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Condition based maintenance-systems integration and intelligence using Bayesian classification and sensor fusion

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Abstract

System integration in condition based maintenance (CBM) is one of the biggest challenges that need to be overcome for widespread deployment of the CBM methodology. CBM system architectures investigated in this work include an independent monitoring and control unit with no communication with machine control (Architecture 1) and a data acquisition and control unit integrated with the machine control (Architecture 2). Based on these architectures, three different CBM system applications are discussed and deployed. A verification of the third system was done by performing a destructive bearing test, causing a spindle to seize due to lubrication starvation. This test validated the CBM system developed, as well as provided insights into using sensor fusion for a better detection of bearing failure. The second part of the work discusses intelligence in a CBM system using a Bayesian probabilistic decision framework and data generated while running validation tests, it is demonstrated how the Naïve Bayes classifier can aid in the decision making of stopping the machine before catastrophic failure occurs. Discussing value in combining information supplied by more than one sensor (sensor fusion), it is demonstrated how a catastrophic failure can be prevented. The work is concluded with open issues on the topic with ongoing work and future opportunities.

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Abbreviations

CBM:

Condition based maintenance

DAQ:

Data acquisition

CNC:

Computer numerical control

COTS:

Commercial-off-the-shelf

OAC:

Open architecture control

API:

Application programming interface

FFT:

Fast fourier transform

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Acknowledgments

The authors acknowledge continued equipment support from Okuma America Corp. The authors would also like to thank Joe Alhafi, Brian Sides, Ron Sanders, Frank Gonzalez, and Casey Croussoure as they provided valuable technical insights at various milestones during this research work.

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Authors and Affiliations

  1. Mechanical Engineering Department, Clemson University, Clemson, SC, USA

    Parikshit Mehta

  2. Robert Bosch LLC, Charleston, SC, USA

    Andrew Werner

  3. Automotive Engineering Department, CU-ICAR, Clemson University, Greenville, SC, USA

    Laine Mears

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  1. Parikshit Mehta
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  2. Andrew Werner
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  3. Laine Mears
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Correspondence to Parikshit Mehta.

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Mehta, P., Werner, A. & Mears, L. Condition based maintenance-systems integration and intelligence using Bayesian classification and sensor fusion. J Intell Manuf 26, 331–346 (2015). https://doi.org/10.1007/s10845-013-0787-1

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  • DOI: https://doi.org/10.1007/s10845-013-0787-1

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