Abstract: Methods, systems, and computer program products for generating estimates of failure risk for a vehicular component are provided herein. A method includes splitting an input time series pertaining to a vehicular component across a fleet of multiple vehicles into multiple sub-time series, wherein each sub-time series comprises multiple data points of the input time series that correspond to measurements derived from the vehicular component; determining a weight applied to each of the sub-time series based on a pre-determined weight associated with the input time series; applying a failure or non-failure classification label to each of the sub-time series and the input time series; calculating a performance measure for the input time series; determining an updated weight associated with the input time series; and generating an estimate of failure risk for the vehicular component based on the classification label applied to each input time series and the updated weight.

Abstract: Methods, systems, and computer program products for generating estimates of failure risk for a vehicular component in situations of high-dimensional and low sample size data are provided herein. A method includes splitting a first input time series comprising multiple data points derived from a vehicular component across a fleet of multiple vehicles into multiple sub-time series; generating a first failure status predicting function of a first selected sub-time series; deleting, from the first input time series, the portion of the data points that corresponds to the first selected sub-time series; repeating the preceding two steps for a second selected sub-time series; generating a second failure status predicting function of each selected sub-time series; applying each second failure status predicting function to a second input time series to calculate prediction of failure values; and identifying the largest prediction of failure value as an estimate of failure risk for the vehicular component.

Abstract: Methods, systems, and computer program products for generating wear-based indicators are provided herein.

Abstract: Methods, systems, and computer program products for generating a vehicular component replacement policy are provided herein.

Abstract: Methods, systems, devices and computer program products for planning for failures of vehicular components are provided herein. A method includes obtaining a first set of data of maintenance events recorded for multiple vehicular components across multiple vehicles in a fleet; obtaining a second set of data of maintenance work orders performed on the vehicular components; obtaining a third set of data of measurements taken in connection with the vehicular components; analyzing (i) the first set of data and (ii) the second set of data to identify component failure events associated with the multiple vehicular components; determining failure indicators for each of the vehicular components in each of the vehicles in the fleet based on the second set of data, the third set of data, and the identified component failure events; and outputting the indicators in multiple visualized forms, each representing one of multiple levels of granularity.

Abstract: Methods, systems, and computer program products for generating wear-based indicators are provided herein.

Abstract: Methods, systems, and computer program products for generating wear-based indicators for vehicular components are provided herein. A method includes assigning a failure class label to each data point, from multiple data points derived from measurements associated with a vehicular component across a fleet of vehicles, that is within a pre-specified number of runtime hours of a replacement; assigning a non-failure class label to each data point not within the pre-specified number of runtime hours of a replacement and each data point associated with a component yet to be replaced; estimating a failure probability at each data point over a pre-specified future runtime of the component based on the assigned class label; determining a cumulative hazard function for the vehicular component based on the failure probability; and generating a cumulative wear-based indicator for the vehicular component by executing a regression function at a given time based on the cumulative hazard function.

Abstract: Methods, systems, and computer program products for generating estimates of failure risk for a vehicular component in situations of high-dimensional and low sample size data are provided herein. A method includes splitting a first input time series comprising multiple data points derived from a vehicular component across a fleet of multiple vehicles into multiple sub-time series; generating a first failure status predicting function of a first selected sub-time series; deleting, from the first input time series, the portion of the data points that corresponds to the first selected sub-time series; repeating the preceding two steps for a second selected sub-time series; generating a second failure status predicting function of each selected sub-time series; applying each second failure status predicting function to a second input time series to calculate prediction of failure values; and identifying the largest prediction of failure value as an estimate of failure risk for the vehicular component.

Abstract: Methods, systems, and computer program products for generating a vehicular component replacement policy are provided herein.

Abstract: Methods, systems, and computer program products for generating estimates of failure risk for a vehicular component are provided herein. A method includes splitting an input time series pertaining to a vehicular component across a fleet of multiple vehicles into multiple sub-time series, wherein each sub-time series comprises multiple data points of the input time series that correspond to measurements derived from the vehicular component; determining a weight applied to each of the sub-time series based on a pre-determined weight associated with the input time series; applying a failure or non-failure classification label to each of the sub-time series and the input time series; calculating a performance measure for the input time series; determining an updated weight associated with the input time series; and generating an estimate of failure risk for the vehicular component based on the classification label applied to each input time series and the updated weight.

Abstract: Methods, systems, devices and computer program products for planning for failures of vehicular components are provided herein. A method includes obtaining a first set of data of maintenance events recorded for multiple vehicular components across multiple vehicles in a fleet; obtaining a second set of data of maintenance work orders performed on the vehicular components; obtaining a third set of data of measurements taken in connection with the vehicular components; analyzing (i) the first set of data and (ii) the second set of data to identify component failure events associated with the multiple vehicular components; determining failure indicators for each of the vehicular components in each of the vehicles in the fleet based on the second set of data, the third set of data, and the identified component failure events; and outputting the indicators in multiple visualized forms, each representing one of multiple levels of granularity.

Abstract: Methods, systems, and computer program products for generating wear-based indicators for vehicular components are provided herein. A method includes assigning a failure class label to each data point, from multiple data points derived from measurements associated with a vehicular component across a fleet of vehicles, that is within a pre-specified number of runtime hours of a replacement; assigning a non-failure class label to each data point not within the pre-specified number of runtime hours of a replacement and each data point associated with a component yet to be replaced; estimating a failure probability at each data point over a pre-specified future runtime of the component based on the assigned class label; determining a cumulative hazard function for the vehicular component based on the failure probability; and generating a cumulative wear-based indicator for the vehicular component by executing a regression function at a given time based on the cumulative hazard function.

Abstract: Techniques for producing probabilistic event networks (Bayesian network based representation of node dependencies, whereas nodes comprise event occurrences, explicit times of occurrences, and the context of event occurrences) based on distributed time-stamped data are disclosed. An aspect provides a method for predicting events from event log data via constructing a probabilistic event net and using the probabilistic event net to infer a probabilistic statement regarding a future event using a network inference mechanism. Other embodiments are disclosed.

Abstract: Embodiments relate to mode determination for multivariate time series data. An aspect includes determining first within-mode and first cross-mode parameters for a first number of modes, each mode comprising one or more time periods in the multivariate time series. Another aspect includes determining a first likelihood of the at least one multivariate time series based on the first sets of within-mode parameters and first set of cross-mode parameters. Another aspect includes determining second within-mode and second cross-mode parameters for a second number of modes. Another aspect includes determining a second likelihood of the at least one multivariate time series based on the second sets of within-mode parameters and second set of cross-mode parameters. Another aspect includes based on the first likelihood being higher than the second likelihood, selecting the first number of modes to model the at least one multivariate time series.

Abstract: Embodiments relate to mode determination for multivariate time series data. An aspect includes determining first within-mode and first cross-mode parameters for a first number of modes, each mode comprising one or more time periods in the multivariate time series. Another aspect includes determining a first likelihood of the at least one multivariate time series based on the first sets of within-mode parameters and first set of cross-mode parameters. Another aspect includes determining second within-mode and second cross-mode parameters for a second number of modes. Another aspect includes determining a second likelihood of the at least one multivariate time series based on the second sets of within-mode parameters and second set of cross-mode parameters. Another aspect includes based on the first likelihood being higher than the second likelihood, selecting the first number of modes to model the at least one multivariate time series.

Abstract: Embodiments relate to mode determination for multivariate time series data. An aspect includes determining first within-mode and first cross-mode parameters for a first number of modes, each mode comprising one or more time periods in the multivariate time series. Another aspect includes determining a first likelihood of the at least one multivariate time series based on the first sets of within-mode parameters and first set of cross-mode parameters. Another aspect includes determining second within-mode and second cross-mode parameters for a second number of modes. Another aspect includes determining a second likelihood of the at least one multivariate time series based on the second sets of within-mode parameters and second set of cross-mode parameters. Another aspect includes based on the first likelihood being higher than the second likelihood, selecting the first number of modes to model the at least one multivariate time series.

Abstract: Embodiments relate to mode determination for multivariate time series data. An aspect includes determining first within-mode and first cross-mode parameters for a first number of modes, each mode comprising one or more time periods in the multivariate time series. Another aspect includes determining a first likelihood of the at least one multivariate time series based on the first sets of within-mode parameters and first set of cross-mode parameters. Another aspect includes determining second within-mode and second cross-mode parameters for a second number of modes. Another aspect includes determining a second likelihood of the at least one multivariate time series based on the second sets of within-mode parameters and second set of cross-mode parameters. Another aspect includes based on the first likelihood being higher than the second likelihood, selecting the first number of modes to model the at least one multivariate time series.

Abstract: Described herein are techniques for producing probabilistic event networks (Bayesian network based representation of node dependencies, whereas nodes comprise event occurrences, explicit times of occurrences, and the context of event occurrences) based on distributed time-stamped data. An aspect provides a method for predicting events from event log data via constructing a probabilistic event net and using the probabilistic event net to infer a probabilistic statement regarding a future event using a network inference mechanism. Other embodiments are disclosed.