Railway Wheel Condition Diagnoses with the Assistance of ANFIS Technique

Prominent purpose is to design AI (Artificial Intelligence) under predicting model for diagnosis conditions of railway wheel demonstrates how AI technique proves as a valuable alternative for remaining methods under sophisticated mathematical models during this can be applicable on problem of wheel-rail contact force measurement. This intention originates in obstacles such as identifying conditions on rail wheel that computes complex as well as consumes time. Such urge is integrating AI methods for predicting conditions of railway wheel to overcome the obstacles. The database holds attributes of input such as train velocity, vibration level and frequency, conversely condition of wheel into output attributes. Eventually, such database uses to ANFIS (Adaptive Neuro-Fuzzy Inference System) for identifying the conditions of railway wheel. Here, diagnosis conditions of railway wheel can be categorized into Dangerous (D), Low Damaged (LD), Faulty (F) and Good (G). Such investigation evidently done on manual computation AI methods can be proficient for predicting conditions of railway wheel during less computational time. Above contest methods, ANFIS shows superior predicting performance of 96.5% accuracy for diagnosing conditions of railway wheel consequent preventive maintenance requirement.