Work

Rapid Detection of Rotating Machinery Anomalies Based on Gramian Angular Field With Gray Level Co-Occurrence Matrix

Year: 2025

Type: article

Abstract: To address the challenges associated with the early detection of rotor faults, this paper proposes a Dynamic Time-Frequency Feature Mapping Analysis (DTFMA) algorithm that integrates time-frequency feature fusion. Specifically, DTFMA first transforms rotor vibration signals into the time-frequency domain and reconstructs the transformed signals into two-dimensional (2D) feature images— a key step that significantly enhances the distinguishability of fault-related features. Building on this, a dedicated feature fusion strategy is employed to extract and integrate critical information from these 2D feature images; this strategy is further coupled with an adaptive threshold update mechanism, which enables rapid fault alarming. Compared with traditional approaches, this method exhibits superior performance: through the dimensional reconstruction of vibration signals, it achieves fast and accurate capture of early-stage rotor faults. Notably, the novel application of time-frequency feature fusion in the DTFMA framework effectively improves the robustness of early fault features, while the embedded adaptive alarm mechanism ensures both high detection sensitivity and reliable performance—two core requirements for practical early fault detection systems.

Source: IEEE Access

DOI: 10.1109/access.2025.3626011

Authors: Hu Yu, Chunlei Wang, Yizhong Sun, Rupeng Zhu, Weifang Chen

Institutions: Nanjing University of Aeronautics and Astronautics, Aero Engine Corporation of China (China)

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,075

Funders: National Natural Science Foundation of China, Basic Research Program of Jiangsu Province