Abstract: This disclosure relates to systems and methods for performing an autonomous procedure for monitoring and diagnostics of a machine using electrical signature analysis. In one embodiment of the disclosure, a method includes providing electrical data of an electrical rotating machine associated with at least one fault frequency. While in a learning mode, the method includes converting the electrical data from a time domain to a frequency domain to obtain baseline data. While in an operational mode, the method includes converting the electrical data from the time domain to the frequency domain to obtain monitoring data. The method further includes determining, based at least on the monitoring data, a ratio value at the fault frequency, determining a rate of change of the ratio value at the fault frequency, and, optionally, issuing, based on the rate of change, an alarm concerning at least one event of the electrical rotating machine.

Abstract: This disclosure relates to systems and methods for electrical signature analysis of electrical rotating machines. In one embodiment of the disclosure, a method includes ascertaining initial information associated with an electrical rotating machine, assigning a plurality of operational conditions associated with the machine to a plurality of buckets, and recording the electrical data to obtain a pre-defined number of sets of learning data. The method further includes determining, based at least on the initial information and the learning data, that the machine is in a healthy condition, obtaining, based on the learning data, baseline data associated with at the at least one bucket, and generating, based on the baseline data, a threshold associated with the bucket and at least one fault frequency associated with the machine. The method further includes generating, based on the threshold and the baseline data, alarms concerning a state of the electrical rotating machine.

Abstract: This disclosure relates to systems and methods for electrical signature analysis of electrical rotating machines. In one embodiment of the disclosure, a method includes ascertaining initial information associated with an electrical rotating machine, assigning a plurality of operational conditions associated with the machine to a plurality of buckets, and recording the electrical data to obtain a pre-defined number of sets of learning data. The method further includes determining, based at least on the initial information and the learning data, that the machine is in a healthy condition, obtaining, based on the learning data, baseline data associated with at the at least one bucket, and generating, based on the baseline data, a threshold associated with the bucket and at least one fault frequency associated with the machine. The method further includes generating, based on the threshold and the baseline data, alarms concerning a state of the electrical rotating machine.

Abstract: This disclosure relates to systems and methods for performing an autonomous procedure for monitoring and diagnostics of a machine using electrical signature analysis. In one embodiment of the disclosure, a method includes providing electrical data of an electrical rotating machine associated with at least one fault frequency. While in a learning mode, the method includes converting the electrical data from a time domain to a frequency domain to obtain baseline data. While in an operational mode, the method includes converting the electrical data from the time domain to the frequency domain to obtain monitoring data. The method further includes determining, based at least on the monitoring data, a ratio value at the fault frequency, determining a rate of change of the ratio value at the fault frequency, and, optionally, issuing, based on the rate of change, an alarm concerning at least one event of the electrical rotating machine.