Disclosed are a method for automatically diagnosing and controlling an apparatus in an intelligent building and relevant device. The method includes: performing, based on historical data of working parameters of multiple apparatuses, an abnormal diagnosis on received real-time data of the working parameters; determining an abnormal apparatus; selecting a neural network predictive control model corresponding to the abnormal apparatus; selecting one piece of non-abnormal data which has a same parameter type as that of the abnormal data and is close to the current abnormal data in time as a predictive control target, and determining a predictive control data that can cause an output matching the predictive control target; and controlling the abnormal apparatus according to the predictive control data. The automatic diagnosis and automatic control of an apparatus in an intelligent building are realized, meanwhile the safe and efficient operation of all apparatuses in an intelligent building is ensured.

A device for monitoring a state of a propulsion engine includes an acquisition module that acquires data of flights of the propulsion engine, comprising, for each flight, values of input variables, environment variables, and output variables of the propulsion engine during the flight, a learning module that computes, by learning from the data of each flight, an individual flight model for the flight, a using module that computes, for each flight, estimates of the values of the output variables, by applying the individual flight model to reference values of the input variables and the environment variables, and an error associated with the estimates of the values of the output variables that is obtained by applying the individual flight model to the reference values of the input variables and the environment variables. The reference values belong to a set of reference data, which are identical for the individual flight models.

A vehicle fault detection system including at least one sensor configured for coupling with a vehicle system, a vehicle control module coupled to the at least one sensor, and being configured to receive at least one time series of numerical sensor data from the at least one sensor, at least one of the at least one time series of numerical sensor data corresponds to a respective system parameter of the vehicle system being monitored, generate a graphical representation for the at least one time series of numerical sensor data to form an analysis image of at least one system parameter, and detect anomalous behavior of a component of the vehicle system based on the analysis image, and a user interface coupled to the vehicle control module, the user interface being configured to present to an operator an indication of the anomalous behavior for the component of the vehicle system.

A computer readable network analysis program of performing local modeling analysis of determining an estimated value of a current network quality corresponding to explanatory variable vector in current aggregated data based on a local model including local training data; determining an abnormality in the network based on whether or not a measured value of the current network quality is lower than a threshold; determining whether or not a distribution of the connections having the measured value of the network quality exceeding the threshold is present in a large size; extracting an individual-analysis-target connection group including more than predetermined proportions of connections in the distribution of the connections having the large size; and performing the local modeling analysis to the individual-analysis-target connection group and the remaining connection groups to determine the abnormality in the network.

A computer-implemented method comprising: controlling input of data quantifying damage received by a compressor of a gas turbine engine into a first machine learning algorithm; receiving data quantifying a first operating parameter of the compressor as an output of the first machine learning algorithm; and determining operability of the compressor by comparing the received data quantifying the first operating parameter of the compressor with a threshold.

A method and a system for fatigue life prediction of additive manufactured components accounting for localized material properties. The method and the system is employed for prediction of fatigue life properties of an additive manufactured element, with a data collection step in which several data points for maximum stress vs. cycles to failure for different given processing steps of the element are collected, with a training step in which a Machine Learning system is trained with the collected data, and with an evaluation step in which the trained Machine Learning system is confronted with actual processing steps and used to predict the fatigue life properties of the element.

Systems, apparatuses, and methods include technology that identifies a first dataset that comprises a plurality of data values, and partitions the first dataset into a plurality of bins to generate a second dataset, where the second dataset is a compressed version of the first dataset. The technology randomly subsamples data associated with the first dataset to obtain groups of randomly subsampled data, and generates a plurality of decision tree models during an unsupervised learning process based on the groups of randomly subsampled data and the second dataset.

A method operates a technical or non-technical system. At least one information element of a first type which relates to the system and is dependent on the respective system state of the system is transmitted according to the method from at least one first facility of the system to at least one second facility. The second facility uses at least one information element of a second type which originates neither from the first facility nor from a different facility of the system, i.e. it comes from a source other than the system, to estimate the system state, checks, on the basis of the estimated system state, whether the received information matches the estimated system state to a predefined extent, and, in the event of a match to the predefined extent, regards the information as trusted, and otherwise generates a warning signal indicating a possible data attack.

Systems, methods and apparatus of optimizing neural network computations of predictive maintenance of vehicles. For example, a data storage device of a vehicle includes: a host interface configured to receive a sensor data stream from at least one sensor configured on the vehicle; at least one storage media component having a non-volatile memory; and a controller. The non-volatile memory is configured into multiple partitions (e.g., namespaces) having different sets of memory operation settings configured for different types of data related to an artificial neural network (ANN). The partitions include a model partition configured to store model data of the ANN. The sensor data stream is applied in the ANN to predict a maintenance service of the vehicle. The memory units of the model partition can be configured for read, infrequent updates, improved storage capacity, and/or for access in parallel with input/output for the ANN.

Systems, program products, and methods for detecting thermal stability within gas turbine systems are disclosed. The systems may include a computing device(s) in communication with a gas turbine system, and a plurality of sensors positioned within or adjacent the gas turbine system. The sensor(s) may measure operational characteristics of the gas turbine system. The computing device(s) may be configured to detect thermal stability within the gas turbine system by performing processes including calculating a lag output for each of the plurality of measured operational characteristics. The calculated lag output may be based on a difference between a calculated lag for the measured operational characteristics and the measured operational characteristic itself. The calculated lag output may be also be based on a time constant for the measured operational characteristics. The computing device(s) may also determine when each of the calculated lag outputs are below a predetermined threshold.