Advanced Search
Select up to three search categories and corresponding keywords using the fields to the right. Refer to the Help section for more detailed instructions.

Search our Collections & Repository

All these words:

For very narrow results

This exact word or phrase:

When looking for a specific result

Any of these words:

Best used for discovery & interchangable words

None of these words:

Recommended to be used in conjunction with other fields

Language:

Dates

Publication Date Range:

to

Document Data

Title:

Document Type:

Library

Collection:

Series:

People

Author:

Help
Clear All

Query Builder

Query box

Help
Clear All

For additional assistance using the Custom Query please check out our Help Page

i

Leveraging IIoT to Improve Machine Safety in the Mining Industry

Filetype[PDF-1013.22 KB]


  • English
    • Details Supporting Files You May Also Like

    Details:

    • Alternative Title:
      Min Metall Explor
    • Personal Author:
    • Description:
      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.
    • Subjects:
    • Source:
      Min Metall Explor. 36(4):675-681
    • Pubmed ID:
      33005876
    • Pubmed Central ID:
      PMC7527258
    • Document Type:
      Journal Article
    • Funding:
      CC999999/ImCDC/Intramural CDC HHS/United States
    • Collection(s):
    • Main Document Checksum:
    • Download URL:
    • File Type:

    You May Also Like

    Checkout today's featured content at stacks.cdc.gov