Abstract: A computerized device, computer program product and method for performing a range search in numeric time-series data that includes a) acquiring, at least temporarily, a numeric time-series including a plurality of readings associated with time; b) processing, by a first processing unit, the acquired numeric time-series to derive an index from the acquired numeric time-series; c) storing the index in a storage unit; d) receiving a search request including an amplitude range criterion; e) processing, by a second processing unit, the index to determine a time range for which the numeric time-series is known to match the amplitude range criterion; and f) outputting the determined time range in response to the search request, whereby the processing time for determining the time range in response to the range search request may be significantly reduced.

Abstract: A method and machine for validating an investigated sensor within a sensor group includes receiving sensor data from sensors of the sensor group measuring the same physical property, calculating a deviation of the sensor data received from the investigated sensor within the sensor group from sensor data reconstructed based on the sensor data received from all other sensors of the sensor group, and signaling a sensor fault of the investigated sensor if the calculated deviation is outside of a trusted range.

Abstract: A method and machine for validating an investigated sensor within a sensor group includes receiving sensor data from sensors of the sensor group measuring the same physical property, calculating a deviation of the sensor data received from the investigated sensor within the sensor group from sensor data reconstructed based on the sensor data received from all other sensors of the sensor group, and signaling a sensor fault of the investigated sensor if the calculated deviation is outside of a trusted range.

Abstract: A method for computer-assisted analyzing of a technical system is provided. The technical system is described by a case base including multiple cases, each case including a state vector with a number of attributes, the state vector referring to an operation state of the technical system, wherein a class from a number of classes is assigned to each case, each class referring to an operation condition of the technical system. Each case is processed by extracting a local information vector depending on the classes of one or more neighboring cases in the case base, the neighboring cases being similar to the case being processed according to a neighborhood measure. Subsequently, machine learning of a classification is performed based on the extracted local information vectors of the cases in the case base, resulting in a learned adaptation function providing a class depending on a local information vector extracted for a case.