A device for detecting anomalies in an aircraft turbine engine by acoustic analysis, the device not an onboard device and including: a mobile module including a directional system for acquiring acoustic signals from the turbine engine; a processor for processing the signals, which is suitable for generating a damage report; a transmitter for transmitting the damage report; a server capable of exchanging data with the mobile module, the server including a receiver for receiving the damage report; and a storage device suitable for storing the damage report.

A method for monitoring the operation of a turbomachine controlled by a digital control system including at least one component, includes acquiring operating state information relating to the state of at least one component; determining, depending on the state information acquired, a current degraded configuration in which at least one of the components has failed; determining a classification of the current degraded configuration using at least one classification table stored in a storage device, the classification tables associating with at least one degraded configuration one classification expressing the level of criticality of the degraded configuration, the tables being obtained by calculating a conditional probability of a predefined anticipated event from the probability of occurrence of elementary events relating to a failure of one of the components; and estimating an operating time permitted for the turbomachine depending on the classification determined for the current degraded configuration.

Monitoring structure and a monitoring method for monitoring at least one step of a process performed on an industrial site is described, which allows prompt, reliable and efficient detection of disturbances based on readily available information, provided by the measurement results of the measurement devices involved in the performance of the step, and determined based on a model of the step, comprising primary models for the measurement devices comprising a basic function representing a time dependency of the measurement results of the device, a first property determined by best fitting the basic function to the measurement results obtained by the respective measurement device, a secondary model for each of the primary models, each comprising a second property given by a dispersion of residues between the measurement results obtained by the respective measurement device and the corresponding fitted function, and tertiary models for those pairs of measurement devices, which render correlated measurement results during faultless performance of the respective step, each tertiary model comprising a third property, given by a degree of correlation between simultaneously obtained measurement results of the respective pair of measurement devices during performance of the respective step, and a reference range for each of the properties, comprising a range within which the respective property is expected to occur during faultless performance of the step.

Disclosed herein are systems, devices, and methods related to assets and predictive models and corresponding workflows that are related to the operation of assets. In particular, examples involve assets configured to receive and locally execute predictive models, locally individualize predictive models, and/or locally execute workflows or portions thereof.

The speed and security of the monitoring the operation of a drive component is improved by transferring data relating to the drive component and/or to the operation of the drive component to a central IT infrastructure. Within the central IT infrastructure, the transferred data are associated with a first model of the drive component, and with a second model of at least one virtual component associated with the first model. An operating state of the drive component is determined from a correlation of the first and second models.

A control system may include a fault detection system, an electromechanical actuator, the electromechanical actuator electronically coupled to the fault detection system, and a failure simulation apparatus mechanically coupled between the electromechanical actuator and a load, the failure simulation apparatus selectively applies an external resistive force to the electromechanical actuator.

A system collects training data in order to train a determination model of artificial intelligence that determines an abnormality of an industrial machine. The system includes a storage device and a processor. The storage device stores state data indicative of a state of the industrial machine acquired in time series. The processor determines an occurrence of a trigger related to an occurrence of the abnormality in the industrial machine, and extracts data corresponding to the trigger from the state data when the trigger occurs. The processor stores the data corresponding to the trigger as the training data.

A method includes receiving a first time-dependent data characterizing measurement by a first sensor operatively coupled to an oil and gas industrial machine; determining a first anomaly score associated with a first portion of the first time-dependent data over a time period, the determination is based on a first value of an operating characteristic over the time period and a second value of the operating characteristic over the time period, wherein the first value of the operating characteristic is calculated from the first time-dependent data and the second value of the operating characteristic is detected at the oil and gas industrial machine; and rendering, in a graphical user interface display space, a visual representation indicative of the first anomaly score. Related apparatus, systems, articles, and techniques are also described.

The present invention discloses a method for fault diagnosis of the sensors and actuators of an aero-engine based on LFT, and belongs to the field of fault diagnosis of aero-engines. The method comprises: establishing an aero-engine state space model using a combination of a small perturbation method and a linear fitting method; establishing an affine parameter-dependent linear-parameter-varying (LPV) model of the aero-engine based on the model; converting the LPV model of the aero-engine having perturbation signals and sensor and actuator fault signals into a linear fractional transformation (LFT) structure to obtain an synthesis framework of an LPV fault estimator; solving a set of linear matrix inequalities (LMIs) to obtain the solution conditions of the fault estimator; and designing the fault estimator in combination with the LFT structure to realize fault diagnosis of the sensors and actuators of an aero-engine.

Diagnosing whether controllers of internal vehicle systems are the source of failures detected by a system control managing a vehicle such as a spacecraft. Highspeed data is received via at a field programmable gate array (FPGA) embedded in an assembly of the vehicle. The FPGA includes a controller and a digital diagnostic interface. In one embodiment, the diagnostic interface utilizes Very Highspeed Integrated Circuit (VHSIC) Hardware Description Language (VHDL) for performance modeling of a controller configured to control at least one internal system within the vehicle. The VHDL performance models the controller. Upon receiving an indication of a failure, the performance modeling of the controller is used to ascertain whether or not the controller is the source of the failure. Disassembly of the assembly housing the internal system is not required in order to ascertain whether or not the controller is the source of the failure.