Fault Signature Identification for BLDC motor Drive System -A Statistical Signal Fusion Approach

TL;DR

This paper introduces a hybrid multirate signal processing and sensor data fusion method for fault signature identification in BLDC motor drives, improving detection accuracy over traditional techniques.

Contribution

It proposes a novel framework combining multirate signal processing and sensor fusion to enhance fault diagnosis in BLDC motor systems, addressing limitations of existing methods.

Findings

Effective detection of faults in real-time data

Improved accuracy over traditional STFT-based methods

Validated on DSP-based BLDC motor controller

Abstract

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.