1 research outputs found
Fault Signature Identification for BLDC motor Drive System -A Statistical Signal Fusion Approach
A hybrid approach based on multirate signal processing and sensory data
fusion is proposed for the condition monitoring and identification of fault
signal signatures used in the Flight ECS (Engine Control System) unit. Though
motor current signature analysis (MCSA) is widely used for fault detection
now-a-days, the proposed hybrid method qualifies as one of the most powerful
online/offline techniques for diagnosing the process faults. Existing
approaches have some drawbacks that can degrade the performance and accuracy of
a process-diagnosis system. In particular, it is very difficult to detect
random stochastic noise due to the nonlinear behavior of valve controller.
Using only Short Time Fourier Transform (STFT), frequency leakage and the small
amplitude of the current components related to the fault can be observed, but
the fault due to the controller behavior cannot be observed. Therefore, a
framework of advanced multirate signal and data-processing aided with sensor
fusion algorithms is proposed in this article and satisfactory results are
obtained. For implementing the system, a DSP-based BLDC motor controller with
three-phase inverter module (TMS 320F2812) is used and the performance of the
proposed method is validated on real time data.Comment: 7 Pages, 7 figure