Major project Manufacturing, Robotics & Industry 4.0 Case Studio

Production line failure prediction

Inspired by publicly reported work at Bosch

Predict which parts will fail final quality checks from measurements taken along the production line.

The brief

Bosch has written about using AI to find process faults early across many production lines, and it released anonymised production-line data for a public competition. Using that data, predict which parts will fail their final quality check from measurements and timings at each station, find the stations that matter most, and design how alerts would reach the line. Good looks like: Matthews correlation coefficient against simple baselines, handling of very rare failures and huge sparse data, station-level explanations, and an alert design for engineers.

Read the case first

What Bosch has said in public about this kind of work, on its own pages:

Make it your own

Pick a line you can picture: a two-wheeler assembly line, a bottling plant or a textile mill. Add the costs of a missed failure and a false alarm for that line, and show how the best threshold moves.

AITeky has no link with Bosch, which has not reviewed or endorsed this brief. The brief only draws on what the company has published, uses open data rather than its data, and is a learning exercise: don't present your project as Bosch's work or system.

What you will build

Explore the numeric, categorical and timing data and its sparsity
Engineer features from station paths and timings
Train gradient boosting with imbalance handling
Evaluate with MCC and pick an alert threshold
Explain failures by station
Design an alert dashboard and a retraining plan

Free, open data to build it with

It takes a minute with your email. AiTeky Pass (₹999/month or ₹9,999/year) is free for your first 30 days.