A novel health state prediction approach based on artificial intelligence combination strategy for compensation capacitors in track circuit

• Published in: The Journal of supercomputing Vol. 80; no. 8; pp. 11661 - 11681
• Main Authors: Wang, Conghui, Yang, Shiwu, Liu, Chang
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.05.2024; Springer Nature B.V
• Subjects: Artificial intelligence; Artificial neural networks; Capacitors; Compensation; Compilers; Computer Science; Degradation; High speed rail; Interpreters; Knowledge; Maintenance; Mathematical models; Methods; Monitoring systems; Neural networks; Processor Architectures; Programming Languages; Search algorithms; Structural health monitoring; Track circuits; Health state prediction; Sparrow search algorithm (SSA); Deep neural network; Compensation capacitor; Track circuit
• ISSN: 0920-8542; 1573-0484
• DOI: 10.1007/s11227-024-05888-2

The health management of railway signal equipment in the high-speed railway is a key link between intelligent operation and maintenance. Accurately predicting the health state of compensation capacitors is of great significance to ensure the reliable work of track circuits. This paper proposes an improved deep neural network algorithm focusing on the problem of long-term accurate health forecasts for compensation capacitors. First, establishing a transmission state model for degradation mechanism mining, the difference function that can quantitatively evaluate features is defined by piecewise processing cab signaling receiving voltage. Introducing the degradation model, predictive driving under both model and data is implemented. Then, the convolutional neural networks and bidirectional long–short-term memory are combined and improved to construct a novel artificial intelligence combination strategy, while parameters are optimized based on the sparrow search algorithm. Finally, facing the conditional repair of compensation capacitors, we set a reasonable threshold for the occurrence of hidden dangers to complete fault warning. This novel and practical approach effectively explores the procedure of prognosis and health management, while the refined maintenance will better utilize current monitoring information, helping the intelligence and accuracy of safety control decision-making.