

A non-intrusive method for monitoring conditions in manufacturing systems is proposed. The method requires only a single current sensor for the monitoring of multiple machine individually, which is done by means of disaggregating measured waveforms. For accurate disaggregation even in complicated systems with multiple identical machines, it employs a new model-combining factorial hidden Markov model (FHMM) with behavioral models derived from queuing theory. Experimental results with an actual system show that the proposed method achieves more accurate disaggregation than conventional methods and obtains such valuable information on productivity as the reasons for and timing of manufacturing process stoppages.

@InProceedings{8081437,
  author = {R. Suzuki and S. Kohmoto and T. Ogatsu},
  booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},
  title = {Non-intrusive condition monitoring for manufacturing systems},
  year = {2017},
  pages = {1390-1394},
  abstract = {A non-intrusive method for monitoring conditions in manufacturing systems is proposed. The method requires only a single current sensor for the monitoring of multiple machine individually, which is done by means of disaggregating measured waveforms. For accurate disaggregation even in complicated systems with multiple identical machines, it employs a new model-combining factorial hidden Markov model (FHMM) with behavioral models derived from queuing theory. Experimental results with an actual system show that the proposed method achieves more accurate disaggregation than conventional methods and obtains such valuable information on productivity as the reasons for and timing of manufacturing process stoppages.},
  keywords = {condition monitoring;hidden Markov models;machinery;manufacturing systems;nonintrusive condition monitoring;manufacturing systems;current sensor;machine monitoring;measured waveforms disaggregation;factorial hidden Markov model;FHMM;behavioral models;queuing theory;Hidden Markov models;Monitoring;Home appliances;Queueing analysis;Manufacturing processes;Power demand;non-intrusive monitoring;factorial hidden Markov model;queueing network},
  doi = {10.23919/EUSIPCO.2017.8081437},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570347798.pdf},
}

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