Abstract: A method for determining a vehicle system prognosis includes detecting a predetermined characteristic of a vehicle with one or more sensors, receiving a plurality of sensor signals from the one or more sensors and determining an input time series of data based on the sensor signals, clustering a matrix of time series data, generated from the input time series of data, into a predetermined number of hyperplanes, extracting extracted features that are indicative of an operation of a vehicle system from a sparse temporal matrix based on data point behavior with respect to two or more hyperplanes within the sparse temporal matrix and determining an operational status of the vehicle system based on the extracted features, the sparse temporal matrix being based on the predetermined number of hyperplanes; and communicating the operational status of the vehicle system to an operator or crew member of the vehicle.

Abstract: A fault detection system including one or more sensors onboard a vehicle to detect a characteristic of the vehicle and generate sensor signals corresponding to the characteristic, a processor onboard the vehicle to receive the sensor signals, generate one or more fast Fourier transform vectors based on the sensor signals so that the one or more fast Fourier transform vectors are representative of the characteristic, generate an analysis model from a time history of the fast Fourier transform vectors, and determine, using the analysis model, a degree to which the one or more fast Fourier transform vectors could have been generated by the analysis model, and an indicator to communicate an operational status of the vehicle to an operator or crew member of the vehicle based on the degree to which the one or more fast Fourier transform vectors could have been generated by the analysis model.

Abstract: A vehicle system prognosis apparatus including sensor(s) for detecting a characteristic of a vehicle system and generating at least one time series of condition indicator values, and a processor that receives the at least one time series and generates an analysis model, for the characteristic, that is trained with one or more of the at least one time series, that are obtained from the one or more sensors with the vehicle system operating under normal conditions, extracts from the at least one time series one or more features embodying an indication of a health of the vehicle system, generates a quantified health assessment of the vehicle system by quantifying the one or more features based on a normal distribution of the one or more features from the analysis model, and communicates the quantified health assessment of the vehicle system to an operator or crew member of the vehicle.

Abstract: A fault detection system including one or more sensors onboard a vehicle to detect a characteristic of the vehicle and generate sensor signals corresponding to the characteristic, a processor onboard the vehicle to receive the sensor signals, generate one or more fast Fourier transform vectors based on the sensor signals so that the one or more fast Fourier transform vectors are representative of the characteristic, generate an analysis model from a time history of the fast Fourier transform vectors, and determine, using the analysis model, a degree to which the one or more fast Fourier transform vectors could have been generated by the analysis model, and an indicator to communicate an operational status of the vehicle to an operator or crew member of the vehicle based on the degree to which the one or more fast Fourier transform vectors could have been generated by the analysis model.

Abstract: A fault detection system including one or more sensors onboard a vehicle, the one or more sensors being configured to detect a predetermined characteristic of the vehicle and generate a plurality of sensor signals corresponding to the predetermined characteristic, and a processor onboard the vehicle and in communication with the one or more sensors, the processor being configured to generate an analysis model for the predetermined characteristic, the analysis model being trained by the processor with a training data set of fast Fourier transform vectors that are generated from the plurality of sensor signals obtained under normal operating conditions of the predetermined characteristic, and determine a health of a vehicle component corresponding to the predetermined characteristic with the analysis model.

Abstract: A method for determining a vehicle system prognosis includes detecting a predetermined characteristic of a vehicle with one or more sensors, receiving a plurality of sensor signals from the one or more sensors and determining an input time series of data based on the sensor signals, clustering a matrix of time series data, generated from the input time series of data, into a predetermined number of hyperplanes, extracting extracted features that are indicative of an operation of a vehicle system from a sparse temporal matrix based on data point behavior with respect to two or more hyperplanes within the sparse temporal matrix and determining an operational status of the vehicle system based on the extracted features, the sparse temporal matrix being based on the predetermined number of hyperplanes; and communicating the operational status of the vehicle system to an operator or crew member of the vehicle.

Abstract: A method for determining a vehicle system prognosis includes detecting a predetermined characteristic of a vehicle with one or more sensors, obtaining a plurality of sensor signals corresponding to the predetermined characteristic, receiving the plurality of sensor signals from the one or more sensors and determining an input time series of data based on the sensor signals, generating, a matrix of time series data based on the input time series of data, clustering the matrix of time series data based on predetermined clustering criteria into a predetermined number of clusters, generating a sparse temporal matrix based on the predetermined number of clusters, extracting extracted features that are indicative of an operation of a vehicle system from the sparse temporal matrix and determining an operational status of the vehicle system based on the extracted features, and communicating the operational status of the vehicle system to an operator or crew member of the vehicle.

Abstract: A vehicle system prognosis apparatus including sensor(s) for detecting a characteristic of a vehicle system and generating at least one time series of condition indicator values, and a processor that receives the at least one time series and generates an analysis model, for the characteristic, that is trained with one or more of the at least one time series, that are obtained from the one or more sensors with the vehicle system operating under normal conditions, extracts from the at least one time series one or more features embodying an indication of a health of the vehicle system, generates a quantified health assessment of the vehicle system by quantifying the one or more features based on a normal distribution of the one or more features from the analysis model, and communicates the quantified health assessment of the vehicle system to an operator or crew member of the vehicle.

Abstract: A fault detection system including one or more sensors onboard a vehicle, the one or more sensors being configured to detect a predetermined characteristic of the vehicle and generate a plurality of sensor signals corresponding to the predetermined characteristic, and a processor onboard the vehicle and in communication with the one or more sensors, the processor being configured to generate an analysis model for the predetermined characteristic, the analysis model being trained by the processor with a training data set of fast Fourier transform vectors that are generated from the plurality of sensor signals obtained under normal operating conditions of the predetermined characteristic, and determine a health of a vehicle component corresponding to the predetermined characteristic with the analysis model.

Abstract: A method for determining a vehicle system prognosis includes detecting a predetermined characteristic of a vehicle with one or more sensors, obtaining a plurality of sensor signals corresponding to the predetermined characteristic, receiving the plurality of sensor signals from the one or more sensors and determining an input time series of data based on the sensor signals, generating, a matrix of time series data based on the input time series of data, clustering the matrix of time series data based on predetermined clustering criteria into a predetermined number of clusters, generating a sparse temporal matrix based on the predetermined number of clusters, extracting extracted features that are indicative of an operation of a vehicle system from the sparse temporal matrix and determining an operational status of the vehicle system based on the extracted features, and communicating the operational status of the vehicle system to an operator or crew member of the vehicle.

Abstract: A method for detecting vehicle system faults includes receiving, with a processor, a plurality of sensor signals from one or more sensors; thresholding, with the processor, the plurality of sensor signals for each respective sensor substantially in real time; and generating, with the processor, abnormal derivative frequency values for each of the plurality of thresholded sensor signals in real time and determining an operational status of at least each of the one or more sensors based on the abnormal derivative frequency values.

Abstract: A method for detecting vehicle system faults includes receiving, with a processor, a plurality of sensor signals from one or more sensors; thresholding, with the processor, the plurality of sensor signals for each respective sensor substantially in real time; and generating, with the processor, abnormal derivative frequency values for each of the plurality of thresholded sensor signals in real time and determining an operational status of at least each of the one or more sensors based on the abnormal derivative frequency values.