Automatic Rotating-Machine Fault Diagnosis With Confidence Level Indication

摘要

Automatic fault diagnosis is performed on vibration data sensed from a machine. A set of faults to screen for is identified from the machine configuration. For each fault there are characteristic symptoms. For each characteristic symptom, there is a corresponding indication used to diagnose the symptom. The indications are based on analyses of the current vibration data. The diagnosed symptoms have weights assigned according to a Bayesian network, and are used to derive a Bayesian probability for the fault. A fault having a Bayesian probability exceeding a threshold value is identified as being present in the machine. For each fault a confidence level is derived. The confidence level for a first fault is based on a similarity between characteristic symptoms for the first fault and characteristic symptoms for each one of the other faults being screened.

主权项

1. A method of automatic fault diagnosis performed by a processor on machine diagnostic data sensed from a machine, comprising:
analyzing with said processor said machine diagnostic data to screen a plurality of potential faults of the machine; determining a probability value for each one potential fault of the plurality of potential faults, wherein said probability is an indication of fault severity for said one potential fault; for said each one potential fault, deriving a confidence level of the potential fault, wherein the confidence level for said one potential fault is based in part on the probability value determined for each other one of the plurality of faults.