Predicting Industrial Machine Downtime: Level 1
Executive Summary
Machine downtime in precision manufacturing not only disrupts production schedules but also significantly increases operational costs. This report analyzes machine operational data from November 24, 2021, to July 3, 2022, aiming to provide actionable insights that enable proactive maintenance scheduling. The focus of this report is to understand key metrics, identify critical problem areas, and offer data-driven recommendations to enhance machine reliability and operational efficiency.
Summary of Key Findings:
- Data Span: Data collected over seven months, providing a robust foundation for identifying trends.
- Key Metric - Torque: The average torque was measured at 25.23 Nm, indicative of typical machine load.
- Primary Downtime Issue: Shopfloor-L1 recorded the highest downtime, indicating the need for focused maintenance efforts.
- Correlation Insights: Analysis showed strong correlations between hydraulic pressure and downtime, suggesting operational vulnerabilities.
This report aims to support the maintenance team in implementing proactive measures that reduce downtime, optimize resource allocation, and ultimately improve production efficiency.
Step 1: Introduction
In a high-precision manufacturing environment, such as aerospace and automotive components, minimizing machine downtime is crucial to meet production deadlines and control costs. This analysis aims to explore the operational data of machines and provide insights to help prioritize maintenance efforts effectively. The focus will be on identifying the following:
- The date range of readings.
- The average torque of the machines.
- The assembly line with the highest frequency of machine downtime.
By using visual storytelling techniques, we aim to turn complex data into understandable and actionable insights that can help decision-makers quickly grasp key issues and take corrective actions.
Step 2: Code to Explore the Dataset
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
# Load the dataset
file_path = 'data/machine_downtime.csv' # Adjust the path as needed
downtime = pd.read_csv(file_path)
# Convert Date column to datetime format
downtime['Date'] = pd.to_datetime(downtime['Date'], dayfirst=True)
# Display dataset overview
print(downtime.info())
print(downtime.head())Step 3: Detailed Analysis
3.1. Date Range of Data Collection
first_date = downtime['Date'].min()
last_date = downtime['Date'].max()
print(f"Data collection spans from {first_date} to {last_date}.")The data spans a period from November 24, 2021, to July 3, 2022. Imagine this as a timeline, where each data point represents a day in the life of these machines.
Over this period, every operational parameter and downtime event was captured, providing us with a comprehensive understanding of the machines' health and performance.
3.2. Average Torque Analysis
average_torque = downtime['Torque(Nm)'].mean()
print(f"The average torque is {average_torque:.2f} Nm.")Torque is akin to the strength of a machine. The average torque of 25.23 Nm represents the typical load that machines bear during their operation.
This metric acts as a benchmark—like the pulse of a human being—that tells us whether the machines are operating normally or under stress.
By monitoring deviations from this average, we can potentially identify issues before they lead to downtime.
3.3. Downtime Analysis by Assembly Line