Industrial Diagnostics

基于 ot-aiops MCP 服务器的跨协议只读 OT 诊断 Skill，提供 diagnose_dataflow（区分连不上、地址错、质量坏、值过期、平板）、alarm_bad_actors（ISA-18.2 报警泛滥、帕累托坏行为源、抖动与常驻报警）、tag_health（坏质量/平板/越限/统计异常标签排名）和 historian_health（采样序列平板与缺口检测），输出结构化 JS…

zw008

热度 16.02

最近更新 2026-06-28 14:49:12

分类：数据依赖：MCP 服务应用场景：数据处理 / 报告撰写

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add industrial-diagnostics

Metadata

name: industrial-diagnostics
description: Cross-protocol, READ-ONLY OT troubleshooting via the ot-aiops MCP server. diagnose_dataflow localizes a "no data" break (cannot connect vs stale value vs flatline sensor) across any endpoint's reachable hops; alarm_bad_actors runs ISA-18.2 alarm-flood analysis (rate vs thresholds, Pareto offenders, chattering, standing); tag_health ranks bad-quality/flatline/out-of-range/anomaly offenders; historian_health finds flatline/gaps over a sample series. Use when the task is "why is there no data / why did the dashboard go blank", alarm flooding, bad actors, stuck/flatline tags, or general OT data-quality triage. Not for IT/network/Kubernetes/hypervisor/backup troubleshooting.

industrial-diagnostics

The cross-protocol intelligence layer of ot-aiops (read-only). Structured JSON outputs are designed for an agent to visualize multi-dimensionally. Preview — diagnoses the layers reachable from this host and accepts injected series for the historian/SCADA tiers it cannot reach.

When to use

"No data" / blank dashboard triage; alarm floods; bad-actor tags; stuck sensors.
After a protocol *-tap skill connects, to reason about why values are wrong.

When NOT to use

Single-tag reads → use the matching protocol *-tap skill directly (incl. ethernetip-tap for Rockwell/Allen-Bradley Logix).
OEE / downtime / asset inventory / change-of-value → industrial-analytics.
IT/network/Kubernetes/hypervisor/backup → not this tool.

Tools

`diagnose_dataflow(endpoint?, ref?, freshness_threshold_s=60, series?, flatline_eps?)`

Probes connect → read(ref) → freshness → variance. Returns {verdict, diagnosis, recommended_action, hops:[{hop, ok, detail}]} where verdict ∈ cannot_connect (network/PLC down) · comms_ok_value_unreadable (wrong address) · comms_ok_bad_quality (sensor/source fault) · comms_ok_value_stale (upstream stopped updating) · comms_ok_flatline (stuck source) · healthy. Works over any protocol (ref = OPC-UA node id / Modbus address / S7 address / MELSEC device). Pass series (scalars or {value,timestamp}) for freshness/variance when a historian is out of reach.

`alarm_bad_actors(events, window_minutes?, chatter_window_s=60, standing_s=86400, top_n=10)`

ISA-18.2 over events:[{source, timestamp, priority?, state?}]. Returns {alarms_per_hour, isa_18_2:{ok_max:6,manageable_max:12,flood_min:30}, flood_verdict, priority_distribution, pareto_sources_for_80pct, top_offenders:[{source,count,share_pct,chattering,standing}], chattering[...], standing[...]}.

`tag_health(tags, thresholds?)`

tags:[{ref, samples:[scalar|{value,good|quality}], warn_high?, alarm_high?, ...}]. Returns {overall, offenders:[{ref, latest, flags, anomaly_count, severity 0..3}]}. Flags: bad_quality, flatline, out_of_range_warn/alarm, statistical_anomaly (z-score >3σ and IQR fence).

`historian_health(series, gap_threshold_s=60, flatline_eps?)`

series:[scalar|{value,timestamp,quality|good}] → {bad_quality_count, flatline, gap_count, gaps[...], verdict ('ok'|'degraded'|'gappy'|'flatline'|'bad_tag')}.

Example

diagnose_dataflow(endpoint="line1", ref="ns=2;i=5", freshness_threshold_s=30) → {"verdict":"comms_ok_value_stale","diagnosis":"... value is STALE ...", "recommended_action":"Trace upstream ...","hops":[...]}

Safety

Strictly read-only — never writes. 缺功能提 issue/PR 欢迎留言.