A wayside hotbox system with fuzzy and fault detection algorithms in IIoT environment

Abstract Moving towards industrial internet of things (IIoT) concept is one of the hot issues of the management, control, and maintenance of railway transportation. The wayside hotbox systems are useful for monitoring of overheated axle bearings in the harsh dust environment. In the paper, the fuzzy-based fault detection algorithm (FDA) of the spatially distributed hotbox monitoring system (SDHMS) and its IIoT concept are presented. SDHMS consists of three stand-alone wayside hotbox monitoring systems that are installed in three distant locations: two thermal power plants and a coal mine. The system plays an important role in keeping railway safety, preventive maintenance and accident avoidance. Therefore, the IIoT concept is needed for increasing the reliability of coal transportation and the energy efficiency of electricity production. The main advantage of the presented concept is the application of the FDA at the edge and fog levels as well as the application of a complex fuzzy model for decision making at the cloud level. Edge computing involves signal processing in hotbox systems with the aim of the fault detection and quality assessment of the measured signals. Fog computing involves data processing with the aim of detecting an overheated bearing (temperature, position, axle number and train side), and alarming. Cloud computing is in charge of assessing the bearing condition and the need for replacement and maintenance planning. The proposed algorithm and fuzzy model were validated in real conditions of exploitation, successfully.