This paper describes a nonstationary spectral analysis method and its application to prognosis and diagnosis of automobiles. An instantaneous frequency spectrum is considered first at a single point of time based on the instantaneous representation of autocorrelation. The spectral distortion is then considered on two-dimensional spectrum, and the filtering is introduced into the instantaneous autocorrelations. By the above procedure, the Instantaneous Covariance method (ICOV), the Instantaneous Maximum Entropy Method (IMEM), and the Wigner method are shown and they are unified. The IMEM is used for the time-dependent spectral estimation of vibration and acoustic sound signals of automobiles. A multi-dimensional (M-D) space is composed based on the variables which are obtained by the IMEM. The M-D space is transformed into a simple two-dimensional (2-D) plane by a projection matrix chosen by the experiments. The proposed method is confirmed useful to analyze nonstationary signals, and it is expected to implement automatic supervising, prognosis and diagnosis for a traffic system.