Abstract
Predictive diagnostics in industry and in railroad transportation on the basis of the Industrial Internet of Things is analyzed. Attention focuses on predictive diagnostics in the maintenance and repair of locomotives and motorized rail cars and prospects for a differential approach to operational support. Empirical methods are employed: (1) comparison, so as to identify the similarities and differences of predictive diagnostic systems of the same type used for the maintenance and repair of locomotives and motorized rail cars in the depot; (2) description: itemization of the available data regarding predictive diagnostic systems for the maintenance and repair of locomotives and motorized rail cars. A positional map is prepared, showing existing predictive diagnostic systems in terms of the manufacturers’ characteristics and the type of locomotive or motorized rail car. On the basis of the chart, a differential approach to the introduction of predictive diagnostic systems for specific companies is adopted. Predictive diagnostic systems are compared in terms of eight parameters used in maintenance and repair of locomotives and motorized rail cars in the depot. The advantages and disadvantages of each system are noted; a ranking is prepared. The analysis leads to the conclusion that the inefficient existing maintenance system based on standard preventive measures must be replaced by a predictive system.
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Pudovikov, O.E., Tarasova, V.N. & Degtyareva, V.V. Predictive Diagnostics of Rolling Stock and the Industrial Internet of Things. Russ. Engin. Res. 43, 987–990 (2023). https://doi.org/10.3103/S1068798X23080282
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DOI: https://doi.org/10.3103/S1068798X23080282