A computer-implemented method for automatic event detection in industrial processes, comprising: (a) acquiring monitoring data from an acquisition system, wherein the monitoring data is imaging data and/or acoustic data; wherein the following steps are performed by a machine learning engine: (b) analyzing historical data to generate a library comprising statistics, historical pattern and expected events and their corresponding historical alerting and control protocols, wherein the statistics is used to adapt and augment event detection, alerting control and protocols, (c) classifying the monitoring data under at least a classifier associated with imaging and/or acoustic data, said at least a classifier representing one or more pieces of equipment, one or more components, or one or more characteristics that are identified, from the monitoring data, using image and/or acoustic processing algorithms; (d) for each said at least a classifier associated with imaging and/or acoustic data, using algorithms and/or the historical patterns for generating at least one graphical and/or numerical quantifier; (e) detecting an event when the at least one quantifier satisfies a predetermined rule corresponding to the at least one quantifier, and when an event is detected, the method further comprises: (f) matching the detected event to one of the expected events; (g) if there is no match, generating and transmitting an alert to inform an operations staff that an unexpected detected event has occurred and to provide the operations staff with descriptive text and supporting images with regions of interest highlighted or specific portions of acoustic data for review and identification of potential abnormalities; (h) if there is a match, using the historical alerting or control protocols for transmitting an alert workflow associated with the expected event or the control signal associated with the expected event, wherein the control signal is transmitted to a controllable equipment related to the detected event. (a) acquiring monitoring data from an acquisition system, wherein the monitoring data is imaging data and/or acoustic data; wherein the following steps are performed by a machine learning engine: (b) analyzing historical data to generate a library comprising statistics, historical pattern and expected events and their corresponding historical alerting and control protocols, wherein the statistics is used to adapt and augment event detection, alerting control and protocols, (c) classifying the monitoring data under at least a classifier associated with imaging and/or acoustic data, said at least a classifier representing one or more pieces of equipment, one or more components, or one or more characteristics that are identified, from the monitoring data, using image and/or acoustic processing algorithms; (d) for each said at least a classifier associated with imaging and/or acoustic data, using algorithms and/or the historical patterns for generating at least one graphical and/or numerical quantifier; (e) detecting an event when the at least one quantifier satisfies a predetermined rule corresponding to the at least one quantifier, and when an event is detected, the method further comprises: (f) matching the detected event to one of the expected events; (g) if there is no match, generating and transmitting an alert to inform an operations staff that an unexpected detected event has occurred and to provide the operations staff with descriptive text and supporting images with regions of interest highlighted or specific portions of acoustic data for review and identification of potential abnormalities; (h) if there is a match, using the historical alerting or control protocols for transmitting an alert workflow associated with the expected event or the control signal associated with the expected event, wherein the control signal is transmitted to a controllable equipment related to the detected event.