Optimized railway track health monitoring system based on dynamic differential evolution algorithm

• Published in: Measurement : journal of the International Measurement Confederation 2020-02, Vol.152, p.107332, Article 107332
• Main Authors: Chellaswamy, C., Krishnasamy, M., Balaji, L., Dhanalakshmi, A., Ramesh, R.
• Format: Article
• Language: English
• Subjects: Abnormalities; Accelerometers; Algorithms; Axleboxes; Background noise; Defects; Dynamic differential evolution optimization; Evolutionary algorithms; Evolutionary computation; Fault detection; Genetic algorithms; Global positioning systems; GPS; Irregularities; Location of the abnormality; Mechanical systems; MEMS accelerometer; Particle swarm optimization; Passenger safety; Rail track health monitoring; Rail transportation; Railroad accidents & safety; Railroads; Railway networks; Railway tracks; Research projects; Satellite navigation systems; Sensors; Shafts (machine elements); Signal processing; Structural health monitoring; Undercarriages
• ISSN: 0263-2241; 1873-412X
• DOI: 10.1016/j.measurement.2019.107332

•A novel railway track abnormality is analyzed using optimization algorithm.•Automatic fault location estimation will perform when the GPS signal is absent.•The time and frequency domain response has been analyzed.•The comfort and ride quality of the rail transport will be increased.•This system eliminates the requirements of manual inspection of railway tracks. This paper describes a new method to check for defects in railway tracks for improving passenger safety and comfort. The irregularities in the railway tracks are the fundamental cause of vibration, and different research projects are currently in progress for optimizing the process. External background noises causes the signals sent from the sensors to be distorted. In order to solve this issue, the Railway track Health Monitoring system uses a Dynamic differential Evolution algorithm (RHMDE) for identifying defects in railway tracks. Micro Electro Mechanical System (MEMS) accelerometers are mounted vertically and horizontally on the bogie and axle-box for sensing abnormalities. To locate the irregularities, a new method is included in the proposed RHMDE method. It automatically updates the location of an abnormality even if the signal from the Global Positioning System (GPS) is absent. Four different railway track problems were used for the experimental study, and the time and frequency domain responses were studied. The experimental setup of the proposed RHMDE is tested and compared the Chaos Particle Swarm Optimization (CPSO) and Genetic Algorithm (GA). The experiment results from the experiment prove that the proposed RHMDE method is the superior method for detecting faults in railway tracks. The RHMDE method will greatly improve the quality of railway transportation through detecting the track faults effectively and consistently.