Natural Language Processing for Maintenance and Compliance

The Business Problem: Extracting Insight from Unstructured Text

Utilities produce vast amounts of text data that rarely gets analyzed systematically. Maintenance crews write inspection notes and failure reports. Control centers log operational events and equipment alarms. Regulatory compliance audits generate lengthy documentation and findings. These records often contain valuable information about recurring issues, equipment behavior, or compliance risks.

The challenge is that this information is unstructured. Unlike SCADA readings or meter data, logs and reports are free-form text, written in different styles by different people. Searching them manually is time-consuming, and important patterns or trends can easily be overlooked. When incidents occur, investigators must sift through thousands of records to piece together root causes. Compliance teams face similar burdens, manually compiling evidence for audits from disparate systems and document sets.

Without tools to systematically process this text, utilities lose opportunities to learn from past events, identify emerging risks, and streamline compliance reporting.

The Analytics Solution: Applying Natural Language Processing

Natural language processing (NLP) converts unstructured text into structured data for analysis. Utilities can apply NLP to maintenance logs, incident reports, regulatory documents, and even customer service notes.

One approach is classification. Models can be trained to label records automatically, such as distinguishing routine inspection notes from failure events. This allows utilities to filter and prioritize records quickly. Another is entity extraction, which identifies key terms—like asset IDs, equipment types, or failure modes—from text. These structured elements can then be linked to operational or asset data for further analysis.

More advanced NLP techniques can summarize long documents, flag unusual or high-risk language in reports, or cluster similar incidents to identify systemic issues. This reduces the burden on staff while uncovering insights hidden in narrative text.

Compliance and Operational Benefits

NLP has immediate applications in compliance. Regulatory standards like NERC CIP or state-level reliability requirements often require documented evidence of inspections, testing, and maintenance. Automating extraction of this evidence from logs and work orders accelerates audit preparation and reduces the risk of missing required documentation.

In operations, analyzing historical incident reports with NLP can reveal patterns, such as recurring failures linked to specific equipment models or environmental conditions. Control room logs can be mined for operational anomalies or deviations that merit further review. By connecting text-based records to other datasets, utilities create a more comprehensive picture of asset health and operational performance.

Transition to the Demo

In this chapter’s demo, we will explore how NLP can be used to process maintenance and compliance text. We will:

• Classify inspection notes as routine or indicating a potential failure.
• Extract asset references and failure modes from sample text.
• Show how these results could feed into maintenance prioritization or compliance dashboards.

By turning narrative records into structured, searchable information, NLP provides utilities with a new layer of insight that complements sensor and asset data, improving both operational awareness and regulatory readiness.

Code

"""
Chapter 11: Natural Language Processing for Utilities
Analyze maintenance logs and regulatory documents using NLP (spaCy).
"""

import pandas as pd
import re
import spacy
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report

def generate_maintenance_logs():
    """
    Generate synthetic maintenance logs with failure vs. routine labels.
    """
    logs = [
        "Transformer oil leak detected near bushing. Immediate repair required.",
        "Routine inspection of substation breakers completed.",
        "Severe vibration detected on cooling fan motor.",
        "Preventive maintenance: tested relay settings.",
        "Burn marks observed on conductor, risk of fault high.",
        "Monthly cleaning of control room performed."
    ]
    labels = [1, 0, 1, 0, 1, 0]  # 1=Failure, 0=Routine
    return pd.DataFrame({"log": logs, "failure": labels})

def classify_logs(df):
    """
    Train a TF-IDF + Logistic Regression model to classify logs.
    """
    tfidf = TfidfVectorizer()
    X = tfidf.fit_transform(df["log"])
    y = df["failure"]

    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
    model = LogisticRegression()
    model.fit(X_train, y_train)
    preds = model.predict(X_test)

    print("Maintenance Log Classification Report:")
    print(classification_report(y_test, preds, target_names=["Routine", "Failure"]))

def extract_entities(text):
    """
    Extract equipment and issues from regulatory or maintenance text.
    """
    nlp = spacy.load("en_core_web_sm")
    doc = nlp(text)
    print("Named Entities:")
    for ent in doc.ents:
        print(f"{ent.text} ({ent.label_})")

    equipment_terms = re.findall(r"(transformer|breaker|relay|conductor|fan)", text, re.IGNORECASE)
    print("\nDetected Equipment Terms:", equipment_terms)

if __name__ == "__main__":
    # Classify logs
    df_logs = generate_maintenance_logs()
    classify_logs(df_logs)

    # Extract entities from regulatory document
    sample_text = """
    NERC CIP compliance audit found gaps in relay testing documentation.
    Transformer T-103 requires oil quality testing per IEEE C57 standards.
    """
    extract_entities(sample_text)