Intelligent Diagnosis for Railway Wheel Flat Using Frequency-Domain Gramian Angular Field and Transfer Learning Network

• Published in: IEEE access Vol. 8; pp. 105118 - 105126
• Main Authors: Bai, Yongliang, Yang, Jianwei, Wang, Jinhai, Li, Qiang
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Axleboxes; Condition monitoring; Diagnosis; Diagnostic systems; fault diagnosis; Force; Frequency domain analysis; Image classification; Intelligent transportation system; knowledge transfer; Learning; Maintenance; Medical imaging; Performance indices; Rail transportation; Railroad wheels; railway safety; Shafts (machine elements); Signal classification; Time series analysis; Time-domain analysis; Vibration; Vibrations; Visual signals; Wheels
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3000068

The intelligent diagnosis of wheel flat based on vibration image classification is a promising research subject for performance maintenance of railway vehicles. However, the image representation method of vibration signal and classification network construction under small samples have become two obstacles to intelligent diagnosis of wheel flat. This paper presents a novel frequency-domain Gramian angular field (FDGAF) algorithm to encode the vibration signal of wheel flat to featured images. Furthermore, a modified transfer learning network is introduced to classify these featured images under small samples without any involvement of prior knowledge. The proposed FDGAF can calculate the Gramian angular matrix of axle box acceleration signal in frequency domain and assign frequency position dependence to the featured images to preserve original characteristic information. Then, these featured images can be intelligent classified by a transfer learning network under the condition of 30 sample without require of prior knowledge. To verify the efficiency of this proposed method, 12 cases of artificial wheel flats are processed on a scaled railway test rig, and their axle box acceleration signals are collected to obtain visual diagnosis results. The verfication proves that FDGAF is able to obtain accurate diagnostic results with high separability, for separability indexes of FDGAF reaches 10.8, 8.7, 14.9, and 5.8. We anticipate that this method will find use in the performance maintenance of railway vehicles and the improvement of industrial condition monitoring.