Abstract: According to some embodiments, a system, method and non-transitory computer-readable medium are provided to protect a decision manifold of a control system for an industrial asset, comprising: a detection and neutralization module including: a decision manifold having a receiver configured to receive a training dataset comprising data, wherein the decision manifold is operative to generate a first decision manifold with the received training dataset; and a detection model; a memory for storing program instructions; and a detection and neutralization processor, coupled to the memory, and in communication with the detection and neutralization module and operative to execute program instructions to: receive the first decision manifold, wherein the first decision manifold separates a normal operating space from an abnormal operating space; determine whether there are one or more inadequacies with the detection model; generate a corrected decision manifold based on the determined one or more inadequacies with the

Abstract: Input signals may be received from monitoring nodes of the industrial asset, each input signal comprising time series data representing current operation. A neutralization engine may transform the input signals into feature vectors in feature space, each feature vector being associated with one of a plurality of overlapping batches of received input signals. A dynamic decision boundary may be generated based on the set of feature vectors, and an abnormal state of the asset may be detected based on the set of feature vectors and a predetermined static decision boundary. An estimated neutralized value for each abnormal feature value may be calculated based on the dynamic decision boundary and the static decision boundary such that a future set of feature vectors will be moved with respect to the static decision boundary. An inverse transform of each estimated neutralized value may be performed to generate neutralized signals comprising time series data that are output.

Abstract: According to some embodiments, a system, method and non-transitory computer-readable medium are provided to protect a decision manifold of a control system for an industrial asset, comprising: a detection and neutralization module including: a decision manifold having a receiver configured to receive a training dataset comprising data, wherein the decision manifold is operative to generate a first decision manifold with the received training dataset; and a detection model; a memory for storing program instructions; and a detection and neutralization processor, coupled to the memory, and in communication with the detection and neutralization module and operative to execute program instructions to: receive the first decision manifold, wherein the first decision manifold separates a normal operating space from an abnormal operating space; determine whether there are one or more inadequacies with the detection model; generate a corrected decision manifold based on the determined one or more inadequacies with the

Abstract: According to some embodiments, a system, method and non-transitory computer-readable medium are provided comprising one or more heterogeneous data source nodes generating data associated with operation of the medical device; an abnormal state detection, prediction and correction module to receive data from one or more heterogeneous data source nodes; a memory for storing program instructions; and an abnormal state processor, coupled to the memory, and in communication with the abnormal state detection, prediction and correction module and operative to execute program instructions to: receive data from one or more heterogeneous data source nodes; receive a decision manifold separating a normal operating space from an abnormal operating space; perform a feature extraction process on the received data to generate at least one feature vector; determine, via the abnormal state detection, prediction and correction module, whether the feature vector maps to the normal operating space or the abnormal operating space

Abstract: Input signals may be received from monitoring nodes of the industrial asset, each input signal comprising time series data representing current operation. A neutralization engine may transform the input signals into feature vectors in feature space, each feature vector being associated with one of a plurality of overlapping batches of received input signals. A dynamic decision boundary may be generated based on the set of feature vectors, and an abnormal state of the asset may be detected based on the set of feature vectors and a predetermined static decision boundary. An estimated neutralized value for each abnormal feature value may be calculated based on the dynamic decision boundary and the static decision boundary such that a future set of feature vectors will be moved with respect to the static decision boundary. An inverse transform of each estimated neutralized value may be performed to generate neutralized signals comprising time series data that are output.