Multi-source fault diagnosis of chiller plant sensors based on an improved ensemble empirical mode decomposition Gaussian mixture model

Abstract

As the core of the heating, ventilation, and air-conditioning (HVAC) control system, the sensor directly determines whether the HVAC control system is running normally. Poor natural and artificial environments can cause sensors to simultaneously fault, called multi-source sensor faults. Compared to the other methods, the Gaussian mixture model (GMM) has a better effect on high-dimensional data classification. In this paper, an improved ensemble empirical mode decomposition hard threshold denoising (EEMD-HTD) is first proposed to denoise the collected data and highlight each sensor’s fault characteristics. Secondly, the single-source and multi-source faults of the sensors are analyzed by the GMM at different points. The above fault diagnosis processes are collectively referred to as EEMD-HTD-GMM. Experimental results show that the EEMD-HTD-GMM has a higher diagnostic ability and distinguishes between the fault types that cause GMM classification confusion.

Keywords

• Multi-source sensor fault
• Ensemble empirical mode decomposition hard threshold denoising (EEMD-HTD)
• Gaussian mixture model (GMM)