Caterpillar Inc.

David A Villero

Kyle J Cline

Benjamin J Hodel

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.

Anomalous operating characteristic detection system, and associated devices and methods

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.

System and method for estimating a machine's potential usage, profitability, and cost of ownership based on machine's value and mechanical state

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.

Systems and methods for identifying machine modifications for improved productivity

A machine includes a frame, an engine supported on the frame, ground-engaging elements supported on the frame and driven by the engine to propel the machine, a machine light structure supported on the frame, a multiple-axis accelerometer positioned to generate an acceleration signal relative to a base of the machine light structure, and a controller. The controller is programmed to receive the acceleration signal, partition the acceleration signal into overlapped window periods, transform the acceleration signal of each overlapped window period into a power spectral density, and pass the power spectral densities of each overlapped window period through a strain power spectral densities prediction model to generate predicted strain power spectral densities of the machine light structure. A health monitor receives the predicted strain power spectral densities and is configured to generate a notification responsive to the predicted strain power spectral densities.

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Champaign, IL, United States of America

David A Villero