The Enkurgram: A characteristic frequency extraction method for fluid machinery based on multi-band demodulation strategy

Abstract

Modulation frequency extraction of fluid machinery is significant, not only for the condition monitoring and fault diagnosis of cooperative targets in industrial fields but also for object detection and information prejudgment of non-cooperative targets in military applications. However, most existing demodulation methods tend to show poor performance due to the ubiquitous heavy Gaussian and non-Gaussian noise. Especially, narrowband demodulation methods such as Kurtogram are in a dilemma due to the signal characteristic of multi-wideband carrier wave in fluid machinery. In this paper, the Enkurgram is proposed for multiple demodulation frequency bands selection based on the combination of energy factor and shape factor. This method shows excellent demodulation capability in both simulation analysis and applications to fluid machinery. Firstly, an Amplitude-Modulated (AM) signal model with multi-wideband carrier wave is established. Unlike other narrowband demodulation methods, multiple non-overlapping frequency bands are selected by Enkurgram for demodulation based on the composite criterion of Spectral Energy (SE) and Spectral Kurtosis (SK). Moreover, the demodulation performance is quantified by the proposed Signal Characterization Ratio (SCR). Secondly, the effectiveness of Enkurgram is validated by simulation signals under different noise interference situations, including white Gaussian noise, stochastic impulse interference, periodic impulse interference, and contaminated modulation wave. Finally, the proposed method is verified by the actual signals from centrifugal pump and propeller, respectively. The analysis results prove that the Enkurgram has satisfactory demodulation capability in dealing with signals under low Signal-to-Noise Ratio (SNR) levels or with non-Gaussian noise, which is the Achilles’ heel of SK-based methods.