neqsim-autonomous-investigation

name	neqsim-autonomous-investigation
version	1.1.0
description	Autonomous investigation loop for operational and engineering anomalies — turns an agent from 'told what to look for' into 'discovers relationships and hypotheses on its own'. USE WHEN: solving a PEPR action, root-cause, or operational study where the symptom, driver, or important relationships are NOT given up front. Runs an observe -> hypothesize -> predict -> test -> discriminate loop, using neqsim.process.diagnostics.RelationshipGraph for unsupervised lead-lag relationship discovery across historian tags, then hands the discovered relationships to neqsim-root-cause-analysis. Anchors on neqsim.process.diagnostics classes.
last_verified	2026-07-19
requires	{"java_packages":["neqsim.process.diagnostics","neqsim.process.automation"]}

NeqSim Autonomous Investigation Skill

Make agents investigate instead of follow a checklist. Use this skill when a task (PEPR action, root-cause, operational study, digital-twin deviation) does not tell you the symptom, the driver, or which relationships matter. The goal is that the agent discovers the important relationships from the data and the flowsheet on its own, forms competing hypotheses, and tests them — reaching findings that were not spelled out in the task.

When to Use

• A PEPR action or work order describes a problem but not a cause.
• Historian data is available but no one has said "look at tag X vs tag Y".
• A model-vs-plant deviation appears and the responsible variable is unknown.
• Any "why is this happening?" question where spoon-feeding relations is wrong.

Do not use this to replace a known, well-scoped calculation — if the symptom and mechanism are already given, go straight to neqsim-root-cause-analysis or the relevant discipline skill.

The Investigation Loop (mandatory ordering)

Run this loop before fixing a scope. Never assume the task's stated classification is correct — treat it as a hypothesis to challenge.

1. Observe (no assumptions). Pull all available tags, not just the ones the task names. Scan each tag against its own baseline and (when available) STID design envelope for what is abnormal. Discover cross-tag relationships with RelationshipGraph — including lead-lag direction, which distinguishes a driver from a follower.
2. Hypothesize (compete). Generate at least three competing causal hypotheses, always including a "not a real problem / instrument or data artifact" hypothesis. Seed them from the discovered leaders (candidate causes), not from intuition alone.
3. Predict (differentiate). For each hypothesis, state what it implies for other tags/streams. Two hypotheses that predict the same thing cannot be distinguished — find a prediction where they disagree (the discriminating test).
4. Test. Use NeqSim (runProcess/runFlowAssurance/simulation verification via RootCauseAnalyzer) plus historian evidence (EvidenceCollector) to check each prediction.
5. Discriminate & iterate. Keep the hypothesis that best explains the pattern across relationships, not a single number. Loop until one dominates or the data is exhausted; report residual ambiguity honestly.

Report the relationships you discovered, not just the answer. A finding without its supporting lead-lag relationships and discriminating test is not complete.

Unsupervised relationship discovery — RelationshipGraph

RelationshipGraph (in neqsim.process.diagnostics) scans every tag pair in a historian data set with no symptom and no hypothesis supplied, and reports which tags move together and, crucially, which moves first. Lead-lag directionality is the signal an agent uses to pick candidate causes on its own.

Java

import java.util.Map;
import neqsim.process.diagnostics.RelationshipGraph;

RelationshipGraph graph = new RelationshipGraph();
graph.setTimestamps(timestamps);      // optional: enables lag in seconds
graph.setMaxLagSamples(10);           // search +/- 10 samples
graph.setMinAbsCorrelation(0.5);      // only report |r| >= 0.5
List<RelationshipGraph.Relationship> edges = graph.analyze(historianData);
String relationshipReport = graph.toTextReport(edges);

for (RelationshipGraph.Relationship r : edges) {
  // r.getSource() leads r.getTarget() (candidate cause -> candidate effect)
  // r.getDirection(): LEADS or SYNCHRONOUS
  // r.getLagSamples() / r.getLagSeconds(): how far ahead the driver moves
  // r.getCorrelation(): strength & sign at the best lag
}

Python (task notebook / runner)

RelationshipGraph = ns.JClass("neqsim.process.diagnostics.RelationshipGraph")

graph = RelationshipGraph()
graph.setTimestamps(timestamps)     # java double[]; omit if unavailable
graph.setMaxLagSamples(10)
graph.setMinAbsCorrelation(0.5)
edges = graph.analyze(historian_map)  # Map<String, double[]>
for r in edges:
    print(r.getSource(), "->", r.getTarget(),
          "r=", round(r.getCorrelation(), 2),
          "lag_s=", r.getLagSeconds())

Reading the output

• A -> B (r=+0.85, leads by 300 s) — A moves first; A is a candidate cause of B. Prioritise hypotheses about A.
• A <-> B (r=+0.90, synchronous) — tightly coupled with no detectable lag; may share a common driver — look for a third tag that leads both.
• A strong statistical edge that does not follow a physical process path (upstream -> downstream in the flowsheet) is a candidate common-cause or instrument artifact, not a direct cause.

Auto-detect the symptom — AnomalyScanner

You should not have to be told the symptom either. AnomalyScanner (in neqsim.process.diagnostics) scans every tag against its own robust baseline (median / MAD) and, when supplied, its STID design envelope, and reports abnormal tags plus a candidate symptom inferred from the tag name. Detection kinds: THRESHOLD_HIGH/LOW (crosses a design limit), SPIKE_HIGH/LOW (robust-z outlier), TREND_UP/DOWN (sustained drift).

AnomalyScanner scanner = new AnomalyScanner();
scanner.setDesignLimit("Compressor-1.vibration", Double.NaN, 7.1); // optional
List<AnomalyScanner.Anomaly> anomalies = scanner.scan(historianData);
Symptom candidate = scanner.suggestSymptom(anomalies); // e.g. HIGH_VIBRATION

Promote statistics to causes — CausalTopologyModel

A statistical lead-lag edge is not proof of causation. CausalTopologyModel overlays the flowsheet connectivity (which equipment feeds which) on the RelationshipGraph edges and classifies each: CAUSAL_CANDIDATE (leader is upstream of follower and moves first), LOCAL (same equipment), COUNTER_FLOW (lead-lag opposes process flow — feedback), or COMMON_CAUSE_OR_ARTIFACT (no process path — a shared hidden driver or an instrument artifact).

Map<String, Set<String>> adjacency = CausalTopologyModel.buildDownstreamAdjacency(processSystem);
CausalTopologyModel model = new CausalTopologyModel(adjacency, tagToEquipment);
List<CausalTopologyModel.CausalEdge> edges = model.classify(relationships);

One call — RootCauseAnalyzer.analyzeAutonomous()

The three steps above plus the Bayesian scoring are chained in a single entry point. No symptom is required — the analyzer scans anomalies, infers the symptom, discovers relationships, and classifies them against topology, then converts hypothesis-matched anomaly and physically consistent topology findings into weighted evidence used in ranking.

RootCauseAnalyzer rca = new RootCauseAnalyzer(processSystem, "Compressor-1");
rca.setHistorianData(historianData, timestamps);
rca.setDesignLimit("Compressor-1.vibration", Double.NaN, 7.1);

// Autonomous: no setSymptom() call needed.
RootCauseReport report = rca.analyzeAutonomous();                 // anomalies + relationships + RCA
// or, to also get causal-vs-artifact classification:
RootCauseReport report2 = rca.analyzeAutonomous(tagToEquipment);  // + CausalTopologyModel

rca.getLastAnomalies();     // what looked abnormal
rca.getLastRelationships(); // who leads whom
rca.getLastCausalEdges();   // causal candidate vs common-cause/artifact

Only LOCAL and CAUSAL_CANDIDATE edges matching a hypothesis fingerprint affect ranking. COUNTER_FLOW, COMMON_CAUSE_OR_ARTIFACT, and UNKNOWN findings remain reportable but are not treated as causal support. This conservative admission rule prevents correlation alone from inflating confidence.

Python: get the classes with ns.JClass("neqsim.process.diagnostics.AnomalyScanner"), ...RelationshipGraph, ...CausalTopologyModel, ...RootCauseAnalyzer.

Cooperation (chain both directions)

plant-data / enterprise-plant-data   (historian tags)
alarm-events / maintenance-api / STID (events, work orders, design limits)
        v
neqsim-autonomous-investigation      (this skill: discover relations + hypotheses)
        v
neqsim-root-cause-analysis           (Bayesian scoring + simulation verification)
        v
neqsim-process-safety / discipline   (consequence, if the cause is a hazard)

• Gather ALL data first (do not spoon-feed one tag): pull the full historian tag map via neqsim-plant-data (community) or enterprise-plant-data (historian/Seeq); add alarm & event history (enterprise-alarm-events), maintenance work orders / notifications (enterprise-maintenance-api), and STID design limits when available. Feed the whole tag map (plus design limits) into AnomalyScanner / RelationshipGraph — the more context, the better the discovery.
• Downstream: hand the discovered leaders and their lags to neqsim-root-cause-analysis as the candidate causes / expected signals, so the Bayesian scorer verifies them with a NeqSim simulation instead of relying on a fixed symptom.
• The one-call path RootCauseAnalyzer.analyzeAutonomous(tagToEquipment) runs the whole chain (anomaly scan -> symptom inference -> relationship discovery -> topology classification -> Bayesian scoring) so the agent only supplies data + flowsheet.

Limitations

• Correlation and lead-lag are screening signals, not proof of causation. Always confirm with the flowsheet topology and a NeqSim simulation.
• Default correlation is linear (Pearson); enable rank/Spearman mode (RelationshipGraph.setUseRankCorrelation(true)) to catch strong monotonic non-linear couplings. Strongly non-monotonic couplings may still be under-reported — consider transforming variables (log, rate-of-change) first.
• Lag resolution is limited by the sampling interval; sub-sample lags round to the nearest sample.
• Common-cause structure (one hidden driver behind many tags) is flagged only indirectly — look for a tag that leads several others, or a COMMON_CAUSE_OR_ARTIFACT verdict from CausalTopologyModel.