Design and Implementation of a Data Analysis System for Heavy-Duty Engine Fault Diagnosis

Abstract

Aiming at the problems of lagging monitoring of heavy-duty engines under high load, long cycle and complex working conditions, difficulty in fault location and low maintenance efficiency, this paper designs and implements a lightweight, visual and integrated heavy-duty engine fault diagnosis data analysis system (V1.0). The system uses Python as the development platform, integrates five core capabilities of data access, health inspection, feature analysis, fault identification and report generation, and can perform centralized analysis and intelligent diagnosis of key operating parameters such as engine speed, load, coolant temperature, oil pressure, fuel pressure, intake pressure, exhaust temperature, and vibration value. Through data quality verification, multi-dimensional feature visualization, threshold rule discrimination and risk quantitative scoring, the system realizes rapid identification and accurate positioning of typical faults such as abnormal cooling, abnormal lubrication, abnormal fuel, abnormal vibration and abnormal intake. The test results show that the system can complete data import, operation situation analysis and fault diagnosis output without complex deployment conditions, effectively improve the efficiency of fault troubleshooting, reduce the experience dependence of maintenance personnel, and provide reliable technical support for the transformation of heavy-duty engines from passive maintenance to predictive maintenance.