A measuring system is provided that includes a turbine engine thrust estimator that computes “virtual measurements” of dynamic engine thrust and other parameters of interest from test cell data in a very short amount of time. The measuring system ‘tunes’ a user's engine model, in a numerical propulsion system simulation, by optimizing system biases and health parameters to match the sensor outputs of a set of steady state data points across the operating range. The tuned model is then utilized by the measuring system to create a constant gain extended Kalman filter that is added directly within a code of the numerical propulsion system simulation. Results, including thrust, from the numerical propulsion system simulation with Kalman filter are then presented as ‘actual’ corrected data.

A system for automatically learning and adapting to the energy usage of an equipment installed at a facility or many pieces of equipment at a plurality of facilities, where the system is provided with an initial baseline energy usage signature for the equipment, which is modified by measured energy usage and by at least one peripheral sensor measurement data to create a modified energy usage signature. The system uses artificial intelligence to learn and adapt the baseline energy usage signature to learn the business operation and account for external variables such as temperature variance and increased business flow or an interaction between devices. The smart system can identify when a piece of equipment falls outside of “normal” operation and determines what automatic action is to be taken for that piece of equipment.

Methods and systems of predicting the growth rate of hydrogen-induced cracking (HIC) in a physical asset (e.g., a pipeline, storage tank, etc.) are provided. The methodology receives a plurality of inputs regarding physical characteristics of the asset and performs parametric simulations to generate a simulated database of observations of the asset. The database is then used to train, test, and validate one or more expert systems that can then predict the growth rate and other characteristics of the asset over time. The systems herein can also generate alerts as to predicted dangerous conditions and modify inspection schedules based on such growth rate predictions.

A method for determining a failure mode of a comfort device, the method includes obtaining a first input during a first operation of the comfort device, wherein the first operation is a known normal operation; classifying the first input into a class including a series of attributes and storing the first input in a database of input classes; obtaining a second input during a second operation of the comfort device; and classifying the second input into a class including a series of attributes and comparing the class of the second input to the class of the first input in a first comparison, wherein if a match exists, comparing the series of attributes of the second input to the series of attributes of the first input in a second comparison, wherein if a discrepancy is detected, a warning is raised.

A sensor data stream is provided consisting of feature vectors acquired by sensors of rotating equipment, similar feature vectors are aggregated in microclusters. For newly arriving feature vectors, a correlation distance measure between the new feature vector and each microcluster is calculated. If there is no microcluster in range, then a new microcluster is created. Otherwise, the feature vector is assigned to the best fitting microcluster, and the necessary statistical information is incorporated into the aggregation contained in the microcluster. In other words, similar feature vectors are aggregated in the same microclusters. The microclusters thus provide a generic summary structure that captures the necessary statistical information of the incorporated feature vectors. At the same time, the loss of accuracy is quite small. Clustering the sensor data stream with microclusters has the benefit that the computational complexity can be reduced significantly.

A machine monitoring device is provided which includes (a) a communications link to interrogate a machine with a probe signal and receive one or more measured machine operating condition outputs; and (b) a device controller capable of (i) selecting a machine operating condition input variable for which a corresponding machine operating condition output is unknown; (ii) applying a predictive model in which the machine operating condition input variable serves as an argument of a predicted machine operating condition output; (iii) updating a library of predicted machine operating condition outputs; and (iv) alerting a human operator if a measured or predicted machine operating condition output exceeds a predetermined limit. The predictive model is based on at least two independent primary models having at least one correspondence between a primary model machine operating condition and a corresponding machine output. The primary models share a common basis in the predictive model.

A building management system includes building equipment operable to affect a variable state or condition of a building and a controller including a processing circuit. The processing circuit is configured to obtain a vibration data set related to vibrations of the building equipment and analyze the vibration data set by one or more machine learning models to generate a set of probabilities. The set of probabilities is related to a probability that the vibration data set is abnormal. The processing circuit is configured to identify the vibration data set as normal or abnormal based on the set of probabilities and initiate a corrective action responsive to identifying the vibration data set as abnormal.

Methods and systems of predicting the growth rate of hydrogen-induced cracking (HIC) in a physical asset (e.g., a pipeline, storage tank, etc.) are provided. The methodology receives a plurality of inputs regarding physical characteristics of the asset and performs parametric simulations to generate a simulated database of observations of the asset. The database is then used to train, test, and validate one or more expert systems that can then predict the growth rate and other characteristics of the asset over time. The systems herein can also generate alerts as to predicted dangerous conditions and modify inspection schedules based on such growth rate predictions.

A method and system includes predicting energy consumption of a vehicle using a statistical model. The method includes obtaining a plurality of input vectors for plurality of points in time, wherein each input vector includes a plurality of variables with a weight vector. Thereafter, the energy level for each input vector is captured for each point in time. Subsequent to capturing the energy level, the method includes predicting a change in energy level of the vehicle using the statistical model.

A method for operating a machine component under stress. The method comprises determining a probability of failure PoF(N) of the component as a function of N cycles, selecting a time-based acceptable risk limit for the component and selecting an operational profile for the component, converting the time-based acceptable risk limit to a cycle-based acceptable risk limit using the operational profile, comparing the cycle-based acceptable risk limit with the PoF(N) values to determine an operational status of the component, comparing the cycle-based acceptable risk limit with the PoF(N) values, and operating the machine component responsive to results of the comparing step.