Plant Memory: Technical Language Processing and a Verified Maintenance Ontology for Agent-Accessible Failure Knowledge from SAP PM Work Orders

Akash Raj

cs.AIOct 13, 2026

Plant Memory: Technical Language Processing and a Verified Maintenance Ontology for Agent-Accessible Failure Knowledge from SAP PM Work Orders

Authors: Akash Raj

Organizations: M.Tech, School of Artificial Intelligence and Data Science, Indian Institute of Technology Jodhpur

Abstract

Maintenance work orders are the largest written record of how industrial equipment fails, yet in most plants they are close to unusable for analysis. Short texts are abbreviated and misspelt, asset identity is buried in free text, work-type labels contradict the work described, and the fields that matter most for reliability (symptom, root cause, action taken) are usually empty. As a result, diagnostic knowledge stays with senior technicians and leaves when they do. We present Plant Memory, a system that applies technical language processing (TLP) to SAP Plant Maintenance (PM) exports and builds an ISO 14224-aligned maintenance ontology in which every extracted statement is a reified fact carrying a confidence score, the evidence it was read from, and a verification state. Extraction is rule-first, with an optional large language model (LLM) acting as a second reader whose disagreements raise a fact's review priority rather than overwrite it. Technicians verify facts through a review queue, answer targeted knowledge-gap questions where failures repeat without a recorded cause, and approve or reject ontology drift proposals when new terms appear. The resulting graph is served to people through a web application and to AI agents through a Model Context Protocol (MCP) server, so both query the same verified knowledge and every answer cites the work orders behind it. On a synthetic 12-month export of 400 work orders from a parboiled-rice plant, rule-based extraction reaches 96.2% component accuracy, 88.9% failure-mode accuracy and 100% root-cause precision at 62% coverage; on 28 diagnostic questions, ranked root causes from the ontology are correct at top-1 in 82.1% of cases against 53.6% for the raw SAP root-cause column.

Figures & tables

Panel of Figure 1 — Figure 1: Plant Memory architecture. All three interfaces read and write the same graph through one core library, so a fact verified in the browser is reported as verified the next time an agent asks.

Class	Role and examples
ProcessArea	Top of the asset hierarchy: parboiling, paddy-to-rice milling, utilities, packing, warehouse
Asset	Maintainable item with a functional location, class and shop-floor aliases (e.g. Dryer elevator , alias “LSU elev”)
Component	Part of an asset: bearing, V-belt, sprocket, plummer block, rubber roll, mechanical seal
SparePart	SAP material that realises a component type, with material code and unit cost
Symptom	Observation: abnormal noise, vibration, belt slip, tripping, reduced output
FailureMode	ISO 14224-aligned, with mechanism code: bearing wear (2.4 Wear), belt slippage (1.5 Looseness), seal leakage (1.1 Leakage)

Table 1: Core ontology classes. The vocabulary contains 26 components, 20 failure modes, 13 root causes, 10 actions and 9 symptoms, each with synonyms used for matching.

Tool	Returns
list_assets	Assets by process area, with order and failure counts
get_asset_history	Orders for an asset with extracted facts, statuses, cost and downtime
suggest_root_causes	Ranked causes with scores, confidence bands, evidence orders and technician statements
find_similar_failures	BM25 matches for a free-text description, with causes, actions and parts
rank_bad_actors	Pareto by cost, downtime or frequency
detect_recurrence	Repeat failures within a window

Table 2: MCP tools. Read tools are open to any connected client; the single write tool cannot change verified knowledge.

Figure 2: Overview screen on the demo dataset: summary figures and a Pareto of breakdown downtime by asset, with the cumulative-share line and the 80% threshold. The planted bad actor (the dryer elevator) ranks first.

Figure 3: Graph screen (patterns view): assets, failure modes and root causes, with edge width proportional to the number of work orders. Solid green edges are verified and dashed yellow edges await review. Selecting a node (here, rubber roll wear ) highlights its neighbourhood and lists the work orders behind it.

Figure 4: A general-purpose assistant answering a planner’s question through the Plant Memory MCP server. The answer is assembled from tool results over the verified graph and notes the limits of the available history.

Layer	Technology
Core and pipeline	Python 3.10+, pandas, openpyxl, rule-based TLP, BM25
LLM extraction	Any OpenAI-compatible API via httpx; evaluated with openai/gpt-oss-120b on Groq; local models via Ollama
Knowledge graph	Neo4j 5 (property graph, Cypher) or SQLite; RDF/Turtle export with rdflib; OWL 2 schema
Agent interface	Model Context Protocol server (Python MCP SDK, stdio transport)
Web API	FastAPI, Uvicorn, background build jobs
Web application	React 18, TypeScript, Vite, Cytoscape.js

Table 3: Technology stack.

Field	Events	Coverage	Precision	Accuracy	Macro F1
Asset	400	1.000	1.000	1.000	1.000
Component	391	0.987	0.974	0.962	0.953
Failure mode	350	0.991	0.896	0.889	0.880
Root cause	350	0.620	1.000	0.620	0.805
Action	400	0.948	0.982	0.930	0.899
Symptom	357	0.608	0.982	0.597	0.767

Table 4: Rule-based extraction on the synthetic export (400 work orders, seed 7).

Answerer	Top-1	Top-3	Answers citing evidence
Raw SAP root-cause column	0.536	n/a	n/a
Plant Memory	0.821	0.964	1.000

Table 5: Diagnostic accuracy on 28 questions.

Field	Rules	LLM alone	Rules + LLM (adopted policy)
Component	96.2	87.2	96.2
Failure mode	88.9	55.4	88.9
Root cause	62.3	46.9	n/r
Action	93.0	92.5	95.0
Symptom	59.7	53.5	61.1
Diagnosis top-1	82.1	n/a	82.1

Table 6: Accuracy (%) of rules, the LLM as an independent extractor, and the merged output. n/r: not reported separately; the LLM filled one root cause correctly where the rules were silent.

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