Machine learning based predictive maintenance of a dryer
First Claim
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1. A machine learning architecture associated with a dryer comprising:
- (a) one or more heaters linked to a three-phase power supply;
(b) one or more machine wearable sensors;
(c) a process blower;
(d) a cassette motor;
(e) a regeneration blower associated with the one or more machine wearable sensors; and
(f) a processor configured to execute instructions which, when executed by the processor, causes the processor to;
(i) receive a sensor data over a communication network, wherein the sensor data is at least one of a vibration, a magnetic field of at least one of the process blower, the cassette motor, and the regeneration blower, and a current measurement;
(ii) indicate a failure of the at least one heater based on a reading of current by a machine wearable sensor associated with the at least one heater;
(iii) determine an anomaly based on characteristic of vibration at least of one or more of a process blower, a cassette motor, and a regeneration blower;
(iv) track a balance of at least one or more of the process blower and the regeneration blower; and
(v) raise an alarm for maintenance, when at least one of an anomaly, a failure, and an off-balance is detected;
wherein the anomaly, the failure, and the off balance is at least one of detected in real-time and predicted for a future time, wherein the prediction is based on a machine learning algorithm.
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Abstract
A machine learning method and system for predictive maintenance of a dryer. The method includes obtaining over a communication network, an information associated with the dryer and receiving measurements of a vibration level of one of a process blower, a cassette motor and a regeneration blower associated with the dryer. Further, an anomaly is determined based on at least one of a back pressure and a fault and balance of at least one of the process blower and the regeneration blower is tracked. An alarm for maintenance is raised when one of an anomaly and an off-balance is detected.
111 Citations
10 Claims
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1. A machine learning architecture associated with a dryer comprising:
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(a) one or more heaters linked to a three-phase power supply; (b) one or more machine wearable sensors; (c) a process blower; (d) a cassette motor; (e) a regeneration blower associated with the one or more machine wearable sensors; and (f) a processor configured to execute instructions which, when executed by the processor, causes the processor to; (i) receive a sensor data over a communication network, wherein the sensor data is at least one of a vibration, a magnetic field of at least one of the process blower, the cassette motor, and the regeneration blower, and a current measurement; (ii) indicate a failure of the at least one heater based on a reading of current by a machine wearable sensor associated with the at least one heater; (iii) determine an anomaly based on characteristic of vibration at least of one or more of a process blower, a cassette motor, and a regeneration blower; (iv) track a balance of at least one or more of the process blower and the regeneration blower; and (v) raise an alarm for maintenance, when at least one of an anomaly, a failure, and an off-balance is detected; wherein the anomaly, the failure, and the off balance is at least one of detected in real-time and predicted for a future time, wherein the prediction is based on a machine learning algorithm. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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Specification