Can precision manufacturing move beyond reactive maintenance?

Can Precision Manufacturing Move Beyond Reactive Maintenance?

A Data-Driven Investigation into Machine Downtime Patterns

Creator Notes:

Use light mode view for optimal reading.
Plots are interactive, allowing you to explore the data in detail.

Executive Summary

This analysis provides clear targets for maintenance optimization and downtime reduction, with specific focus areas identified for immediate intervention.

Timeline Analysis

First reading: November 24, 2021 (Wednesday)
Last reading: July 3, 2022 (Sunday)
Critical period: March 2022 showed highest failure concentration
Pattern: Mid-week periods consistently record more failures

Torque Performance

Mean value: 25.23 Nm (critical operational threshold)
Two distinct operating ranges identified:
- Lower cluster: 13-19 Nm
- Higher cluster: 23-30 Nm
Mean value acts as transition point where failures frequently occur

Assembly Line Performance

Shopfloor-L1: 454 failures (51.95% downtime)
Shopfloor-L2: 396 failures (49.01% downtime)
Shopfloor-L3: 415 failures (50.73% downtime)
Critical gap: 58 more failures in L1 versus L2

Key Insights

All assembly lines show concerning failure rates around 50%
Mid-week operations face higher risk of machine failures
Torque transitions through 25.23 Nm represent critical failure points
March 2022 requires particular attention in maintenance planning
Shopfloor-L1 maintenance practices need immediate review

Immediate Actions Required

Implement torque monitoring at identified critical ranges
Review March production schedules and maintenance timing
Standardize maintenance procedures across all assembly lines

I. Background

In the precision manufacturing sector, where components must meet exacting standards for aerospace, automotive, and medical applications, machine reliability directly impacts product quality and delivery schedules. A leading manufacturer operates three specialized machines, each dedicated to producing components of different sizes. These machines represent the backbone of the production line, where every minute of downtime translates to delayed shipments and potential revenue loss.

The traditional approach to machine maintenance has been reactive — waiting for failures to occur before taking action. This method leads to unpredictable production schedules, rushed repairs, and increased maintenance costs. To address these challenges, the company implemented a comprehensive data collection system over a year ago, recording various operational parameters that might indicate potential machine failures.

II. Objectives

This analysis examines operational data from three manufacturing assembly lines producing precision components for aerospace, automotive, and medical applications. The core focus is understanding machine downtime patterns to enable proactive maintenance planning.

It aims to:

Establish the complete timeline of machine monitoring
Analyze average torque values and operating ranges
Identify the most problematic assembly line based on downtime frequency

III. The data

The operational data contains detailed measurements from each machine, recorded daily:

Column Name	Description	Unit	Significance
Date	Daily timestamp of readings	YYYY-MM-DD	Tracks temporal patterns and maintenance history
Machine_ID	Unique machine identifier	Text	Enables machine-specific analysis and comparisons
Assembly_Line_No	Production line location	Integer	Maps physical layout and workflow dependencies
Hydraulic_Pressure	Hydraulic system pressure	bar	Indicates fluid power system health
Coolant_Pressure	Cooling system pressure	bar	Monitors heat dissipation efficiency
Air_System_Pressure	Pneumatic system pressure	bar	Reflects compressed air system status
Coolant_Temperature	Cooling system temperature	Celsius	Tracks thermal management effectiveness
Hydraulic_Oil_Temperature	Hydraulic fluid temperature	Celsius	Indicates system stress and oil condition
Spindle_Bearing_Temperature	Bearing temperature	Celsius	Monitors critical component health
Spindle_Vibration	Spindle oscillation	micrometers	Detects mechanical imbalances
Tool_Vibration	Cutting tool movement	micrometers	Indicates tool wear and stability
Spindle_Speed	Rotational velocity	RPM	Measures cutting performance
Voltage	Electrical input	volts	Monitors power supply stability
Torque	Rotational force	Nm	Indicates mechanical load
Cutting	Tool force	KN	Measures material removal effort
Downtime	Operational status	Boolean	Records machine availability

The company has stored the machine operating data in a single table, available in 'data/machine_downtime.csv'.

# Install necessary packages
!pip install plotly-calplot
!pip install exploralytics

# Import essential libraries for data manipulation and visualization
import plotly.graph_objects as go
import pandas as pd
import numpy as np
import plotly.figure_factory as ff
import plotly.express as px
from plotly.subplots import make_subplots

# Import library for visualizing missing data
import matplotlib.pyplot as plt
import missingno as msno 

# Import specific visualization tools
from exploralytics.visualize import Visualizer
from plotly_calplot import calplot

# Load the dataset and display the first 10 rows
data = pd.read_csv('data/machine_downtime.csv')
display(data.head(10))

# Display information about the dataset including the data types and non-null counts for each column
data.info()

IV. Data Completeness Check

Hidden code

Let me break down what the following visualizations tell us about the machine sensor data in simple terms:

Overall Data Quality

We have 40,000 total data points from various machine sensors
Only 143 measurements are missing (0.36% of all data)
This tells us the data collection system is working very well, with over 99% complete data

Missing Data Patterns (Matrix View - Left Plot)

The white gaps show where data is missing
These gaps appear scattered rather than clustered
This random pattern suggests we're dealing with occasional sensor hiccups rather than systematic failures

Missing Values by Sensor (Bar Chart - Middle Plot)

Torque measurements have the most gaps (21 missing readings)
Followed by Coolant Pressure (19 missing) and Air System Pressure (17 missing)
Most other sensors are missing fewer than 10 readings

Related Sensor Issues (Heatmap - Right Plot)

The blue squares (0.6-0.8) show moderately strong relationships between missing values
For example, when Coolant Temperature readings are missing, there's often missing data in Spindle Speed. This might indicate that certain sensor issues tend to happen together

Given the high data quality and identified sensor relationships, we can proceed confidently with our objectives. The next step is to analyze how these sensor measurements relate to actual downtime events.

V. Analysis Report

1. What is the first and last date readings were taken on?

calendar_data = data.groupby('Date', as_index=False)['Machine_ID'].count()
fig = calplot(
   calendar_data,
   x="Date",
   y="Machine_ID",
   gap=1,
   colorscale='Aggrnyl',
   name='Machine ID Count',
   date_fmt='%d-%m-%Y'  # Ensure the date format is specified correctly
)
fig.update_layout(
   title={
       'text': 'Heatmap showing the number of machines monitored each day from Nov 24, 2021 to Jul 3, 2022<br>' +
               '<sup>The lighter the color, the higher count of machine checked</sup>',
       'y':0.90,
       'x':0.05,
       'xanchor': 'left',
       'yanchor': 'top',
       'font': {
           'size': 20,
           'color': 'black'  # Explicitly setting color to black
       }
   },
   height=400,
   margin=dict(t=100, b=20)
)
fig.show()