Predict Factory Equipment Failures | Dataford Interview Questions

Business Context

Northline Components operates 18 manufacturing lines across 3 plants and loses significant production time to unplanned machine stoppages. The operations team wants a model that predicts whether a machine will fail in the next 7 days so maintenance can be scheduled before breakdowns occur.

Dataset

You are given historical machine-level daily records built from sensor streams, maintenance logs, and machine metadata.

Feature Group	Count	Examples
Sensor aggregates	22	avg_temperature_24h, vibration_rms_24h, pressure_std_24h, motor_current_max_24h
Usage and load	8	runtime_hours_7d, cycles_completed_7d, load_factor_avg, shift_count
Maintenance history	7	days_since_last_service, service_type_last, parts_replaced_30d, prior_failures_90d
Machine metadata	6	machine_type, line_id, manufacturer, install_age_days, plant_id
Derived trend features	9	temp_slope_7d, vibration_delta_3d, rolling_fault_code_count

Size: 420K machine-day rows covering 30 months, 52 features
Target: Binary label indicating whether the machine fails within the next 7 days
Class balance: Failures are rare; about 4.6% positive class
Missing data: 12% missing in some sensor fields due to telemetry dropouts; 6% missing in maintenance fields for newly installed machines

Success Criteria

A good solution should identify at least 75% of upcoming failures while keeping precision high enough that maintenance teams are not overwhelmed. The model should produce ranked risk scores and explain the main drivers of each alert.

Constraints

Batch scoring runs every 6 hours for ~9,000 active machines
Predictions must be explainable to reliability engineers
False negatives are costly, but excessive false positives create unnecessary maintenance work
Retraining should be feasible on a monthly cadence

Deliverables

Build a binary classification pipeline for failure prediction within 7 days.
Explain feature engineering, leakage prevention, and validation strategy.
Compare at least one baseline model against a stronger tree-based model.
Choose an operating threshold based on maintenance capacity and failure recall.
Report final test metrics and the most important features.

Problem

Business Context

Dataset

You are given historical machine-level daily records built from sensor streams, maintenance logs, and machine metadata.

Feature Group	Count	Examples
Sensor aggregates	22	avg_temperature_24h, vibration_rms_24h, pressure_std_24h, motor_current_max_24h
Usage and load	8	runtime_hours_7d, cycles_completed_7d, load_factor_avg, shift_count
Maintenance history	7	days_since_last_service, service_type_last, parts_replaced_30d, prior_failures_90d
Machine metadata	6	machine_type, line_id, manufacturer, install_age_days, plant_id
Derived trend features	9	temp_slope_7d, vibration_delta_3d, rolling_fault_code_count

Size: 420K machine-day rows covering 30 months, 52 features
Target: Binary label indicating whether the machine fails within the next 7 days
Class balance: Failures are rare; about 4.6% positive class
Missing data: 12% missing in some sensor fields due to telemetry dropouts; 6% missing in maintenance fields for newly installed machines

Success Criteria

Constraints

Batch scoring runs every 6 hours for ~9,000 active machines
Predictions must be explainable to reliability engineers
False negatives are costly, but excessive false positives create unnecessary maintenance work
Retraining should be feasible on a monthly cadence

Deliverables

Build a binary classification pipeline for failure prediction within 7 days.
Explain feature engineering, leakage prevention, and validation strategy.
Compare at least one baseline model against a stronger tree-based model.
Choose an operating threshold based on maintenance capacity and failure recall.
Report final test metrics and the most important features.

Problem

Business Context

Dataset

You are given historical machine-level daily records built from sensor streams, maintenance logs, and machine metadata.

Feature Group	Count	Examples
Sensor aggregates	22	avg_temperature_24h, vibration_rms_24h, pressure_std_24h, motor_current_max_24h
Usage and load	8	runtime_hours_7d, cycles_completed_7d, load_factor_avg, shift_count
Maintenance history	7	days_since_last_service, service_type_last, parts_replaced_30d, prior_failures_90d
Machine metadata	6	machine_type, line_id, manufacturer, install_age_days, plant_id
Derived trend features	9	temp_slope_7d, vibration_delta_3d, rolling_fault_code_count

Size: 420K machine-day rows covering 30 months, 52 features
Target: Binary label indicating whether the machine fails within the next 7 days
Class balance: Failures are rare; about 4.6% positive class
Missing data: 12% missing in some sensor fields due to telemetry dropouts; 6% missing in maintenance fields for newly installed machines

Success Criteria

Constraints

Batch scoring runs every 6 hours for ~9,000 active machines
Predictions must be explainable to reliability engineers
False negatives are costly, but excessive false positives create unnecessary maintenance work
Retraining should be feasible on a monthly cadence

Deliverables

Build a binary classification pipeline for failure prediction within 7 days.
Explain feature engineering, leakage prevention, and validation strategy.
Compare at least one baseline model against a stronger tree-based model.
Choose an operating threshold based on maintenance capacity and failure recall.
Report final test metrics and the most important features.

Problem

Business Context

Dataset

You are given historical machine-level daily records built from sensor streams, maintenance logs, and machine metadata.

Feature Group	Count	Examples
Sensor aggregates	22	avg_temperature_24h, vibration_rms_24h, pressure_std_24h, motor_current_max_24h
Usage and load	8	runtime_hours_7d, cycles_completed_7d, load_factor_avg, shift_count
Maintenance history	7	days_since_last_service, service_type_last, parts_replaced_30d, prior_failures_90d
Machine metadata	6	machine_type, line_id, manufacturer, install_age_days, plant_id
Derived trend features	9	temp_slope_7d, vibration_delta_3d, rolling_fault_code_count

Size: 420K machine-day rows covering 30 months, 52 features
Target: Binary label indicating whether the machine fails within the next 7 days
Class balance: Failures are rare; about 4.6% positive class
Missing data: 12% missing in some sensor fields due to telemetry dropouts; 6% missing in maintenance fields for newly installed machines

Success Criteria

Constraints

Batch scoring runs every 6 hours for ~9,000 active machines
Predictions must be explainable to reliability engineers
False negatives are costly, but excessive false positives create unnecessary maintenance work
Retraining should be feasible on a monthly cadence

Deliverables

Build a binary classification pipeline for failure prediction within 7 days.
Explain feature engineering, leakage prevention, and validation strategy.
Compare at least one baseline model against a stronger tree-based model.
Choose an operating threshold based on maintenance capacity and failure recall.
Report final test metrics and the most important features.