Data Scraping for Excavator Reliability Test

Internship project during [HD Hyundai Infracore] (2021)

Key skills: Web Crawling / Python

Motivation

The durability of the heater switch is determined by how much it has operated normally before failure. If the average number of operations throughout the year can be calculated, it's possible to discern the average lifespan of the heater switch. The heater switch operates only while the excavator is running in sub-zero conditions, making the task of calculating the annual average operations quite tedious. Moreover, our company plans to export products worldwide, and with the annual temperature distribution varying by region, manually researching the annual temperature distribution for major global regions would not only be time-consuming but also carries a significant risk of human error.

Automating Temperature Data via Web Scraping

To address this issue, I utilized a method known as web scraping to collect temperature data for major regions worldwide over a period of three years. With the gathered temperature data, along with the typical operational hours of the excavator and identifying when the temperatures drop below zero, I was able to calculate how many hours and thus, how many times the heater switch operates annually.

Result

As a result, my program was able to calculate the sub-zero periods throughout the year for 26 regions domestically and 97 countries globally within 30 minutes. These findings were reported to the executive team, facilitating the smooth planning of durability tests. This not only streamlined the process but also provided a data-driven basis for conducting durability assessments, thereby potentially enhancing the accuracy and efficiency of the testing plan. By automating the data collection and analysis process, I was able to significantly expedite the decision-making process, providing valuable insights that could help ensure the reliability and longevity of the heater switch in different climate conditions worldwide.