Dr. Jingxian Wu

“Quickest Change Detection with Applications on Wind Turbine Fault Detection”

IEEE MTT/AP Orlando chapter & Raj Mittra Distinguished Lecture Program

Date/time: Monday, March 19, 2018 (6:00 PM-7:00 PM)

SPEAKER: Dr. Jingxian Wu

Associate Professor, Department of Electrical Engineering University of Arkansas

ABSTRACT:

Quickest change detection (QCD) aims to minimize the detection delay of an abrupt change in probability distributions of a random process, subject to certain performance constraints such as the probability of false alarm (PFA). It has a wide range of applications, such as wireless network data analysis, intrusion detection, anomaly detection, quality control, financial market analysis and medical diagnosis, etc. In this talk I will first give an overview of existing quickest change detection algorithms and discuss some of our recent results. Then I will present the application of QCD on the detection of bearing faults of wind turbines (WT). The QCD algorithm is developed by analyzing the statistical behaviors of stator currents generated by the WTs. It is discovered that, at a given frequency, the amplitude of stator current follows the Gamma distribution, and the presence of fault will affect the parameters of the Gamma distribution. Since the signature of a fault can appear in one of multiple possible frequencies, we need to monitor the signals on multiple frequencies simultaneously, and each possible frequency is denoted as a candidate. Based on the unique properties of WT bearing faults, we propose a new multi-candidate QCD (MC-QCD) algorithm that can combine the statistics of signals from multiple candidate frequencies. The new algorithm does not require a separate training phase, and it can be directly applied to the stator current data and perform online detection of various possible bearing faults. The theoretical performance of the proposed algorithm is analytically identified in the form of upper bounds of the PFA and average detection delay (ADD).

BIOGRAPHY: