Leveraging IIoT to Improve Machine Safety in the Mining Industry

  • M. McNinchEmail author
  • D. Parks
  • R. Jacksha
  • A. Miller


Each year, hundreds of mine workers are involved in machinery-related accidents. Many of these accidents involve inadequate or improper use of lockout/tagout (LOTO) procedures. To mitigate the occurrence of these accidents, new safety methods are needed to monitor access to hazardous areas around operating machinery, improve documentation/monitoring of maintenance that requires shutdown of the machinery, and prevent unexpected startup or movement during machine maintenance activities. The National Institute for Occupational Safety and Health (NIOSH) is currently researching the application of Internet of Things (IoT) technologies to provide intelligent machine monitoring as part of a comprehensive LOTO program. This paper introduces NIOSH’s two phase implementation of an IoT-based intelligent machine monitoring system. Phase one is the installation of a proof-of-concept system at a concrete batch plant, while phase two involves scaling up the system to include additional sensors, more detailed safety/performance metrics, proximity detection, and predictive failure analysis.


IoT Conveyor Lockout/tagout Machine safety 



The authors wish to acknowledge the management and staff of Central Pre-Mix a CRH Company for their cooperation and assistance in this work. Additionally, the authors would like to express their gratitude to Greg Bierie, Managing Director of Safe Reliable Systems, for partnering with NIOSH in an effort to bring this solution to wide-scale implementation.

Compliance with Ethical Standards


The findings and conclusions in this paper are those of the authors and do not necessarily represent the official position of the National Institute for Occupational Safety and Health, Centers for Disease Control and Prevention. Mention of any company or product does not constitute endorsement of NIOSH.

Conflict of interest

The authors declare that they have no conflict of interest.


  1. 1.
    MSHA (2017) Accident/injury/illness: machinery/power Haulage data, 2001-2016Google Scholar
  2. 2.
    Ruff T, Coleman P, Martini L (2011) Machine-related injuries in the US mining industry and priorities for safety research. Int J Inj Control Saf Promot 18(1):11–20CrossRefGoogle Scholar
  3. 3.
    MSHA (2018) Safety improvement technologies for mobile equipment at surface mines, and for belt conveyors at surface and underground mines. Proposed rule. Fed Register 83 FR 29716 (to be codified at 30 CFR 56 and 75). September 1 2018; Available from:
  4. 4.
    Zhou C, Damiano N, Whisner B, Reyes M (2017) Industrial Internet of Things: (IIoT) applications in underground coal mines. Min Eng 69(12):50–56CrossRefGoogle Scholar
  5. 5.
    Reyes MA, King GW, Miller GG (2014) Intelligent machine guard monitoring: a wireless system to improve miner Safety. IEEE Ind Appl Mag 20(2):69–75CrossRefGoogle Scholar
  6. 6.
    Akyildiz IF, Wang X (2005) A survey on wireless mesh networks. IEEE Commun Mag 43(9):S23–S30CrossRefGoogle Scholar
  7. 7.
    Bruno R, Conti M, Gregori E (2005) Mesh networks: commodity multihop ad hoc networks. IEEE Commun Mag 43(3):123–131CrossRefGoogle Scholar
  8. 8.
    Zheng K et al (2012) Radio resource allocation in LTE-advanced cellular networks with M2M communications. IEEE Commun Mag 50(7)Google Scholar
  9. 9.
    LoRa Alliance (2017) LoRaWAN specification. Available from:
  10. 10.
    ZigBee Alliance (2012) ZigBee specification. Available from:
  11. 11.
    Bluetooth SIG (2016) Bluetooth Core specification v 5.0. Available from:
  12. 12.
    MSHA (2018) Examinations of working places in metal and nonmetal mines. Fed Register 83 FR 15055. April 9 2018. Available from

Copyright information

© This is a U.S. government work and its text is not subject to copyright protection in the United States; however, its text may be subject to foreign copyright protection  2019

Authors and Affiliations

  1. 1.CDC NIOSHSpokaneUSA

Personalised recommendations