Equipment Condition Monitoring and Prognostic Methods for Single Variable Systems

This paper introduces empirical modeling techniques for process and equipment monitoring, fault detection and diagnostics, and prognostics. The paper first provides a brief background and an overview of the theoretical foundations and presents a new method for applying these methods to systems which only have one useful measured variable. A case study is then presented for the application of the method to an aircraft generator that includes

• 1.
  Normal feature prediction over different operating conditions
• 2.
  Actual feature measurement and residual generation
• 3.
  Fault detection, identification, and quantification

Application of the proposed single variable monitoring system to the simulated aircraft generator data resulted in fault diagnosis accuracy of 96.3%, only one misdiagnosed case in 27, for the types and severities of faults considered. Future work in developing a prognostic model for a single-variable system will be outlined.