System and method for forecasting industrial machine failures

1. A method for forecasting failures in industrial machines, comprising:

receiving raw sensory inputs collected from at least one machine;

generating a plurality of data features based on the raw sensory inputs;

selecting from the plurality of data features a plurality of indicative data features, wherein the selection is based on a distribution of the plurality of indicative data features that determines an association between the plurality of indicative data features and a machine failure, wherein the distribution of the plurality of indicative data features is a set of abnormal parameters of each of the indicative data features;

selecting, based on the plurality of indicative data features, a machine learning model;

applying the selected machine learning model to the plurality of indicative data features; and

determining a probability for a forthcoming machine failure.

2. The method of claim 1 , wherein each type of the raw sensory inputs is related to at least a process that is associated with the machine.

3. The method of claim 1 , further comprising:

preprocessing the received raw sensory inputs.

4. The method of claim 3 , wherein preprocessing includes any of the following processes: data cleansing, normalization, rescaling, re-trending, reformatting, and noise filtering.

5. The method of claim 1 , further comprising:

generating a notification for the determined probability of the forthcoming machine failure, wherein the notification indicates the at least one of: the forthcoming machine failure, and at least one recommendation for avoiding or mitigation the determined forthcoming machine failure.

6. The method of claim 1 , wherein the selecting of the machine learning model further comprises:

selecting a set of hyperparameters that is optimal for use with the selected machine learning model for optimizing an accuracy level of the machine learning model.

7. The method of claim 6 , wherein the selected machine learning model comprises an accurate machine failure prediction capabilities and better long-range predictability of a machine failure with respect to a plurality of machine learning models.

8. The method of claim 1 , wherein the determination of the at least a probability is performed on a constant basis.

9. The method of claim 1 , wherein the at least one data feature is represented by at least a statistical feature.

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

receiving raw sensory inputs collected from at least one machine;

generating a plurality of data features based on the raw sensory inputs;

selecting from the plurality of data features a plurality of indicative data features, wherein the selection is based on a distribution of the plurality of indicative data features that determines an association between the plurality of indicative data features and a machine failure, wherein the distribution of the plurality of indicative data features is a set of abnormal parameters of each of the indicative data features;

selecting, based on the plurality of indicative data features, a machine learning model;

applying the selected machine learning model to the plurality of indicative data features; and

determining a probability for a forthcoming machine failure.

11. A system for forecasting failures in industrial machines, comprising:

a processing circuitry; and

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

receiving raw sensory inputs collected from at least one machine;

generate a plurality of data features based on the raw sensory inputs;

select from the plurality of data features a plurality of indicative data features, wherein the selection is based on a distribution of the plurality of indicative data features that determines an association between the plurality of indicative data features and a machine failure, wherein the distribution of the plurality of indicative data features is a set of abnormal parameters of each of the indicative data features;

select, based on the plurality of indicative data features, a machine learning model;

apply the selected machine learning model to the plurality of indicative data features; and

determine a probability for a forthcoming machine failure.

12. The system of claim 11 , wherein each type of the raw sensory inputs is related to at least a process that is associated with the machine.

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

preprocess the received raw sensory inputs.

14. The system of claim 13 , wherein preprocessing includes any of the following processes: data cleansing, normalization, rescaling, re-trending, reformatting, and noise filtering.

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

generating a notification for the determined probability of the forthcoming machine failure, wherein the notification indicates the at least one of: the forthcoming machine failure, and at least one recommendation for avoiding or mitigation the determined forthcoming machine failure.

16. The system of claim 11 , wherein the selecting of the machine learning model further comprises:

selecting a set of hyperparameters that is optimal for use with the selected machine learning model for optimizing an accuracy level of the machine learning model.

17. The system of claim 16 , wherein the selected machine learning model comprises an accurate machine failure prediction capabilities and better long-range predictability of a machine failure with respect to a plurality of machine learning models.

18. The system of claim 11 , wherein the determination of the at least a probability is performed on a constant basis.

19. The system of claim 11 , wherein the at least one data feature is represented by at least a statistical feature.