Nonstationary Motor Fault Detection Using Recent Quadratic Time–Frequency Representations

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.