Holo-hilbert square spectral analysis: A new fault diagnosis tool for rotating machinery health management

Abstract

The Holo-Hilbert spectral analysis (HHSA) is an emerging analysis tool for rotating machinery fault diagnosis. It has an excellent performance in reflecting the cross-scale coupling relationship in the nonlinear and non-stationary vibration signals, by combining the two-layer empirical mode decomposition structure. However, the random phase shift involved in the envelope signal analysis limits the wide application of HHSA and may lead to an inaccurate instantaneous frequency estimation. To solve the aforementioned problem and acquire an accurate modulation relationship between amplitude-modulated (AM) and frequency-modulated (FM) characteristics, a novel non-stationary signal analysis method, namely Holo-Hilbert square spectral analysis (HHSSA), is put forward in this paper by combing the square envelope analysis. The proposed HHSSA is integrated to compute a new quantity called the AM-marginal spectrum to reveal the separation efficiency of fault characteristics. Moreover, the relationships between HHSSA with the commonly used second-order cyclostationary methods (Cyclic Modulation Spectrum and Fast Spectral Correlation) are further investigated. Furthermore, the analysis of simulation, experimental data, and industrial data are conducted to demonstrate the high resolution and superior modulation relationship recognition capability of HHSSA. The analysis results further prove that the proposed HHSSA-based AM-marginal spectrum has the best fault identification performance and robustness compared with traditional methods.