Time series: theory and methods
Time series: theory and methods
Fault diagnosis in dynamic systems: theory and application
Fault diagnosis in dynamic systems: theory and application
Robust model-based fault diagnosis for dynamic systems
Robust model-based fault diagnosis for dynamic systems
Fault Diagnosis: Models, Artificial Intelligence, Applications
Fault Diagnosis: Models, Artificial Intelligence, Applications
Random matrix theory and wireless communications
Communications and Information Theory
Diagnosis and Fault-Tolerant Control
Diagnosis and Fault-Tolerant Control
Improved principal component monitoring using the local approach
Automatica (Journal of IFAC)
A periodogram-based metric for time series classification
Computational Statistics & Data Analysis
Issues of Fault Diagnosis for Dynamic Systems
Issues of Fault Diagnosis for Dynamic Systems
Dynamic principal component analysis using subspace model identification
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Hi-index | 35.68 |
This paper proposes a fault detection method for multivariate signals. The method assesses whether or not the multivariate autocovariance functions of two independently sampled system signals coincide. If the first signal is known to be sampled from a well-functioning system, then rejection of signal equality is tantamount to concluding that the second signal is sampled from a faulty system. The proposed method is based on the asymptotic properties of the periodogram of multivariate stationary time series and is nonparametric in nature; in particular, there is no need to model the signals under study, an often arduous task. Several natural and synthetic faults were introduced in a Solar Turbines Mercury 50 4.5 MW gas turbine and the resulting compressor delivery pressure and generated electrical power were analyzed. The propod method capably detected all faults.