Abstract: Methods and apparatus of modeling work vehicle data and predicting machine failures based on the same are disclosed. An example apparatus includes a text miner to text mine first alert data to identify a first machine failure within the first alert data; a failure alert sequence identifier to identify a first alert sequence associated with the first machine failure; a conditional probability determiner to determine a conditional probability of the first alert sequence leading to failure based on i) a number of work machines in which the first alert sequence ended in failure and ii) a number of work machines in which the first alert sequence did not end in failure; and a collator to update a model by correlating the first alert sequence with the first machine failure and first probable parts used to repair the first machine failure.

Abstract: Methods and apparatus of modeling work vehicle data and predicting machine failures based on the same are disclosed. An example apparatus includes a text miner to text mine first alert data to identify a first machine failure within the first alert data; a failure alert sequence identifier to identify a first alert sequence associated with the first machine failure; a conditional probability determiner to determine a conditional probability of the first alert sequence leading to failure based on i) a number of work machines in which the first alert sequence ended in failure and ii) a number of work machines in which the first alert sequence did not end in failure; and a collator to update a model by correlating the first alert sequence with the first machine failure and first probable parts used to repair the first machine failure.

Abstract: Methods and apparatus of modeling work vehicle data and predicting machine failures based on the same are disclosed. An example method includes accessing a first alert sequence from a work vehicle, the first alert sequence is associated with one or more of observed diagnostic code, observed vehicle sensor data, or observed vehicle status data; comparing the first alert sequence to a model including an association between reference alert sequences, reference machine failures, and probable parts used to repair the respective reference machine failures; identifying a substantial similarity between the first alert sequence and a first reference alert sequence of the reference alert sequences; and based on the model and the first reference alert sequence, identifying first probable parts of the probable parts used to repair a first reference machine failure of the reference machine failures, where the first reference machine failure is associated with the first reference alert sequence.

Abstract: Methods and apparatus of modeling work vehicle data and predicting machine failures based on the same are disclosed. An example method includes accessing a first alert sequence from a work vehicle, the first alert sequence is associated with one or more of observed diagnostic code, observed vehicle sensor data, or observed vehicle status data; comparing the first alert sequence to a model including an association between reference alert sequences, reference machine failures, and probable parts used to repair the respective reference machine failures; identifying a substantial similarity between the first alert sequence and a first reference alert sequence of the reference alert sequences; and based on the model and the first reference alert sequence, identifying first probable parts of the probable parts used to repair a first reference machine failure of the reference machine failures, where the first reference machine failure is associated with the first reference alert sequence.