Abstract: Embodiments match sensor data output by a sensor to a trained pattern. Embodiments form a plurality of windows of an identified pattern from the sensor data, each of the plurality of windows having a substantially equal window length to a length of the trained pattern. For each of the windows, embodiments generate a corresponding first Symbolic Aggregate approximation (“SAX”) word, determine a Hamming distance between the first SAX word and a second SAX word corresponding to the trained pattern, and determine a final distance score based on coefficients between the first SAX word and the second SAX word. For each of the windows, embodiments determine a number of positions in the first SAX word that do not contribute to the final distance score, update the Hamming distance after eliminating the number of positions and determine an average distance based on the final distance score and the updated Hamming distance.

Abstract: Embodiments match sensor data output by a sensor to a trained pattern. Embodiments form a plurality of windows of an identified pattern from the sensor data, each of the plurality of windows having a substantially equal window length to a length of the trained pattern. For each of the windows, embodiments generate a corresponding first Symbolic Aggregate approximation (“SAX”) word, determine a Hamming distance between the first SAX word and a second SAX word corresponding to the trained pattern, and determine a final distance score based on coefficients between the first SAX word and the second SAX word. For each of the windows, embodiments determine a number of positions in the first SAX word that do not contribute to the final distance score, update the Hamming distance after eliminating the number of positions and determine an average distance based on the final distance score and the updated Hamming distance.

Abstract: Techniques for identifying anomalous multi-source data points and ranking the contributions of measurement sources of the multi-source data points are disclosed. A system obtains a data point including a plurality of measurements from a plurality of sources. The system determines that the data point is an anomalous data point based on a deviation of the data point from a plurality of additional data points. The system determines a contribution of two or more measurements, from the plurality of measurements, to the deviation of the data point from the plurality of additional data points. The system ranks the at least the two or more measurements, from the plurality of measurements, based on the respective contribution of each of the two or more measurements to the deviation of the anomalous data point from the plurality of prior data points.

Abstract: Embodiments determine anomalies in sensor data generated by a sensor by receiving an evaluation time window of clean sensor data generated by the sensor. Embodiments receive a threshold value for determining anomalies. When the clean sensor data has a cyclic pattern, embodiments divide the evaluation time window into a plurality of segments of equal length, wherein each equal length comprises the cyclic pattern. When the clean sensor data does not have the cyclic pattern, embodiments divide the evaluation time window into a pre-defined number of plurality of segments of equal length. Embodiments convert the evaluation time window and each of the plurality of segments into corresponding curves using Kernel Density Estimation (“KDE”). For each of the plurality of segments, embodiments determine a Kullback-Leibler (“KL”) divergence value between corresponding curves of the segment and the evaluation time window to generate a plurality of KL divergence values.

Abstract: Embodiments match sensor data output by a sensor to a trained pattern. Embodiments form a plurality of windows of an identified pattern from the sensor data, each of the plurality of windows having a substantially equal window length to a length of the trained pattern. For each of the windows, embodiments generate a corresponding first Symbolic Aggregate approximation (“SAX”) word, determine a Hamming distance between the first SAX word and a second SAX word corresponding to the trained pattern, and determine a final distance score based on coefficients between the first SAX word and the second SAX word. For each of the windows, embodiments determine a number of positions in the first SAX word that do not contribute to the final distance score, update the Hamming distance after eliminating the number of positions and determine an average distance based on the final distance score and the updated Hamming distance.

Abstract: Embodiments determine anomalies in sensor data generated by a plurality of sensors that correspond to a single asset. Embodiments receive a first time window of clean sensor input data generated by the sensors, the clean sensor data including anomaly free data comprised of clean data points. Embodiments divide the clean data points into training data points and evaluation data points, and divide the training data points into a pre-defined number of plurality of segments of equal length. Embodiments convert each of the plurality of segments into corresponding segment curves using Kernel Density Estimation (“KDE”) and determine a Jensen-Shannon (“JS”) divergence value for each of the plurality of segments using the segment curves to generate a plurality of JS divergence values. Embodiments then assign the maximum value of the plurality of JS divergence values as a threshold value and validate the threshold value using the evaluation data points.

Abstract: Techniques for identifying anomalous multi-source data points and ranking the contributions of measurement sources of the multi-source data points are disclosed. A system obtains a data point including a plurality of measurements from a plurality of sources. The system determines that the data point is an anomalous data point based on a deviation of the data point from a plurality of additional data points. The system determines a contribution of two or more measurements, from the plurality of measurements, to the deviation of the data point from the plurality of additional data points. The system ranks the at least the two or more measurements, from the plurality of measurements, based on the respective contribution of each of the two or more measurements to the deviation of the anomalous data point from the plurality of prior data points.

Abstract: Embodiments determine anomalies in sensor data generated by a plurality of sensors that correspond to a single asset. Embodiments receive a first time window of clean sensor input data generated by the sensors, the clean sensor data including anomaly free data comprised of clean data points. Embodiments divide the clean data points into training data points and evaluation data points, and divide the training data points into a pre-defined number of plurality of segments of equal length. Embodiments convert each of the plurality of segments into corresponding segment curves using Kernel Density Estimation (“KDE”) and determine a Jensen-Shannon (“JS”) divergence value for each of the plurality of segments using the segment curves to generate a plurality of JS divergence values. Embodiments then assign the maximum value of the plurality of JS divergence values as a threshold value and validate the threshold value using the evaluation data points.

Abstract: Embodiments determine anomalies in sensor data generated by a sensor. Embodiments receive a first time window of clean sensor data generated by the sensor, the clean sensor data including anomaly free data, and determine if the clean sensor data includes a cyclic pattern. When the clean sensor data has a cyclic pattern, embodiments divide the first time window into a plurality of segments of equal length, where each equal length includes the cyclic pattern. Embodiments convert the first time window and each of the plurality of segments into corresponding curves using Kernel Density Estimation (“KDE”). For each of the plurality of segments, embodiments determine a Kullback-Leibler (“KL”) divergence value between corresponding curves of the segment and the first time window to generate a plurality of KL divergence values.

Abstract: Embodiments determine anomalies in sensor data generated by a sensor by receiving an evaluation time window of clean sensor data generated by the sensor. Embodiments receive a threshold value for determining anomalies. When the clean sensor data has a cyclic pattern, embodiments divide the evaluation time window into a plurality of segments of equal length, wherein each equal length comprises the cyclic pattern. When the clean sensor data does not have the cyclic pattern, embodiments divide the evaluation time window into a pre-defined number of plurality of segments of equal length. Embodiments convert the evaluation time window and each of the plurality of segments into corresponding curves using Kernel Density Estimation (“KDE”). For each of the plurality of segments, embodiments determine a Kullback-Leibler (“KL”) divergence value between corresponding curves of the segment and the evaluation time window to generate a plurality of KL divergence values.

Abstract: Embodiments determine anomalies in sensor data generated by a sensor. Embodiments receive a first time window of clean sensor data generated by the sensor, the clean sensor data including anomaly free data, and determine if the clean sensor data includes a cyclic pattern. When the clean sensor data has a cyclic pattern, embodiments divide the first time window into a plurality of segments of equal length, where each equal length includes the cyclic pattern. Embodiments convert the first time window and each of the plurality of segments into corresponding curves using Kernel Density Estimation (“KDE”). For each of the plurality of segments, embodiments determine a Kullback-Leibler (“KL”) divergence value between corresponding curves of the segment and the first time window to generate a plurality of KL divergence values.