Classify Maintenance Logs by Failure Type

Business Context

NorthGrid Energy wants to convert free-text equipment logs and operator notes into structured signals for predictive maintenance and incident triage. You need to design NLP features from noisy operational text and build a model that classifies each note into a failure category.

Data

You have 420,000 historical records from substations and field service systems.

• Sources: SCADA alarm summaries, technician logs, shift handoff notes, operator comments
• Text length: 5-180 tokens (median: 32)
• Language: English, with abbreviations, misspellings, equipment codes, timestamps, and copied alarm strings
• Labels: 6 classes — power_loss, sensor_fault, communication_issue, mechanical_issue, scheduled_maintenance, other
• Label distribution: moderately imbalanced; other is 28%, communication_issue is 9%

Success Criteria

A good solution should achieve:

• Macro-F1 >= 0.80 on a held-out test set
• Recall >= 0.88 for power_loss and mechanical_issue
• Stable performance on unseen sites and new operators

Constraints

• Inference must run in <50 ms per note in a batch scoring service
• The pipeline must be explainable enough for operations analysts
• Notes may contain IDs, device names, and repeated boilerplate that should not dominate predictions

Requirements

1. Build an NLP pipeline for noisy logs and operator notes.
2. Describe what text normalization and domain-specific preprocessing you would apply.
3. Implement a baseline using TF-IDF + linear classifier and a stronger transformer-based model in Python.
4. Explain how you would handle abbreviations, rare tokens, duplicated templates, and class imbalance.
5. Define an evaluation plan, including split strategy, metrics, and error analysis.
6. Identify which features from logs or notes are most useful and which may create leakage or poor generalization.