Changelog

Available right after installation: OmniMES exposes a standard MCP endpoint on the factory server. In any MCP-capable AI assistant (Claude Desktop, ChatGPT, Cursor, Copilot) you install the „OmniMES” plugin, paste an access token to your server, and you're done. From any computer on the company network, in natural language, you build complete dashboards and reports, configure the plant structure, machine statuses, alarms and schedules, and read the live production state as well as OmniEnergy measurement sources and points — without knowing the UI and without writing SQL. A real-world example: a single instruction „build a dashboard with availability, failure rate and energy cost per unit for the machine park” has the assistant create a dozen SQL queries, visualizations and a finished Redash dashboard. Choose the model deliberately: commercial (Claude, GPT, Gemini) — when quality matters; free via OpenRouter (Llama, Qwen, DeepSeek) — when cost matters; local (Ollama, LM Studio) — when data must stay inside the factory network.

Counters (indicators) now line up next to each other and wrap automatically depending on the width of the machine card. With the “1 machine per row” setting (wide card) many fit in a single row; with more machines per row they wrap on their own.

Eliminated the growing memory and CPU usage when switching server / line / sorting (previous broker connections were not being closed). The monitoring screen can now stay open for extended periods without hanging the browser, even with a large number of machines.

Resilience against a wrong clock on the gateway or PLC. Timestamps “from the future” (e.g. +1h after a DST change or a wrong timezone) are rejected in favor of the server time, so they cannot corrupt the archive or the displayed machine state.

Instead of relying solely on digital signals from the controller, the machine's working state (running / idle / changeover / stopped) can now be derived from a threshold on any analog measurement — motor current, hydraulic pressure, spindle RPM. Example: motor current > 12 A → machine running; 3–12 A → idle; < 3 A → stopped. Thresholds are defined in the status configurator; classification runs live in Monitoring and is persisted in history, so OEE, MTBF and production reports reflect the real machine state without wiring additional binary signals from the PLC.

Replacing MongoDB — faster queries and noticeably lower storage footprint for telemetry, while keeping the existing functionality intact. Example: one year of telemetry from ~150 machines took roughly 80 GB in MongoDB, and roughly 7 GB in PostgreSQL + TimescaleDB (with hypertable compression) — about 10× less storage for the same source data, plus faster time-window queries.

The OmniMES stream can now ingest measurements directly from MQTT topics — not only from Sparkplug B frames. This simplifies integration with controllers, IIoT gateways and devices that publish in native formats (JSON or plain payload), without requiring a Sparkplug layer on the source side.

Correct status assignment by MQTT topic and tag; fixed incorrect matches and retrieval of the latest events.

Toast-style notifications and per-view state persistence stored locally in the browser.