A method, apparatus and computer program product are provided to determine a measure of probe quality, such as a level of accuracy with which the location of a probe point is defined. In the context of a method and for a plurality of probe points, a distance is determined between a respective probe point and a line representative of a link. For the plurality of probe points, the method also includes determining a difference between a bearing of the respective probe point and a bearing of the link. The method further includes determining the measure of probe quality based upon a product of a representation of the distance between the plurality of probe points and the line representative of the link and a representation of the difference between the bearing of the plurality of probe points and the bearing of the link.

The present disclosure describes a method of controlling a manufacturing system using multivariate time series, the method comprising: recording data from one or more devices in the manufacturing system; storing the recorded data in a data storage as a plurality of time series, wherein each time series has a first recorded value corresponding to a first time and a final recorded value corresponding to an end of the time series; interpolating, within a first time window, missing values in the plurality of time series using a Bayesian model, wherein the missing values fall between the first and end time of the respective time series; storing the interpolated values as prediction data in a prediction storage, wherein the interpolated values include the uncertainty of each interpolated value; loading the recorded data that fall within a second time window from the data storage; loading prediction data from the prediction storage that fall within the second time window and for which no recorded data are available; optimizing the parameters of the Bayesian model using the loaded recorded data and the prediction data; predicting, using the Bayesian model, values for each of the time series for which loaded recorded and prediction data are not available; storing the predicted values as prediction data in the prediction storage, wherein the prediction values include the uncertainty of each prediction value; and adjusting one or more of the devices that generate the recorded data based on the prediction data within the second time window.

An advisory system of a vessel that monitors variables of a vessel system inclusive of systems and subsystems that are used to operate the vessel. The advisory system may use machine-learning to learn from an operator (i) whether or not two variables are related to one another, and (ii) likelihood that a variable will reach a threshold, and, optionally, time until reaching the threshold. The system may receive operator feedback (i) to indicate whether the two variables are related to one another, and (ii) whether a behavior of the variable is normal or not normal. Thereafter, if a determination that the same two variables are related to one another and behaving in a similar manner, provide notification to the operator of the behavior. In response to determining that the variable is behaving (e.g., trending) in a similar manner that is not normal, providing a notification to the operator.

A system for monitoring at least one HVAC system includes at least one remotely accessible server, at least one probe or sensor operatively connected to the at least one HVAC system and configured to acquire operational data, and a communication module operatively connected to the at least one probe or sensor and configured to transmit the operational data acquired to the at least one remotely accessible server. The remotely accessibly server includes a processor and a memory unit and is programmed to receive and store operational data acquired for the HVAC system, and identify any operational abnormalities by analysing the operational data. Responsive to an operational abnormality being identified, the server is further programmed to designate a tiered maintenance status for the HVAC system and transmit a corresponding tiered maintenance request to a technician based on the tiered maintenance status designated.

A learning apparatus is provided, which comprises: a learning data acquiring unit for acquiring learning data including measurement data obtained by measuring a facility and a state of the facility; a learning pre-processing unit for performing a pre-processing for reducing a drift of the measurement data in the learning data and outputting pre-processed learning data; and a learning processing unit for performing a processing for learning a model for determining the state of the facility from the pre-processed measurement data, by using the pre-processed learning data.

A controller processes data from one or more sensors of a subsystem of a vehicle. The processing includes smoothing the data and calculating a mean of the data. The controller identifies a transition point in the processed data where a moving average of the data is less than the mean by a predetermined amount indicating a trend. The controller selects a segment of the processed data subsequent to the transition point, detects the trend in the segment using regression, and extrapolates the detected trend to reach a predetermined fault threshold. The controller predicts a failure of the subsystem based on a slope of the extrapolated trend and provides an indication of the prediction based on the slope to schedule a service for the subsystem.

The present disclosure describes a method of controlling a manufacturing system using multivariate time series, the method comprising: recording data from one or more devices in the manufacturing system; storing the recorded data in a data storage as a plurality of time series, wherein each time series has a first recorded value corresponding to a first time and a final recorded value corresponding to an end of the time series; interpolating, within a first time window, missing values in the plurality of time series using a Bayesian model, wherein the missing values fall between the first and end time of the respective time series; storing the interpolated values as prediction data in a prediction storage, wherein the interpolated values include the uncertainty of each interpolated value; loading the recorded data that fall within a second time window from the data storage; loading prediction data from the prediction storage that fall within the second time window and for which no recorded data are available; optimizing the parameters of the Bayesian model using the loaded recorded data and the prediction data; predicting, using the Bayesian model, values for each of the time series for which loaded recorded and prediction data are not available; storing the predicted values as prediction data in the prediction storage, wherein the prediction values include the uncertainty of each prediction value; and adjusting one or more of the devices that generate the recorded data based on the prediction data within the second time window.

Provided are a method for early warning of a failure, an electronic device, and a non-transitory computer-readable storage medium. The implementation scheme includes that: a target device is inspected in a current inspection period to obtain a current value of a target device parameter; a new value of the target device parameter in a new inspection period is predicted according to historical inspection records of the target device in historical inspection periods and the current value of the target device parameter; and early warning of a failure is performed according to a preset early warning rule and the new value of the target device parameter.

A controller includes a control unit which stops a transfer mechanism in a case where the value of a deterioration indication parameter has exceeded a preset threshold, and determines whether or not an event in which the value of the deterioration indication parameter has exceeded the preset threshold is attributed to deterioration of the transfer mechanism which has progressed over time based on a change pattern of time series data of the value of the deterioration indication parameter, and causes the transfer mechanism to operate at a reduced operation speed, in a case where the control unit determines that the event in which the value of the deterioration indication parameter has exceeded the preset threshold is attributed to the deterioration of the transfer mechanism which has progressed over time.

An advisory system of a vessel that monitors variables of a vessel system inclusive of systems and subsystems that are used to operate the vessel. The advisory system may use machine-learning to learn from an operator (i) whether or not two variables are related to one another, and (ii) likelihood that a variable will reach a threshold, and, optionally, time until reaching the threshold. The system may receive operator feedback (i) to indicate whether the two variables are related to one another, and (ii) whether a behavior of the variable is normal or not normal. Thereafter, if a determination that the same two variables are related to one another and behaving in a similar manner, provide notification to the operator of the behavior. In response to determining that the variable is behaving (e.g., trending) in a similar manner that is not normal, providing a notification to the operator.