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

Fault detection in electric motors operating under non-stationary operating conditions has been gaining importance, due to the fact that motors are used in many applications such as actuators in the aerospace and transportation industries operate under conditions that rapidly vary with time. In recent times, a plethora of new time-frequency distributions have appeared that are inherently suited to the analysis of non-stationary signals while offering superior frequency resolution characteristics. The Zhao-Atlas-Marks (ZAM) distribution is one such distribution. This paper proposes the use of these new time-frequency distributions to enhance non-stationary fault diagnostics in electric motors. One common myth has been that the quadratic time-frequency distributions are not suitable for commercial implementation. This paper addresses this issue in detail too. Optimal discrete time implementations of some of these quadratic time-frequency distributions are explained. These TFRs have been implemented on a digital signal processing (DSP) platform to demonstrate that the proposed methods can be implemented commercially