Non-Stationary Motor Fault Detection Using Recent Quadratic Time-Frequency Representations

Abstract

Abstract-As the use of electric motors increases in the aerospace and transportation industries where operating conditions continuously change with time, fault detection in electric motors has been gaining importance. Motor diagnostics in a nonstationary environment is difficult and often needs sophisticated signal processing techniques. In recent times, a plethora of new time-frequency distributions has appeared, which are inherently suited to the analysis of nonstationary signals while offering superior frequency resolution characteristics. The Zhao-Atlas-Marks distribution is one such distribution. This paper proposes the use of these new time-frequency distributions to enhance nonstationary fault diagnostics in electric motors. One common myth has been that the quadratic time-frequency distributions are not suitable for commercial implementation. This paper also addresses this issue in detail. Optimal discrete-time implementations of some of these quadratic time-frequency distributions are explained. These time-frequency representations have been implemented on a digital signal processing platform to demonstrate that the proposed methods can be implemented commercially.

The Decision making System for Condition Monitoring of Induction Motors Based on Vector Control Model

“…Some simple threshold selection schemes were reported based on the expected defect severity level [38]. In the meantime, pre-determined threshold schemes reported in the literature have limited performance evaluation as they have been commonly designed based on limited analysis and signal statistics have not been considered in threshold design.…”

“…In [38], the threshold was derived with a pre-determined percentage of the stator current amplitude and was shown to be an efficient way to analyze the fault severity level. In order to detect severe fault signatures, the pre-determined threshold was selected at 1% of the fundamental current signal (−40 dB) as shown in Figure 10.…”

“…Notably, the predetermined threshold designing techniques used in [36][37][38]53] needs to be updated according to the motor operating point (load) which is quite difficult in real time condition monitoring applications. However, the proposed threshold scheme in this paper adopt itself automatically according to the motor operating situations and thus, can perform reliable decisions on the presence of defects.…”

“…MSE based noise estimation has been performed using an average of the bias compensated signal and is given below [38,50,51]:…”

Automated Fault Detection System for Wind Farms

“…A fault diagnosis method based on signal analysis and recognition is presented [12]. Time-frequency representations have been proposed in the literature [13][14][15]. These techniques have high complexity and poor resolution [16].…”