• Login
    View Item 
    •   Home
    • University of Alaska Fairbanks
    • UAF Graduate School
    • Engineering
    • View Item
    •   Home
    • University of Alaska Fairbanks
    • UAF Graduate School
    • Engineering
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of Scholarworks@UACommunitiesPublication DateAuthorsTitlesSubjectsTypeThis CollectionPublication DateAuthorsTitlesSubjectsType

    My Account

    Login

    First Time Submitters, Register Here

    Register

    Statistics

    Display statistics

    Automatic detection of sensor calibration errors in mining industry

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    Pothina_R_2017.pdf
    Size:
    44.56Mb
    Format:
    PDF
    Download
    Author
    Pothina, Rambabu
    Chair
    Ganguli, Rajive
    Committee
    Ghosh, Tathagata
    Lawlor, Orion
    Barry, Ronald
    Keyword
    Detectors
    Defects
    Reporting
    Alaska
    Interior Alaska
    Testing
    Autogenous grinding
    Calibration
    Mining machinery
    Mines and mineral resources
    Electric equipment
    Show allShow less
    Metadata
    Show full item record
    URI
    http://hdl.handle.net/11122/8137
    Abstract
    Sensor errors cost the mining industry millions of dollars in losses each year. Unlike gross errors, "calibration errors" are subtle, develop over time, and are difficult to identify. Economic losses start accumulating even when errors are small. Therefore, the aim of this research was to develop methods to identify calibration errors well before they become obvious. The goal in this research was to detect errors at a bias as low as 2% in magnitude. The innovative strategy developed relied on relationships between a variety of sensors to detect when a given sensor started to stray. Sensors in a carbon stripping circuit at a gold processing facility (Pogo Mine) in Alaska were chosen for the study. The results from the initial application of classical statistical methods like correlation, aggregation and principal component analysis (PCA), and the signal processing methods (FFT), to find bias (±10%) in "feed" sensor data from a semi-autogenous (SAG) grinding mill operation (Fort Knox mine, Alaska) were not promising due to the non-linear and non-stationary nature of the process characteristics. Therefore, those techniques were replaced with some innovative data mining techniques when the focus shifted to Pogo Mine, where the task was to detect calibration errors in strip vessel temperature sensors in the carbon stripping circuit. The new techniques used data from two strip vessel temperature sensors (S1 and S2), four heat exchanger related temperature sensors (H1 through H4), barren flow sensor (BARNFL) and a glycol flow sensor (GLYFL). These eight sensors were deemed to be part of the same process. To detect when the calibration of one of the strip vessel temperature sensors, S1, started to stray, tests were designed to detect changes in relationship between the eight temperature sensors. Data was filtered ("threshold") based on process characteristics prior to being used in tests. The tests combined basic concepts such as moving windows of time, ratios (ratio of one sensor data to data from a set of sensors), tracking of maximum values, etc. Error was triggered when certain rules were violated. A 2% error was randomly introduced into one of the two strip vessel temperature data streams to simulate calibration errors. Some tests were less effective than others at detecting the simulated errors. The tests that used GLYFL and BARNFL were not very effective. On the other hand, the tests that used total "Heat" of all the heat exchanger sensors were very effective. When the tests were administered together ("Combined test"), they have a high success rate (95%) in terms of True alarms, i.e., tests detecting bias after it is introduced. In those True alarms, for 75% of the cases, the introduction of the error was detected within 39.5 days. A -2% random error was detected with a similar success rate.
    Description
    Dissertation (Ph.D.) University of Alaska Fairbanks, 2017
    Date
    2017-12
    Type
    Dissertation
    Collections
    Engineering

    entitlement

     
    ABOUT US|HELP|BROWSE|ADVANCED SEARCH

    The University of Alaska Fairbanks is an affirmative action/equal opportunity employer and educational institution and is a part of the University of Alaska system.

    ©UAF 2013 - 2023 | Questions? ua-scholarworks@alaska.edu | Last modified: September 25, 2019

    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.