Abstract: The present technology includes a method for detecting one or more anomalous operating characteristic of an industrial machine. The method can include collecting telematics data indicative of the industrial machine's performance, generating a histogram based on at least a portion of the collected telematics data, applying a histogram comparator engine to the histogram to determine whether the histogram indicates an anomalous operating characteristic, and if the histogram is determined to indicate an anomalous operating characteristic, presenting, to a user, information associated with the anomalous operating characteristic.

Abstract: Systems and methods for improving recordkeeping and analysis of an asset include creating and maintaining an integrated record about the asset. In some embodiments, the systems and methods include collecting data about an asset to form an asset data collection, recording the asset data collection in a record, analyzing at least a portion of the asset data collection to determine a characteristic of the asset, and recording the characteristic of the asset in the record. In some embodiments, recording the characteristic in the record includes adding the characteristic to the asset data collection.

Abstract: A method for detecting machines engaged in anomalous activity can include receiving telematics data from a plurality of sensors on each of a plurality of machines and determining one or more activity types for each machine over a series of activity time periods based on the associated telematics data for each machine. The method can also include calculating a proportion of the activity time periods in which each machine was engaged in one or more selected activities. The method further includes extracting one or more features for each machine for each of the series of activity time periods from the associated telematics data for each machine. One or more of the plurality of machines engaged in anomalous activity can be identified based on at least the proportion and the one or more extracted features for the machines.

Abstract: A method for forecasting part sales, including collecting sales data for a part over a series of sales time periods and collecting activity data for a plurality of activity types over a series of activity time periods for a plurality of machines including the part. A mean activity time can be calculated for each activity type for each time period in the series of activity time periods based on the collected activity data. An activity probability density function of the mean activity times for each activity type is created and a machine learning model is trained using an expectation of activity derived from the probability density functions for each activity type and the collected sales data. Machine activity data for a set of machines can be fed into the trained model to derive a part sales probability density function for the set of machines.

Abstract: Techniques are provided that include receiving sensor data from sensors of the machine, service history data, previous dealership data, and owner input data. The techniques include generating a state of the machine and a state of each of individual components by processing such data. Some of such data are processed to generate the measure of projected productivity of the machine and the estimate of projected maintained life cycle and costs. The generated data are input to the machine optimization module to generate the optimal performance level of the machine and data indicative of the optimal performance level of the machine, which are processed to generate productivity data of the machine, which are transmitted to a customer-facing application for display or post-processing.

Abstract: A method for identifying machine modifications to improve machine productivity can include receiving telematics data from a plurality of sensors on a machine performing an activity. An activity type can be determined based on the telematics data. The method can also include calculating a current estimated machine productivity for the activity type and calculating a predicted machine productivity for the activity type for each of a plurality of machine modifications. The predicted machine productivity for each of the plurality of machine modifications can be compared with the current estimated productivity. The method can include calculating an investment metric for each of the machine modifications having a predicted machine productivity greater than the current estimated productivity. Each investment metric and its corresponding machine modification can be output for review by a user.

Abstract: A method for determining machine usage severity including collecting repair data for multiple machines over a repair time period and telematics data from sensors on the machines over an activity time period. The method can include calculating predictive features from the telematics data for each of the machines and creating a severity model based on the predictive features. The severity model can be validated with the predictive features and corresponding repair data for each of the machines. The method can include receiving telematics data from sensors on a deployed machine for a deployed period of time, calculating a plurality of machine predictive features from the telematics data, and feeding the machine predictive features into the severity model to calculate a severity score for the deployed machine. The method can include displaying a recommendation to perform maintenance on the deployed machine when the machine usage severity score exceeds a selected threshold.

Abstract: Systems and methods for monitoring an operator of an asset are described herein. The method includes receiving training data, the training data comprising training sensor data associated with one or more tasks performed by a plurality of operators of different skill levels and under different performance impairments. The method can also include training a machine learning model to recognize one or more operator conditions based on the received training data and receiving sensor data from a plurality of sensors associated with the asset or the operator. The method can further include determining an operator condition of the operator based on the received sensor data and the machine learning model and taking one or more actions in response to the determined operator condition.

Abstract: Techniques are provided that include receiving sensor data from sensors of the machine, service history data, previous dealership data, and owner input data. The techniques include generating a state of the machine and a state of each of individual components by processing such data. Some of such data are processed to generate the measure of projected productivity of the machine and the estimate of projected maintained life cycle and costs. The generated data are input to the machine optimization module to generate the optimal performance level of the machine and data indicative of the optimal performance level of the machine, which are processed to generate productivity data of the machine, which are transmitted to a customer-facing application for display or post-processing.

Abstract: The present technology includes a method for detecting one or more anomalous operating characteristic of an industrial machine. The method can include collecting telematics data indicative of the industrial machine's performance, generating a histogram based on at least a portion of the collected telematics data, applying a histogram comparator engine to the histogram to determine whether the histogram indicates an anomalous operating characteristic, and if the histogram is determined to indicate an anomalous operating characteristic, presenting, to a user, information associated with the anomalous operating characteristic.

Abstract: Systems and methods for improving recordkeeping and analysis of an asset include creating and maintaining an integrated record about the asset. In some embodiments, the systems and methods include collecting data about an asset to form an asset data collection, recording the asset data collection in a record, analyzing at least a portion of the asset data collection to determine a characteristic of the asset, and recording the characteristic of the asset in the record. In some embodiments, recording the characteristic in the record includes adding the characteristic to the asset data collection.

Abstract: A method for determining machine usage severity including collecting repair data for multiple machines over a repair time period and telematics data from sensors on the machines over an activity time period. The method can include calculating predictive features from the telematics data for each of the machines and creating a severity model based on the predictive features. The severity model can be validated with the predictive features and corresponding repair data for each of the machines. The method can include receiving telematics data from sensors on a deployed machine for a deployed period of time, calculating a plurality of machine predictive features from the telematics data, and feeding the machine predictive features into the severity model to calculate a severity score for the deployed machine. The method can include displaying a recommendation to perform maintenance on the deployed machine when the machine usage severity score exceeds a selected threshold.

Abstract: A method for identifying machine modifications to improve machine productivity can include receiving telematics data from a plurality of sensors on a machine performing an activity. An activity type can be determined based on the telematics data. The method can also include calculating a current estimated machine productivity for the activity type and calculating a predicted machine productivity for the activity type for each of a plurality of machine modifications. The predicted machine productivity for each of the plurality of machine modifications can be compared with the current estimated productivity. The method can include calculating an investment metric for each of the machine modifications having a predicted machine productivity greater than the current estimated productivity. Each investment metric and its corresponding machine modification can be output for review by a user.

Abstract: A method for estimating warranty costs for an individual machine can include training a warranty cost model. The method can also include receiving telematics data from a plurality of sensors on an individual machine and determining one or more activity types for the individual machine based on the associated telematics data. A mean activity time can be calculated for each activity type. The mean activity time for each activity type can be fed into the trained warranty cost model to provide a predicted warranty cost for the individual machine and a corresponding probability of the predicted warranty cost from the trained warranty cost model.

Abstract: Systems and methods for monitoring an operator of an asset are described herein. The method includes receiving training data, the training data comprising training sensor data associated with one or more tasks performed by a plurality of operators of different skill levels and under different performance impairments. The method can also include training a machine learning model to recognize one or more operator conditions based on the received training data and receiving sensor data from a plurality of sensors associated with the asset or the operator. The method can further include determining an operator condition of the operator based on the received sensor data and the machine learning model and taking one or more actions in response to the determined operator condition.

Abstract: A method for forecasting part sales, including collecting sales data for a part over a series of sales time periods and collecting activity data for a plurality of activity types over a series of activity time periods for a plurality of machines including the part. A mean activity time can be calculated for each activity type for each time period in the series of activity time periods based on the collected activity data. An activity probability density function of the mean activity times for each activity type is created and a machine learning model is trained using an expectation of activity derived from the probability density functions for each activity type and the collected sales data. Machine activity data for a set of machines can be fed into the trained model to derive a part sales probability density function for the set of machines.