Crack damage identification and localisation on metro train bogie frame in IoT using guided waves

Abstract

The Internet of Things (IoT) is one of the key components of metro train safety as an emerging development approach due to its great potential to advance environmental sustainability. A bogie is a key component for carrying a passenger's vehicle body. Its damage and defects can destroy fluent operations and better service for train operation. The critical problem of traditional non-destructive examination for bogie is the high cost of labour and environment. Because they are in effect only after the whole vehicle has to be split into many sub-components and paint removed. This study provides an IoT approach and practice for bogie crack identification of bogie. The presented method can achieve simple and efficient detection of damage with cheap PZT sensor network. Its advantages is that non-split work and non-removal paint pollution. Compared with traditional detection methods, this method is more sensitive to a small area of internal damage and can identify the level of damage and location of the bogie plate frame even with the dirty and non-smooth surface.