TimeSeries-Insight — AI Analytics Engine
Overview
PlantPulse TimeSeries-Insight is an AI anomaly detection, forecasting, and comprehensive analytics MCP server built specifically for manufacturing time series data.
Using the IBM Granite TSPulse (anomaly detection) and TinyTimeMixer TTM (forecasting) AI models, it delivers intelligent analysis of every time series data set in the plant — sensor data, OEE, RAM, EMS, alarms, work orders, and more.
With 33 analysis tools and a feature store, it covers everything from real-time analysis to pre-computed comprehensive insights.
insight-api(:8970) handles the tools and data, while model inference (TSPulse and TTM) is delegated to
insight-server(:8980). Since a GPU is only required on the inference side, the two services can run on
separate machines.
AI Anomaly Detection
Base model: IBM Granite TSPulse
| Target | Tool | Analysis |
|---|---|---|
| Equipment sensors | detect_asset_anomaly | Detects abnormal sensor patterns in temperature, pressure, vibration, and so on |
| System resources | detect_system_anomaly | Detects CPU, memory, and disk anomalies |
| OEE | detect_oee_anomaly | Abnormal patterns in availability, performance, and quality |
| RAM | detect_ram_anomaly | Abnormal MTBF and MTTR patterns |
| EMS | detect_ems_anomaly | Anomalies in energy consumption, power, and carbon emissions |
| Work orders | detect_order_anomaly | Production quality anomalies (OEE/RAM/EMS/combined) |
| Alarms | detect_alarm_anomaly | Abnormal alarm frequency patterns |
Key parameters:
threshold— Anomaly decision threshold (automatic when set to 0)top_k— Number of top anomalous metricslookback_minutes— Analysis time rangetarget_columns— Analysis target (queries all sensors automatically when unspecified)
AI Forecasting
Base model: TinyTimeMixer (TTM)
| Target | Tool | Forecast |
|---|---|---|
| Equipment sensors | forecast_asset | Future sensor value trends |
| System resources | forecast_system | CPU/memory/disk utilization |
| OEE | forecast_oee | Future trends in availability, performance, and quality |
| RAM | forecast_ram | Future trends in equipment reliability indicators |
| EMS | forecast_ems | Energy consumption and carbon emission outlook |
| Work orders | forecast_order | Future production quality trends |
| Alarms | forecast_alarm | Alarm frequency trends, failure prediction |
Key parameters:
prediction_length— Number of forecast stepsenable_bounds— Output confidence interval (upper/lower bounds)
In-Depth Alarm Analysis
| Tool | Description |
|---|---|
get_alarm_stats | Alarm statistics summary — frequency, priority distribution, top alarms, top tags |
analyze_alarm_duration | Alarm duration/MTTA analysis — degradation detection, mean acknowledgment time trend |
analyze_alarm_correlation | Alarm-sensor correlation — which sensor changes trigger alarms |
Equipment Health Index
get_asset_health — Combines sensors, OEE, RAM, EMS, and alarms to produce a health score from 0 to 100
Feature Store
Delivers fast comprehensive analysis using pre-computed features and LLM context.
| Tool | Description |
|---|---|
get_feature_status | Feature store status — scheduler jobs, execution times, backfill status |
get_asset_features | Equipment feature query — health score, anomaly score, alarms, status, production metrics |
get_asset_context | LLM context — daily comprehensive analysis summary text (pre-generated) |
analyze_asset | Comprehensive equipment analysis — health score, sensors, alarms, OEE/RAM/EMS, cause hints |
explain_asset_anomaly | Anomaly cause analysis — cause estimation based on anomalous tags, alarms, and status changes |
trigger_feature_pipeline | Manual execution of the feature pipeline |
Feature store architecture:
- Scheduler: Automatic hourly and daily feature computation
- Embeddings: Converts equipment features into vectors to support similarity search
- LLM context: Pre-generates daily comprehensive analysis text to maximize response speed
MCP Resources (6)
| Resource URI | Description |
|---|---|
plantpulse://system/info | System information |
plantpulse://domains | Domain list and API endpoints |
plantpulse://metrics/oee | OEE metric descriptions |
plantpulse://metrics/ram | RAM metric descriptions |
plantpulse://metrics/ems | EMS metric descriptions |
plantpulse://metrics/alarm | Alarm metrics and analysis capabilities |
Understanding Equipment and Tags
When a piece of equipment carries thousands of tags, no one can explain what each of them is. The tools below automatically characterize tags using real data statistics and platform metadata.
| Tool | Description |
|---|---|
list_asset_tags | Tag list for the equipment |
get_asset_profile | Equipment profile — characterizes which tag is what |
get_asset_tag_relations | Correlation between tags — groups tags that move together |
Case Search and Comparison
| Tool | Description |
|---|---|
find_similar_assets | Similar equipment search — based on cosine similarity of vector embeddings |
find_similar_anomalies | Similar anomaly case search — whether an anomaly of the same shape occurred before |
find_fleet_outlier | Fleet outliers — units of the same equipment type that behave differently from the rest |
search_pattern | Pattern search — finds intervals resembling a specified waveform |
check_failure_precursor | Failure precursor inspection — whether signals that preceded past failures are visible now |
Usage Scenarios
| Scenario | Tools Used |
|---|---|
| Early detection of equipment anomalies | detect_asset_anomaly → explain_asset_anomaly |
| Predictive maintenance planning | get_asset_health → forecast_asset → forecast_ram |
| Daily equipment briefing | get_asset_context → get_asset_features |
| Root cause analysis of alarm floods | get_alarm_stats → analyze_alarm_correlation → analyze_alarm_duration |
| Comparative analysis of similar equipment | analyze_asset → find_similar_assets |
| Has this anomaly happened before? | detect_asset_anomaly → find_similar_anomalies |
| Catching failures before they occur | check_failure_precursor → forecast_ram |
| Same equipment, but only one is abnormal | find_fleet_outlier → analyze_asset |
| Energy anomaly detection | detect_ems_anomaly → forecast_ems |
| Production quality forecasting | detect_order_anomaly → forecast_order |