Examples described herein provide a computer-implemented method that includes receiving training data indicative of incipient compressor surge for cabin air compressors. The method further includes generating, using the training data, a training spectrogram. The method further includes training, by a processing system, a machine learning model to detect incipient compressor surge events for the cabin air compressors using the spectrogram. The method further includes receiving, at a microcontroller associated with a cabin air compressor, operating data associated with the cabin air compressor. The method further includes generating, at the microcontroller and using the operating data, an operating spectrogram. The method further includes detecting, by the microcontroller associated with the cabin air compressor, an incipient compressor surge event by applying the machine learning model to the operating spectrogram. The method further includes implementing a corrective action to correct the incipient compressor surge event.

A system includes a vertical charge pump assembly. The vertical charge pump assembly includes a top portion adjacent to a first end of the vertical charge pump assembly and a bottom portion adjacent to a second end of the vertical charge pump assembly. A pump motor is disposed in the top portion and an impeller is disposed in the bottom portion within a bowl casing. A shaft is disposed within a central passageway and connects the pump motor with the impeller. The vertical charge pump assembly also includes an inlet at the second end below the bowl casing. The pump inlet and the bowl casing are configured to be immersed in a fluid, and the vertical charge pump assembly is configured to pump the fluid into the inlet and upwards through the central passageway by rotation of the impeller. A vibration sensor is disposed on an external surface of the bottom portion, on or proximate to the bowl casing and the pump inlet. The vibration sensor includes a substrate comprising a polymer and a resonant layer disposed on a surface of the substrate. The resonant layer comprises an electrically conductive nanomaterial and is configured to produce a resonant response in response to receiving a radio frequency signal.

A method for stability analysis of a combustion chamber of a gas turbine engine based on image sequence analysis belongs to the field of fault prediction and health management of aeroengine. Firstly, flow field data inside a combustion chamber of a gas turbine engine is acquired. Secondly, flow field images of the combustion chamber are preprocessed to respectively obtain a discrimination model data set and a prediction model data set. Then, a 3DWaveNet model is constructed as a generation network of a prediction model. A discrimination network of the module is constructed. The generation network and the discrimination network are combined to form the prediction model. Finally, a discrimination model is constructed according to the discrimination model data set; the training set in the discrimination model data set is used for training, and the test set is used for assessment.

Systems and methods for optimizing clearances within an engine include an adjustable coupling configured to couple a thrust link to the aircraft engine, an actuator coupled to the adjustable coupling, where motion produced by the actuator adjusts a hinge point of the adjustable coupling, sensors configured to capture real time flight data, and an electronic control unit. The electronic control unit receives flight data from the sensors, implements a machine learning model trained to predict clearance values within the engine based on the received flight data, predicts, with the machine learning model, the clearance values within the engine based on the received flight data, determines an actuator position based on the clearance values, and causes the actuator to adjust to the determined actuator position.

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.

Systems and methods for real-time monitoring and control of well site operations employ well site edge analytics to detect abnormal operations. The systems and methods receive well site data from a remote programmable automation (PAC) controller at the well site, the well site data representing one or more operational parameters related to the well site operations. A probability is derived for a given slope for each one of the one or more operational parameters as correlated to a different one of the one or more operational parameters to produce correlated probabilities for the one or more operational parameters. A resultant probability is derived from the correlated probabilities for the one or more operational parameters and it is determined whether the resultant probability meets a preselected threshold probability value. A responsive action is initiated if the resultant probability fails to meet the preselected threshold probability value.

A control system for turbine systems configured to provide accurate interpretations of detected particle accumulation, improve performance of turbine systems, and/or minimize costs due to downtime and maintenance are disclosed. The control system may build an intelligent model of fluid flow based on measured data provided by a sensor in a fluid flow path of the turbine system. The intelligent model consults a filter efficiency framework and determines an impact value that quantifies an operational efficiency of the turbine system and may identify a location of possible leakage, estimate a total amount of ingress of particles, identify components of the turbine system that may be operating in a diminished capacity, estimate a risk of damage to components of the turbine system, and/or recommend mitigation actions.

A sign detection device is provided with a plurality of sensors, a data acquisition unit, a calculation unit, and a detection unit. The plurality of sensors are respectively disposed at a plurality of positions of a detection target to measure a physical parameter at each positions.

The data acquisition unit is configured to acquire time-series change data of the physical parameters from the plurality of sensors. The calculation unit is configured to calculate dynamic network information representing a time change of a complex network structure including a parameter indicating a correlation between the physical parameters at any two positions of the plurality of positions, on the basis of the time-series change data. The detection unit is configured to detect a sign of sudden change vibration of the detection target, on the basis of the dynamic network information.

Systems and methods for predicting a surge event in a compressor of a turbomachine are provided. According to one embodiment of the disclosure, a system may include one or more computer processors associated with the turbomachine. The one or more computer processors may be operable to receive a plurality of performance parameters of the compressor and analyze the plurality of performance parameters to determine corrected performance values of the performance parameters. Based at least partially on the corrected performance values, a compressor efficiency may be determined. The processor may be further operable to standardize the compressor efficiency for a standard mode of operation, ascertain historical performance data associated with the standard mode of operation, and analyze the compressor efficiency based at least partially on the historical performance data. Based on the analysis of the compressor efficiency, a surge event may be selectively predicted.

A combustion control system and method for a turbulent jet ignition engine is presented. A controller is configured to access a trained feedforward artificial neural network (ANN) configured to model a first spark from a first ignition source and maximum brake torque (MBT) based on measured operating parameters, generate the first spark and MBT using the ANN, generate a second spark from a second ignition device, and determine a target spark timing. The ANN can be further configured to receive an input related to spark stagger.