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MACHINE LEARNING BASED PREDICTIVE MAINTENANCE OF A DRYER

  • US 20170051978A1
  • Filed: 08/23/2015
  • Published: 02/23/2017
  • Est. Priority Date: 08/23/2015
  • Status: Active Grant
First Claim
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1. A machine learning method for predictive maintenance of a dryer comprising:

  • i) obtaining over a communication network, an information associated with the dryer, wherein the information comprises measurements of a current associated with at least one heater of a heater bank, wherein the heater bank is associated with the dryer;

    determining a failure data associated with the at least one heater, wherein the failure data indicates one of a poorly functioning and a failed heater, wherein the failure data is determined through a comparison of ratio of three phase currents with a healthy heater using one of a split-core transformer type machine wearable current sensor or a Hall effect based current sensor associated with the at least one heater;

    ii) receiving measurements of a vibration level of at least a process blower, a cassette motor and a regeneration blower associated with at least one dryer;

    iii) determining an anomaly through at least one of a back pressure and a fault associated with at least one of the cassette motor and the regeneration blower, wherein the anomaly is determined based on at least one of a vibration and magnetic field through an IoT based method;

    iv) tracking at least one of the vibration and the magnetic field of at least one of the process blower and the regeneration blower; and

    v) raising an alarm for maintenance when an anomaly is at least one of a detected in a real-time and predicted for a future time, wherein the prediction is based on at least one of a machine learning algorithm or a look up table.

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