OPTIMIZING ACCURACY OF MACHINE LEARNING ALGORITHMS FOR MONITORING INDUSTRIAL MACHINE OPERATION

1 . A method for optimizing machine learning algorithms for monitoring industrial machine operation, comprising:

monitoring at least one industrial machine behavioral model of at least one industrial machine;

identifying at least a first ambiguous segment of the at least one industrial machine behavioral model having a first set of characteristics, and identifying a corrective solution recommendation associated with the first ambiguous segment;

identifying at least a second ambiguous segment of the at least one industrial machine behavioral model having a second set of characteristics;

determining if a similarity between the first set of characteristics and the second set of characteristics exceed a predetermined threshold; and

updating a machine learning algorithm of the at least one industrial machine behavioral model to associate the corrective solution recommendation to the second ambiguous segment when it is determined that the similarity has exceed the predetermined threshold.

2 . The method of claim 1 , further comprising:

generating a notification related to the corrective solution recommendation for the second ambiguous segment; and

sending the notification to a client device.

3 . The method of claim 1 , wherein the first ambiguous segment indicates a suspected downtime of the at least one industrial machine.

4 . The method of claim 1 , wherein determining that the similarity has exceed the predetermined threshold is achieved using at least one of: a machine learning method, a deep learning model, a statistical approach, and a similarity function.

5 . The method of claim 1 , further comprising:

sending a first query to a client device with respect to the first ambiguous segment to determine if a detected downtime as occurred; and

determining if a downtime has occurred based on a response to the first query.

6 . The method of claim 5 , further comprising:

updating the machine learning algorithm when it is determined that no downtime has occurred.

7 . The method of claim 5 , further comprising:

sending a second query to a client device to determine if a time frame of the downtime is accurate when it is determined that downtime has occurred; and

determining if the downtime time frame is accurate based on a response to the second query.

8 . The method of claim 7 , further comprising:

updating the machine learning algorithm when it is determined that the downtime time frame is accurate.

9 . The method of claim 7 , further comprising:

sending a third query to a client device to determine an updated time frame of the downtime when it is determined that that downtime time frame is not accurate; and

updating the machine learning algorithm with the updated time frame.

10 . A non-transitory computer readable medium having stored thereon instructions for causing a processing circuitry to perform a process, the process comprising:

monitoring at least one industrial machine behavioral model of at least one industrial machine;

identifying at least a first ambiguous segment of the at least one industrial machine behavioral model having a first set of characteristics, and identifying a corrective solution recommendation associated with the first ambiguous segment;

identifying at least a second ambiguous segment of the at least one industrial machine behavioral model having a second set of characteristics;

determining if a similarity between the first set of characteristics and the second set of characteristics exceed a predetermined threshold; and

updating a machine learning algorithm of the at least one industrial machine behavioral model to associate the corrective solution recommendation to the second ambiguous segment when it is determined that the similarity has exceed the predetermined threshold;

11 . A system for optimizing machine learning algorithms for monitoring industrial machine operation, comprising:

a processing circuitry; and

a memory, the memory containing instructions that, when executed by the processing circuitry, configure the system to:

monitor at least one industrial machine behavioral model of at least one industrial machine;

identify at least a first ambiguous segment of the at least one industrial machine behavioral model having a first set of characteristics, and identifying a corrective solution recommendation associated with the first ambiguous segment;

identify at least a second ambiguous segment of the at least one industrial machine behavioral model having a second set of characteristics;

determine if a similarity between the first set of characteristics and the second set of characteristics exceed a predetermined threshold; and

update a machine learning algorithm of the at least one industrial machine behavioral model to associate the corrective solution recommendation to the second ambiguous segment when it is determined that the similarity has exceed the predetermined threshold.

12 . The system of claim 11 , wherein the system is further configured to:

generate a notification related to the corrective solution recommendation for the second ambiguous segment; and

send the notification to a client device.

13 . The system of claim 11 , wherein the first ambiguous segment indicates a suspected downtime of the at least one industrial machine.

14 . The system of claim 11 , wherein determining that the similarity has exceed the predetermined threshold is achieved using at least one of: a machine learning method, a deep learning model, a statistical approach, and a similarity function.

15 . The system of claim 11 , wherein the system is further configured to:

send a first query to a client device with respect to the first ambiguous segment to determine if a detected downtime as occurred; and

determine if a downtime has occurred based on a response to the first query.

16 . The system of claim 15 , wherein the system is further configured to:

update the machine learning algorithm when it is determined that no downtime has occurred.

17 . The system of claim 15 , wherein the system is further configured to:

send a second query to a client device to determine if a time frame of the downtime is accurate when it is determined that downtime has occurred; and

determine if the downtime time frame is accurate based on a response to the second query.

18 . The system of claim 17 , wherein the system is further configured to:

update the machine learning algorithm when it is determined that the downtime time frame is accurate.

19 . The system of claim 17 , wherein the system is further configured to:

send a third query to a client device to determine an updated time frame of the downtime when it is determined that that downtime time frame is not accurate; and

update the machine learning algorithm with the updated time frame.