Affiliation:
1. School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou 213164, China
2. Jiangsu Key Laboratory of Green Process Equipment, Changzhou University, Changzhou 213164, China
Abstract
In the typical vibration signal of a reciprocating air compressor, multi-source nonlinear characteristics are exhibited and are often drowned out in background noise, which leads to a lack of robustness in traditional feature analysis methods and difficulty in effective extraction. To address this issue, an algorithm based on ABC-SGMD is proposed in this paper. The Symplectic Geometry Mode Decomposition (SGMD), which is optimized with the Artificial Bee Colony algorithm (ABC), is utilized to decompose the signal, and a multi-feature fusion model is constructed for fault feature extraction. The extracted features are then input into the Self-Adaptive Evolutionary Extreme Learning Machine (SaDE-ELM), and a fault diagnosis model based on ABC-SGMD and SaDE-ELM is established. Ultimately, the signals of reciprocating air compressors and experimental data are used to demonstrate the applicability of the method. The results manifest that this framework has superiority in handling nonlinear and non-stationary signals.
Funder
Project of National Natural Science Foundation of China
Key University Science Research Project of Jiangsu Province
Jiangsu Key Laboratory of Green Process Equipment and National Natural Science Youth Fund
Reference28 articles.
1. Early Fault Diagnosis Model Design of Reciprocating Compressor Valve Based on Multiclass Support Vector Machine and Decision Tree;Yu;Sci. Program.,2022
2. Application of one-dimensional convolutional neural network in fault diagnosis of reciprocating compressor valve;Ma;J. Xi’an Jiaotong Univ.,2022
3. Comparative Study on Fault Feature Extraction Methods of Rolling Bearings Based on Low-rank and Sparse Decomposition;Wang;J. Vib. Shock.,2023
4. Study of Vibration Characteristics of The Reciprocating Compressor on The Offshore Platform Based on Harmonic Wavelet Packet Transform;Huang;Adv. Mater. Res.,2014
5. Siddique, M.F., Ahmad, Z., Ullah, N., Ullah, S., and Kim, J.-M. (2024). Pipeline Leak Detection: A Comprehensive Deep Learning Model Using CWT Image Analysis and an Optimized DBN-GA-LSSVM Framework. Sensors, 24.