Competition - Predicting industrial machine downtime 🔧 - Level 3

Predicting Industrial Machine Downtime: Level 3

📖 Background

You work for a manufacturer of high-precision metal components used in aerospace, automotives, and medical device applications. Your company operates three different machines on its shop floor that produce different sized components, so minimizing the downtime of these machines is vital for meeting production deadlines.

Your team wants to use a data-driven approach to predicting machine downtime, so proactive maintenance can be planned rather than being reactive to machine failure. To support this, your company has been collecting operational data for over a year and whether each machine was down at those times.

In this third level, you're going to develop a predictive model that could be combined with real-time operational data to detect likely machine failure. This level is aimed towards advanced learners. If you want to challenge yourself a bit less, check out the other levels!

💾 The data

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

Each row in the table represents the operational data for a single machine on a given day:

"Date" - the date the reading was taken on.
"Machine_ID" - the unique identifier of the machine being read.
"Assembly_Line_No" - the unique identifier of the assembly line the machine is located on.
"Hydraulic_Pressure(bar)", "Coolant_Pressure(bar)", and "Air_System_Pressure(bar)" - pressure measurements at different points in the machine.
"Coolant_Temperature", "Hydraulic_Oil_Temperature", and "Spindle_Bearing_Temperature" - temperature measurements (in Celsius) at different points in the machine.
"Spindle_Vibration", "Tool_Vibration", and "Spindle_Speed(RPM)" - vibration (measured in micrometers) and rotational speed measurements for the spindle and tool.
"Voltage(volts)" - the voltage supplied to the machine.
"Torque(Nm)" - the torque being generated by the machine.
"Cutting(KN)" - the cutting force of the tool.
"Downtime" - an indicator of whether the machine was down or not on the given day.

import pandas as pd
downtime = pd.read_csv('data/machine_downtime.csv')
downtime.head()

💪 Competition challenge

Create a report that covers the following:

Train and evaluate a predictive model to predict machine failure.
Which dataset features are the strongest predictors of machine failure?
Are your predictions more accurate if you model each machine separately?

🧑‍⚖️ Judging criteria

CATEGORY	WEIGHTING	DETAILS
Recommendations	35%	Clarity of recommendations - how clear and well presented the recommendation is. Quality of recommendations - are appropriate analytical techniques used & are the conclusions valid? Number of relevant insights found for the target audience.
Storytelling	35%	How well the data and insights are connected to the recommendation. How the narrative and whole report connects together. Balancing making the report in-depth enough but also concise.
Visualizations	20%	Appropriateness of visualization used. Clarity of insight from visualization.
Votes	10%	Up voting - most upvoted entries get the most points.

✅ Checklist before publishing into the competition

Rename your workspace to make it descriptive of your work. N.B. you should leave the notebook name as notebook.ipynb.
Remove redundant cells like the judging criteria, so the workbook is focused on your story.
Make sure the workbook reads well and explains how you found your insights.
Try to include an executive summary of your recommendations at the beginning.
Check that all the cells run without error