A system and method of detecting and responding to a failure of one or more vehicle components, the method including: receiving system input at a failure detection module regarding the one or more vehicle components; determining a system state through use of one or more onboard vehicle sensors; obtaining a nominal state transition matrix and a nominal state input matrix; calculating a present state transition matrix estimate and a present state input matrix estimate based on the nominal state transition matrix, the nominal state input matrix, the system input, and a sampled state derivative; detecting a failure of at least one of the vehicle components based on one or more component parameters of the present state transition matrix estimate and/or the present state input matrix estimate; and performing a vehicle action in response to the detection of the failure.

A method for handling security alarms by a control system of a technical installation includes a) receiving diagnostic messages that have been generated by technical objects (7) of a technical installation; b) analyzing the diagnostic messages such that diagnostic messages relevant to the security of an operation of the technical installation are identified by means of comparative data records, where a machine learning network is used to analyze the diagnostic messages to assess the security relevance of the diagnostic messages, where the network is previously trained using special inputs from operators of the technical installation that have assessed past diagnostic messages with regard to their security relevance; c) if necessary, adapting the previously identified diagnostic messages to requirements of a computer-implemented security module of the technical installation and d) transmitting the previously identified and optionally adapted diagnostic messages to the computer-implemented security module of the technical installation.

A system monitors the health of a helicopter, and includes a device for determining a change of state of the engine and is configured to collect data measured by engine and external conditions sensors during a stable flight phase and to process the measured data.

It is judged whether a first predicted value of an operation index obtained by inputting a scheduled change value of a manipulation parameter of a plant meets an operation criterion, and whether a second predicted value of the operation index obtained by inputting a virtual change value with a greater change amount from a current value than the scheduled change value to a prediction model meets the operation criterion. If it is judged that the first predicted value and the second predicted value meet the operation criterion, the scheduled change value is output as a command value of the manipulation parameter.

Systems, methods, and computer program products for remaining useful life prediction. Operational data is collected from a test machine until a component fails, and a training dataset generated from the operational data. The training dataset is used to define and validate a prediction model. Operational data received from one or more field machines is provided to the prediction model. The prediction model then predicts the remaining useful life of the component of the field machine. To reduce the time-to-failure of the component in the test machine, the component may be repeatedly subjected to an accelerated wear cycle. The prediction model may be defined by extracting features from the training dataset. Like features may be extracted from the field dataset and provided to the prediction model as part of the prediction process. The operational data received from the field machines may be used to generate an updated prediction model.

Systems, methods, and computer program products for monitoring a health condition of a tool. Operational data is collected from a machine while the machine is operating in a predetermined manner with the tool in each of at least two known health conditions. A plurality of features is extracted from the operational data, a training dataset is generated from the extracted features, and an analytic model is trained using the training dataset. The analytic model can then be used to determine the health condition of the tool by providing features extracted from operational data received from one or more field machines to the analytic model. The analytic model may then determine a health condition of the tool in the field machine based on like features extracted from the operational data from the one or more field machines.

A processing abnormality diagnostic device includes an abnormality diagnostic unit that diagnoses whether processing is abnormal using an abnormality threshold by a preset diagnostic model, a success or failure input unit that inputs success or failure of the diagnosis of the abnormality by the abnormality diagnostic unit, a measure determination unit that determines a measure when the diagnosis of the abnormality is input as failure through the success or failure input unit, an abnormality threshold change unit that updates the abnormality threshold, and a learning unit that relearns the diagnostic model using operation information of the machine tool when the diagnosis of the abnormality has failed. The measure determination unit determines which of the abnormality threshold change unit and the learning unit is to be adopted as the measure based on the operation information diagnosed by the diagnostic model when the diagnosis of the abnormality has failed.

One embodiment of the present invention provides an apparatus, or the like, which detects an anomaly of a controller of a control system by learning relationship between input and output of the controller. An anomaly detection apparatus which is one embodiment of the present invention includes a first acquirer, a second acquirer, a history recorder, an estimator, and a first anomaly determiner. The first acquirer acquires an input signal to a control apparatus which executes control on a controlled apparatus. The second acquirer acquires an output signal from the control apparatus. The history recorder records information regarding the acquired input signal and the acquired output signal as history. The estimator estimates the output signal using the history and an estimation model. The first anomaly determiner determines an anomaly of the control apparatus by comparing the estimated output signal with the acquired output signal.

An error compensation system for a numerical control (NC) machine tool based on iterative learning control, including a trajectory generating module, a down-sampling module, a position controller, a first holder, a velocity-loop iterative learning controller, a velocity controller, a second holder and a control plant. The trajectory generating module is configured to generate a desired trajectory command including a first sampling command. The first sampling command is transmitted to the down-sampling module and the velocity-loop iterative learning controller. The first sampling command is down-sampled through the down-sampling module to obtain a second sampling command. The velocity-loop iterative learning controller is configured to receive the first sampling command, and obtain a first sampling error compensation sequence according to a first sampling error sequence and a first sampling error compensation sequence of a previous iteration machining process stored therein. An error compensation method is also provided herein.

A monitoring apparatus according to the present invention includes: a predicting unit configured to, based on a correlation model that corresponds to processing executed in a monitored object in accordance with a preset operation plan and represents a mutual relation between one measured value and other measured value measured from the monitored object, predict the other measured value when the one measured value is changed; and a determining unit configured to determine whether or not the predicted other measured value exceeds an allowable value set for the other measured value during the processing executed in the monitored object in accordance with the operation plan.