System for predictive operational analysis of a machinery component

A method for generating failure data for predictive operational analysis, the method comprising:

• identifying an operational component in a machine;
• gathering operational characteristics of the operational component, wherein the operational characteristics include operating properties, maintenance history, and environmental properties;
• creating a surrogate testing system including a testing device and a testing component corresponding to the operational component;
• introducing a failure cause to the testing component, wherein the failure cause is selected from a group consisting of contamination, brinelling, fatigue, improper lubrication, excessive load, and any combination thereof;
• operating the testing device while measuring performance characteristics of the testing component via one or more sensors;
• detecting when the testing component reaches one or more failure states including a destruction state;
• generating a failure dataset representing the one or more failure states from an initial defect through the destruction state;
• applying scaling parameters to relate the failure dataset to the operational component;
• training a condition monitoring system using the failure dataset with the scaling parameters applied;
• mapping a current status of the operational component to a usable lifetime model generated from the failure dataset;
• and
• displaying an anticipated failure of the operational component based on the mapping.

A system for monitoring an operational component, the system comprising:

• a monitoring system including one or more sensors configured to measure operational characteristics of the operational component associated with a machine;
• a server in communication with the monitoring system;
• a memory storing a failure dataset, wherein the failure dataset is generated from at least one of:
• testing a testing component with an induced defect using a testing assembly, or digitally simulating one or more failure causes on a digital representation of the operational component, and wherein the failure dataset represents failure states from an initial defect state through a destruction state;
• and
• computer readable instructions that, when executed by a processor, cause the processor to:
• compare the operational characteristics received from the one or more sensors with the failure dataset;
• generate an indication when the operational component approaches a failure state based on the comparison;
• receive actual failure information from the operational component;
• and
• modify the failure dataset based on the actual failure information to provide machine learning feedback for subsequent failure state predictions.