US 12,148,508 B2
Predictive engine maintenance apparatuses, methods, systems and techniques
Ryan E. Denton, Franklin, IN (US); Xinjian Xue, Carmel, IN (US); Corey W. Trobaugh, Columbus, IN (US); and Anthony Joseph Huth, Cincinnati, OH (US)
Assigned to Cummins Inc., Columbus, IN (US)
Filed by Cummins Inc., Columbus, IN (US)
Filed on May 24, 2022, as Appl. No. 17/664,700.
Application 17/664,700 is a continuation of application No. PCT/US2020/061917, filed on Nov. 24, 2020.
Claims priority of provisional application 62/940,434, filed on Nov. 26, 2019.
Prior Publication US 2022/0284988 A1, Sep. 8, 2022
Int. Cl. G16B 40/20 (2019.01); G06F 18/214 (2023.01); G06N 5/01 (2023.01); G06N 5/04 (2023.01)
CPC G16B 40/20 (2019.02) [G06F 18/214 (2023.01); G06N 5/01 (2023.01); G06N 5/04 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
providing a computing system configured with a combination of models including a classification model pre-trained by machine learning to output one or more probabilities of one or more fail codes in response to input including oil analysis data, a recommendation model configured to receive output of the pre-trained classification model and pre-trained by machine learning to output a related items dataset in response to the one or more probabilities of one or more fail codes and, and an expert system model configured to receive output of the recommendation model and pre-trained by machine learning to output a root cause indicating a preventative maintenance action in response the related items dataset;
inputting used oil analysis data to the classification model, the used oil analysis data including values quantifying a plurality of chemical components measured in a sample of used oil taken from an engine under analysis;
determining a probability of at least one fail code with the classification model in response to the used oil analysis data, the at least one fail code corresponding to one of a plurality of predetermined engine failure types;
determining with the recommendation model a related items dataset in response to the at least one fail code;
providing the related items dataset, the at least one fail code and the probability of the at least one fail code to the expert system model;
determining a root cause indicating a preventative maintenance action with the expert system model in response to the related items dataset, the at least one fail code and the probability of the at least one fail code; and
performing the predictive maintenance action on the engine under analysis.