Work

An enhanced sparsity-driven morphological decomposition for weak impact fault diagnosis in energy equipment

Year: 2025

Type: article

Abstract: Healthy monitoring is an important component in energy equipment intelligent maintenance, particularly for critical rotating machinery such as gas turbines and wind turbine gearboxes, and one of its core technologies is the impact fault extraction. While the existing sparse decomposition applies on identifying weak impact fault, as it often relays on prior knowledge to represent mechanical fault signal through a series of sparse dictionaries, it is easily affected by other interference, such as various noises, modulation component, resulting in the shortcomings of high computational complexity, which severely limits real-time diagnostics in energy equipment operating under extreme conditions such as high temperature and heavy load. As a result, this paper aims to propose an enhanced sparsity signal decomposition (ESSD) which can obtain a discrete frequency component and periodic shock component, respectively, for healthy monitoring of energy equipment, where accurate separation of fault signatures from complex vibration mixtures is crucial to prevent catastrophic failures. Firstly, on account of the morphological differences between shock component and discrete frequency component separately in the time domain and frequency domain, we introduce unit dictionary and Fourier dictionary to represent signal component, respectively. Furthermore, we design a non-convex sparse objective function based on morphological component analysis (MCA) that ensure that ESSD has a strong sensitivity and amplitude fidelity to weak shock component. Finally, we utilize the majorization minimization (MM) algorithm to derive a fast solver for computing the objective function. Simulation and experiment quantitatively prove that the proposed method not only has low computational complexity but also avoids selecting multiple dictionaries and underestimating pulse features, demonstrating significant potential for deployment in smart maintenance platforms for next-generation energy equipment systems.

Source: Journal of low frequency noise, vibration and active control

DOI: 10.1177/14613484251405718

Authors: Lu Yan, Fu-Xing Li, Chi Yuan, Zongmin Hu, Xiao-Chun Tong

Institution: Shanghai Dianji University

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,500