NEV Fault Dataset Analysis

This project explores faults in New Energy Vehicles (NEVs) using a real-world dataset and interactive visualizations. By analyzing fault patterns, frequencies, and possible correlations, the goal is to reveal insights that can help improve maintenance strategies and support more reliable NEV design. The visualizations are designed to make complex fault data accessible and engaging, encouraging deeper understanding of the common challenges and failure modes unique to electric and hybrid vehicles. This resource aims to be useful for researchers, engineers, and anyone interested in the technical details behind the transition to cleaner transportation.

This plot maps current against temperature, categorized by fault type. It highlights specific current-temperature combinations where certain faults consistently appear, providing insight into their operational footprint.

This plot presents the range and central tendency of motor speed for each fault type. It clarifies how faults influence motor operation, revealing if a fault is associated with consistent, higher, lower, or more variable speeds.

This histogram depicts the prevalent vibration levels for each fault type. It establishes which vibration intensities correlate with specific faults, aiding in understanding fault severity and unique vibrational characteristics.

This plot shows ambient temperature distributions across different fault types. It identifies if particular faults are more likely under certain environmental temperatures, indicating the influence of external conditions on fault manifestation.

Final Summary

These interactive plots offer a comprehensive view of the NEV fault dataset, revealing critical relationships between operational parameters and various fault conditions. The visualizations highlight distinct patterns in current, temperature, motor speed, vibration, and ambient temperature as they correspond to different fault labels. This analysis provides valuable insights for developing more effective predictive maintenance strategies, enabling earlier fault detection, and improving overall electric vehicle reliability.