Accession Number:
ADA243493
Title:
Incipient Fault Detection Using Higher-Order Statistics
Descriptive Note:
Doctoral thesis,
Corporate Author:
AIR FORCE INST OF TECH WRIGHT-PATTERSON AFB OH
Personal Author(s):
Report Date:
1991-08-01
Pagination or Media Count:
189.0
Abstract:
This study balances the development of theory and its application to real an simulated incipient fault data from systems which have cyclostationary properties. The studys theoretical contribution reveals the advantages of approaching estimation of time series in a general framework where estimation of the cumulant spectrum can reveal implications for three classes of stochastic processes stationary, cyclostationary, and nonstationary. The developed cumulant spectrum estimation capability provides for feature construction in addition to bispectrum and power spectrum estimates of stochastic process data. Actual experimental data is obtained to study the incipient wear process of manufacturing drill bits cutting through epoxy glass composite material used for construction of electronic semiconductor panels. The fluctuating vibrations caused by the drill bits cutting through the epoxy-glass composite material used for construction of electronic semiconductor panels. The fluctuating vibrations caused by the drill bits cutting through the epoxy-glass composite are not subject to precise prediction, nor are the external noise, measurement errors, and other disturbances in the transmission of the vibration signal to three accelerometers mounted on the drilling machine considered to have the same characteristic of unpredictability.
Descriptors:
- *MEASUREMENT
- EXPERIMENTAL DATA
- STOCHASTIC PROCESSES
- DETECTION
- MANUFACTURING
- PREDICTIONS
- ACCELEROMETERS
- MATERIALS
- PANELS
- THEORY
- ELECTRONIC EQUIPMENT
- TIME SERIES ANALYSIS
- POWER SPECTRA
- SEMICONDUCTORS
- ESTIMATES
- CONSTRUCTION
- SIGNALS
- ERRORS
- PRECISION
- EXTERNAL
- NOISE
- EPOXY COMPOSITES
- EPOXY RESINS
- FAULTS
- BALANCES
- DRILLS
- DRILLING MACHINES
- GLASS REINFORCED PLASTICS
- VIBRATION
Subject Categories:
- Statistics and Probability
- Manufacturing and Industrial Engineering and Control of Production Systems