Smart city intelligent rail vehicle fault gene prediction method and system

Abstract

The invention discloses a smart city intelligent rail vehicle fault gene prediction method and system, and the method comprises the steps: collecting the vibration data Xh (0) = [e1, e2, e3,..., en] belongs to R of a train part, and enabling e1, e2,..., en to represent the vibration information of each sampling point on a train; encoding the vibration data into a DNA sequence, extracting features of the DNA sequence, and permuting and combining the features to form a predictable DNA sequence, namely a candidate vehicle part fault gene; and training an ESNs deep echo state network by using the candidate vehicle part fault gene to obtain a prediction model. The method and system can accurately predict the vehicle fault.

Description

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AI Technical Summary

Benefits of technology

This patented describes methods that use advanced testing techniques such as molecular biology or machine vision technologies to accurately diagnose failures in railway cars without relying solely upon previous knowledge about them. These advancements make it easier for companies to maintain their fleet's assets by identifying any future issues before they become too late.

Problems solved by technology

Technological Problem: Current systems are limited by their ability to detect failures accurately due to factors like road conditions or equipment failure modes such as overload or insufficient power supply. Additionally, current approaches require manual intervention from human operators who may take longer than necessary time before identifying any issues with an aircraft system.

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