Abstract: A system comprising a distribution grid (2) for a fuel, combustion engines (3), which are coupled with the distribution grid (2) and are configured to combust the fuel, and a computer system (4) comprising data connections (5) to the combustion engines (3) and a data storage device (6), wherein the computer system (4) is configured to receive engine operation parameters stemming from an operation of the combustion engines (3) at a first time and/or during a first time period via the data connections (5) and geographical data of the combustion engines (3) are stored in the data storage device (6), wherein the computer system (4) has a processor (7) which is configured to compute a prediction for at least one characteristic parameter of the fuel at a second time and/or during a second time period later than the first time and/or the first time period and with respect to a geographical location, and the computation of the prediction being based on the geographical data and the engine operation parameters of t

Abstract: A system comprising a distribution grid (2) for a fuel, combustion engines (3), which are coupled with the distribution grid (2) and are configured to combust the fuel, and a computer system (4) comprising data connections (5) to the combustion engines (3) and a data storage device (6), wherein the computer system (4) is configured to receive engine operation parameters stemming from an operation of the combustion engines (3) at a first time and/or during a first time period via the data connections (5) and geographical data of the combustion engines (3) are stored in the data storage device (6), wherein the computer system (4) has a processor (7) which is configured to compute a prediction for at least one characteristic parameter of the fuel at a second time and/or during a second time period later than the first time and/or the first time period and with respect to a geographical location, and the computation of the prediction being based on the geographical data and the engine operation parameters of the

Abstract: According to some embodiments, a system and method are provided to classify an anomaly. The method comprises receiving, from an anomaly detection system, time-series data that comprises one or more anomalies. The time-series data is grouped into a plurality of groups based on a scale range. For each group of the plurality of groups, statistical features are extracted from the time-series data. The extracted statistical features associated with the plurality of groups are combined and the one or more anomalies are classified based on the combined extracted statistical features.

Abstract: According to some embodiments, a system and method are provided to receive a first plurality of data from a machine associated with a first time period. A normal operation of the machine is automatically determined based on the first plurality of data. A second plurality of data may be received from the machine associated with a second time period. An anomaly in the second plurality of data is determined.

Abstract: According to some embodiments, a system and method are provided to classify an anomaly. The method comprises receiving, from an anomaly detection system, time-series data that comprises one or more anomalies. The time-series data is grouped into a plurality of groups based on a scale range. For each group of the plurality of groups, statistical features are extracted from the time-series data. The extracted statistical features associated with the plurality of groups are combined and the one or more anomalies are classified based on the combined extracted statistical features.

Abstract: According to some embodiments, a system and method are provided to receive a first plurality of data from a machine associated with a first time period. A normal operation of the machine is automatically determined based on the first plurality of data. A second plurality of data may be received from the machine associated with a second time period. An anomaly in the second plurality of data is determined.

Abstract: The present embodiments relate to a system and method associated with anomaly classification. The method comprises receiving a plurality of time-series data from one or more sensors associated with a machine. The time-series data may be automatically passed through a convolutional neural network to determine reduced dimension data. An anomaly based on classifying the reduced dimension data may be automatically determined. In a case that the anomaly is an unknown anomaly, the determined anomaly may be labeled and the determined anomaly and its associated label may be stored in an anomaly training database.