A computer-implemented method, environmental control system, and aircraft are provided. Environmental operating conditions data for a heat exchanger is received. Measured values from sensors measuring parameters of the heat exchanger are also received. An operating model for the heat exchanger based on the received environmental operating conditions is retrieved from a first data structure. The retrieved model includes estimated values for the sensors. A difference between the estimated values and the measured values is calculated. The calculated differences are compared to at least one threshold value. Upon the differences exceeding the at least one threshold value, the calculated differences are compared to abnormal operation models in an abnormal operation library data structure. Upon the calculated difference matching an abnormal operation model in the abnormal operation library data structure, outputting an abnormal operation identity associated with the abnormal operation model. If there is no match, a learning process develops a new model for a new abnormal operation.

An engine health diagnostic apparatus is provided for analysing health of a reciprocating internal combustion engine. The apparatus comprises feature generation circuitry for processing vibration sensor data received from a vibration sensor detecting vibration at a component of the reciprocating internal combustion engine 4 and generating a feature vector indicating multiple features of the sensor data. Processing circuitry processes the feature vector using a trained classification model which is defined by model parameters characterising a decision boundary of healthy operation learnt from a training set of feature vectors captured during healthy operation of the engine. The model generates an engine health indication providing a quantitative indication of deviation of the feature vector from the decision boundary of healthy operation.

A method and a device for estimating a state of a power system are provided. The method includes: dividing the power system into a plurality of sub-systems; establishing a first linear model of the power system for a first stage; solving the first linear model by an alternating direction multiplier method to obtain the intermediate state variables of each sub-system; performing a nonlinear transformation at a second stage on the intermediate state variables to obtain intermediate measured values; establishing a second linear model of the power system for a third stage according to the intermediate measured values; and solving the third linear model by the alternating direction multiplier method to obtain the final state variables of each sub-system.

An abnormality degree calculation system includes a concept classification assignment unit that assigns a predetermined concept classification based on an identification number of a target device, a feature value vector extraction unit that extracts a feature value vector based on sensor data of a sensor corresponding to the target device, a likelihood calculation unit that calculates a likelihood of the feature value vector by using a machine learning model obtained from a learning database, a loss calculation unit that calculates a loss using a loss function as a function of the likelihood, a model update unit that updates the model by using the loss and a model, a re-learning necessity determination unit that determines whether re-learning is necessary from the calculated likelihood when an abnormality of the target device is detected, and an abnormality degree calculation unit that calculates an abnormality degree when the re-learning is unnecessary.

A method and apparatus for analysing utility consumption at a utility supply location is described. The method comprises the steps of: receiving utility consumption data corresponding to utility consumption at the utility supply location over a time period to be analysed; generating a recurring consumption model indicative of repeating consumption patterns in the utility consumption data; identifying divergences between the utility consumption data and the recurring consumption model; computing a diagnostic measure indicative of irregular consumption based on the identified divergences; and outputting the diagnostic measure. The diagnostic measure may be used to identify flexibility or irregularities in consumption and/or to control supply of the utility. The utility may be e.g. electricity, gas or water.

System and methods for detecting anomalies and identifying faults of a gas turbine engine may include a recorder in communication with a processor. The recorder may be configured to capture archival data of the gas turbine engine. A flight normalizer module may be configured to produce normalized results based on the archival data. A flight parameter features module may be configured to generate flight parameter features based on the normalized results. A data warehouse module may be configured to determine suspected fault classes by comparing the flight parameter features against training parameter features stored in the data warehouse module based on queries from the flight parameter features module. A majority vote module may be configured to determine a diagnosed fault class based on the suspected fault classes.

A method for operating an industrial automation system may include receiving, via a first module of a plurality of modules in a control system, a plurality of datasets via at least a portion of the plurality of modules. The plurality datasets may include raw values without context regarding the plurality datasets. The method may then include identifying a subset of the plurality of datasets that influences a value of a target variable by analyzing the data without regard to the context, modeling a behavior of the target variable over time based on the subset of the plurality of datasets, and adjusting one or more operations of an automation device based on the model.

Historical aircraft data recorded during flight associated with a plurality of aircraft engines and a plurality of prior aircraft flights is accessed and metrics from the aircraft data recorded during flight is automatically calculated on a per-flight basis, and a probabilistic model is employed to capture and represent relationships based on the calculated metrics, the relationships including a plurality of engine parameters and flight parameters. Conditional probability distributions are then calculated for a particular aircraft engine during a potential or historical aircraft flight based on the probabilistic model, engine parameter values associated with the particular aircraft engine, and flight parameter values associated with the potential or historical aircraft flight, and indications associated with the calculated conditional probability distributions are displayed.

Techniques are provided for classifying runs of a recipe within a manufacturing environment. Embodiments monitor a plurality of runs of a recipe to collect runtime data from a plurality of sensors within a manufacturing environment. Qualitative data describing each semiconductor devices produced by the plurality of runs is determined. Embodiments characterize each run into a respective group, based on an analysis of the qualitative data, and generate a data model based on the collected runtime data. A multivariate analysis of additional runtime data collected during at least one subsequent run of the recipe is performed to classify the at least one subsequent run into a first group. Upon classifying the at least one subsequent run, embodiments output for display an interface depicting a ranking sensor types based on the additional runtime data and the description of relative importance of each sensor type for the first group within the data model.

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.