Fusion of Model-Based and Data Driven Based Fault Diagnostic Methods for Railway Vehicle Suspension

• 作者: Abdulai Ayirebi Ankrah James Kuria Kimotho Onesmus Mutuku Muvengei
• 来源期刊: 智能学习系统与应用(英文)
• 学科: 工学
• 关键词: Railway Vehicle Suspension System Hybird Model Fault Detection Support Vector Machine
• 年，卷（期）: 2020,（3）
• 页码范围: 51-81
• 页数: 31页
• 分类号: TP3

摘要

<span style="font-family:Verdana;">Transportation of freight and passengers by train is one of the oldest types of transport, and has now taken root in most of the developing countries especially in Africa. Recently, with the advent and development of high-speed trains, continuous monitoring of the railway vehicle suspension is of significant importance. For this reason, railway vehicles should be monitored continuously to avoid catastrophic events, ensure comfort, safety, and also improved performance while reducing life cycle costs. The suspension system is a very important part of the railway vehicle which supports the car-body and the bogie, isolates the forces generated by the track unevenness at the wheels and also controls the attitude of the car-body with respect to the track surface for ride comfort. Its reliability is directly related to the vehicle safety. The railway vehicle suspension often develops faults;worn springs and dampers in the primary and secondary suspension. To avoid a complete system failure, early detection of fault in the suspension of trains is of high importance. The main contribution of the research work is the prediction of faulty regimes of a</span> <span style="font-family:Verdana;">railway vehicle suspension based on a hybrid model. The hybrid model</span><span style="font-family:Verdana;"> framework is in four folds;first, modeling of vehicle suspension system to generate vertical acceleration of the railway vehicle, parameter estimation or identification was performed to obtain the nominal parameter values of the vehicle suspension system based on the measured data in the second fold, furthermore, a supervised machine learning model was built to predict faulty and healthy state of the suspension system components (damage scenarios) based on support vector machine (SVM) and lastly, the development of a new SVM model with the damage scenarios to predict faults on the test data. The level of degradation at which the spring and damper becomes faulty for both pri</span><span style