Sensor Drift Detection Based on Discrete Wavelet Transform and Grey Models

Abstract

Drift detection has been a difficult problem in the field of sensor fault diagnosis. In this article, a sensor drift detection method using discrete wavelet transform (DWT) and a grey model GM(1,1) is proposed. DWT is used to separate the noise part from the trend part of the sensor data. Then, the GM(1,1) model is used for time series prediction in the trend part. Finally, residuals generated by predicted and current denoised sensor data are calculated and compared with a pre-selected threshold for drift detection. The residuals may not necessarily be Gaussian distribution. Therefore, the pre-selected threshold is chosen by using the kernel density estimation (KDE) method without Gaussian assumption. The effectiveness of the proposed method has been demonstrated using a simulated temperature sensor output from a sensor model on a continuous stirred-tank reactor (CSTR), as well as measurements from a physical temperature sensor in the nuclear power control test facility (NPCTF).

Keywords

• Discrete wavelet transform
• fault detection
• grey models
• kernel density estimation
• sensor drift