Systems and methods for identifying and predicting industrial machine maintenance events

Abstract

Systems and methods are disclosed herein for identifying and predicting maintenance events with respect to assets, such as mobile machinery. A computing platform can include control circuit(s) (on-board and/or remote) configured to acquire machine fault data and machine utilization data. A feature set can be generated by transforming the machine fault data (e.g., by fusing the machine fault data with machine utilization data), normalizing the data, generating embeddings, and/or executing imputation algorithms to fill in missing values. A classifier model can be trained using the transformed machine fault data in conjunction with labeled maintenance event data to enable the trained classifier model to learn to recognize utilization-specific fault code patterns. The trained classifier model can generate inferences and predictions regarding machinery maintenance events without the use of maintenance data.