A Sensor-based Modified FMD Method to identify fault feature for mechanical fault diagnosis of ship-borne antennae

• Published in: IEEE access Vol. 11; p. 1
• Main Authors: Li, Zipeng, Zhou, Zitong, Zhou, Xiaoyi
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Antennas; Condition monitoring; Decomposition; Demodulation; Fault diagnosis; Feature extraction; Feature Mode Decomposition; Filter banks; Filtering theory; Finite impulse response filters; FIR filters; Identification methods; Mechanical fault diagnosis; Navigation safety; Powertrain; ship-borne antenna transmission system; Signal monitoring; Vibrations
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3269288

It is necessary to perform condition monitoring and fault identification on ship-borne antennae to ensure navigation safety. However, timely fault identification of key parts in a complicated drivetrain of the ship-borne antenna is still a challenging task since fault features are usually modulated and inevitably submerged by heavy noise. Therefore, a new adaptive feature extraction method based on Feature Mode Decomposition (FMD) is proposed in this paper for diagnostic purpose. First, scale transform is applied on the spectrum of the monitoring signal to determine the parameters of FMD, including the number of FIR filters, the location of each filter and the order of the filter bank. Second, a modified feature mode decomposition (MFMD) algorithm is proposed to adaptively extract mono-component modes and combine similar modes for weak feature enhancement. Finally, the combined modes are analyzed for mechanical fault identification based on Hilbert demodulation. Two application cases show that the proposed method owns superior performance during feature extraction, compared with the traditional methods.