Abstract: A system and computer-implemented method for detecting anomalies in industrial machine sensor data, including: computing, based on a received suspected anomalous level value of a sensory input data of each of the a plurality of sensory input data of a plurality of industrial machines that are located within a predetermined proximity, an average anomalous amount that is associated with at least a time interval; and determining that at least one of the plurality of suspected anomalies is an anomaly when a result of a subtraction of the computed average anomalous amount from each suspected anomalous level value of the plurality of sensory input data exceeds a predetermined threshold.

Abstract: A system and method for providing a corrective solution recommendation for an industrial machine failure, the method including: monitoring a plurality of segments of at least an industrial machine behavioral model to identify a first segment having at least a first set of characteristics associated with a previous machine failure; determining a corrective solution recommendation that solved the previous machine failure; identifying at least a second set of characteristics associated with a second segment; and generating a notification comprising the corrective solution recommendation when the second set of characteristics is determined to be similar to the first set of characteristics above a predetermined threshold.

Abstract: A system and method for recognizing and forecasting anomalous sensory behavioral patterns of a machine, including: monitoring a first set of time-stamped sensory input data related to at least one machine; determining, upon analysis of the first set of time-stamped sensory input data, a first suspicious pattern of a first anomalous sensory input behavior associated with the first set of time-stamped sensory input data; comparing the first suspicious pattern to a second pattern of a second anomalous sensory input behavior that is associated with a second set of time-stamped sensory input data, wherein the second pattern previously determined to be indicative of a machine failure; and, determining if the first suspicious pattern is correlated above a predetermined threshold with the second pattern.

Abstract: A system and computer-implemented method for identifying and repairing suboptimal operation of a machine, the computer-implemented method including: monitoring sensory input data related to an industrial machine; analyzing, using an unsupervised machine learning model, the monitored sensory inputs, wherein the output of the unsupervised machine learning model includes at least one indicator; identifying, based on the at least one indicator, at least one behavioral pattern related to the industrial machine, wherein each of the at least one behavioral pattern is indicative of at least one suboptimal operation of the industrial machine; selecting at least one corrective action based on the at least one behavioral pattern; and performing the at least one corrective action on the industrial machine.

Abstract: A system and method for validating unsupervised machine learning models. The method includes: analyzing, via unsupervised machine learning, a plurality of sensory inputs associated with a machine, wherein the unsupervised machine learning outputs at least one normal behavior pattern of the machine; generating, based on the at least one normal behavior pattern, at least one artificial anomaly, wherein each artificial anomaly deviates from the at least one normal behavior pattern; injecting the at least one artificial anomaly into the plurality of sensory inputs to create an artificial dataset; and analyzing the artificial dataset to determine whether a candidate model is a valid representation of operation of the machine, wherein analyzing the artificial dataset further comprises running the candidate model using the artificial dataset as an input.

Abstract: A system and method for forecasting failures in industrial machines, including: 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; 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.

Abstract: A system and method for validating unsupervised machine learning models. The method includes: analyzing, via unsupervised machine learning, a plurality of sensory inputs associated with a machine, wherein the unsupervised machine learning outputs at least one normal behavior pattern of the machine; generating, based on the at least one normal behavior pattern, at least one artificial anomaly, wherein each artificial anomaly deviates from the at least one normal behavior pattern; injecting the at least one artificial anomaly into the plurality of sensory inputs to create an artificial dataset; and analyzing the artificial dataset to determine whether a candidate model is a valid representation of operation of the machine, wherein analyzing the artificial dataset further comprises running the candidate model using the artificial dataset as an input.

Abstract: Disclosed herein a method and machine monitoring system for predicting failures of industrial machines. The system is configured to receive sensor data related to a machine, such as large industrial machinery, and select indicative data features for machine failures. The system then applies an unsupervised machine failure detection process and a supervised machine failure prediction process to the selected indicative data feature. When new sensor data of the machine is received, a machine failure detection process is applied to the selected at least one indicative data feature that is associated with the new sensor data. This allows the disclosed system to determine whether at least one machine failure indicator was detected and if so, the machine failure is tagged. Then, the system updates the supervised machine failure prediction process with the new tagged machine failure indicators, such that the supervised machine failure prediction process is continuously updated and improved.

Abstract: A system and method for unsupervised root cause analysis of machine failures. The method includes analyzing, via at least unsupervised machine learning, a plurality of sensory inputs that are proximate to a machine failure, wherein the output of the unsupervised machine learning includes at least one anomaly; identifying, based on the output at least one anomaly, at least one pattern; generating, based on the at least one pattern and the proximate sensory inputs, an attribution dataset, the attribution dataset including a plurality of the proximate sensory inputs leading to the machine failure; and generating, based on the attribution dataset, at least one analytic, wherein the at least one analytic includes at least one root cause anomaly representing a root cause of the machine failure.

Abstract: A system and method for unsupervised prediction of machine failures. The method includes monitoring sensory inputs related to at least one machine; analyzing, via at least unsupervised machine learning, the monitored sensory inputs, wherein the output of the unsupervised machine learning includes at least one indicator; identifying, based on the at least one indicator, at least one pattern; and determining, based on the at least one pattern and the monitored sensory inputs, at least one machine failure prediction.

Abstract: A system and method for unsupervised prediction of machine failures. The method includes monitoring sensory inputs related to at least one machine; analyzing, via at least unsupervised machine learning, the monitored sensory inputs, wherein the output of the unsupervised machine learning includes at least one indicator; identifying, based on the at least one indicator, at least one pattern; and determining, based on the at least one pattern and the monitored sensory inputs, at least one machine failure prediction.

Abstract: A system and method for recognizing and forecasting anomalous sensory behavioral patterns of a machine, including: monitoring a first set of time-stamped sensory input data related to at least one machine; determining, upon analysis of the first set of time-stamped sensory input data, a first suspicious pattern of a first anomalous sensory input behavior associated with the first set of time-stamped sensory input data; comparing the first suspicious pattern to a second pattern of a second anomalous sensory input behavior that is associated with a second set of time-stamped sensory input data, wherein the second pattern previously determined to be indicative of a machine failure; and, determining if the first suspicious pattern is correlated above a predetermined threshold with the second pattern.

Abstract: A system and method for a method for optimizing machine learning algorithms for monitoring industrial machine operation, including: 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.

Abstract: A system and method for providing a corrective solution recommendation for an industrial machine failure, the method including: monitoring a plurality of segments of at least an industrial machine behavioral model to identify a first segment having at least a first set of characteristics associated with a previous machine failure; determining a corrective solution recommendation that solved the previous machine failure; identifying at least a second set of characteristics associated with a second segment; and generating a notification comprising the corrective solution recommendation when the second set of characteristics is determined to be similar to the first set of characteristics above a predetermined threshold.

Abstract: A system and method for forecasting failures in industrial machines, including: 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; 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.

Abstract: A system and computer-implemented method for detecting anomalies in industrial machine sensor data, including: computing, based on a received suspected anomalous level value of a sensory input data of each of the a plurality of sensory input data of a plurality of industrial machines that are located within a predetermined proximity, an average anomalous amount that is associated with at least a time interval; and determining that at least one of the plurality of suspected anomalies is an anomaly when a result of a subtraction of the computed average anomalous amount from each suspected anomalous level value of the plurality of sensory input data exceeds a predetermined threshold.

Abstract: A system and computer-implemented method for identifying and repairing suboptimal operation of a machine, the computer-implemented method including: monitoring sensory input data related to an industrial machine; analyzing, using an unsupervised machine learning model, the monitored sensory inputs, wherein the output of the unsupervised machine learning model includes at least one indicator; identifying, based on the at least one indicator, at least one behavioral pattern related to the industrial machine, wherein each of the at least one behavioral pattern is indicative of at least one suboptimal operation of the industrial machine; selecting at least one corrective action based on the at least one behavioral pattern; and performing the at least one corrective action on the industrial machine.

Abstract: A system and method for unsupervised root cause analysis of machine failures. The method includes analyzing, via at least unsupervised machine learning, a plurality of sensory inputs that are proximate to a machine failure, wherein the output of the unsupervised machine learning includes at least one anomaly; identifying, based on the output at least one anomaly, at least one pattern; generating, based on the at least one pattern and the proximate sensory inputs, an attribution dataset, the attribution dataset including a plurality of the proximate sensory inputs leading to the machine failure; and generating, based on the attribution dataset, at least one analytic, wherein the at least one analytic includes at least one root cause anomaly representing a root cause of the machine failure.

Abstract: A system and method for allocating machine behavioral models. The method includes analyzing, via unsupervised machine learning, a plurality of sensory inputs associated with a machine, wherein the unsupervised machine learning outputs at least one normal behavior pattern of the machine; selecting, based on the output at least one normal behavior pattern, at least one machine behavioral model; generating, based on the selected at least one machine behavioral model, an optimal machine behavioral model representing behavior of the machine; and allocating the generated optimal machine behavioral model to the machine.

Abstract: A system and method for unsupervised prediction of machine failures. The method includes monitoring sensory inputs related to at least one machine; analyzing, via at least unsupervised machine learning, the monitored sensory inputs, wherein the output of the unsupervised machine learning includes at least one indicator; identifying, based on the at least one indicator, at least one pattern; and determining, based on the at least one pattern and the monitored sensory inputs, at least one machine failure prediction.