TDengine Blog
View the latest articles about time-series databases and industrial data processing
Asset-Centric and Event-Centric Visualization: From Dashboards to Operational Understanding
What industrial users need is a new kind of visualization—one that is asset-centric, event-aware, insight-driven, and tightly integrated with the data foundation.
Jeff Tao
April 2, 2026 | AI-Native Industrial Data Foundation, Data Historian
Advanced Analytics in Industrial Systems: Beyond the Historian
Organizations increasingly expect systems to generate insights—detect anomalies, predict future behavior, identify patterns, explain deviations and analyze the root cause.
Jeff Tao
April 2, 2026 | AI-Native Industrial Data Foundation, Data Historian
Event-Centric + Asset-Centric: The Missing Link in Industrial Data
Assets define what exists. Events define what happens. Only when both are modeled together can we truly understand industrial operations—and only then can AI become genuinely useful.
Jeff Tao
March 30, 2026 | AI-Native Industrial Data Foundation, Data Historian
How AI Helps Engineers Move from OEE Monitoring to Root-Cause Analysis
A low OEE number by itself is not very useful. What matters is whether the loss is coming from uptime, speed, or quality, and whether the team can isolate the cause quickly enough to act.
Jim Fan
March 29, 2026 | Data Historian, Industrial Data
Powering a Next-Generation Digital Redrying Facility with TDengine
By comprehensively addressing performance bottlenecks in data ingestion, storage, and computation for massive time-series workloads, TDengine has made the redrying process more digitalized, transparent, and intelligent.
TDengine Team
March 27, 2026 | Case Studies, Manufacturing
Building a Foundation for AI-Driven Manufacturing at Kunming Cigarette Factory
This project has validated TDengine’s suitability for handling massive time-series data in the tobacco industry, providing a reusable technical approach for digital transformation across the sector.
TDengine Team
March 27, 2026 | Case Studies, Manufacturing
Why Time-Series Data Alone Is Not Enough: Rethinking Industrial Event Analysis in the Age of AI
To fully realize the value of industrial data, events need to become a native part of the data foundation, not an optional layer.
Jeff Tao
March 26, 2026 | AI-Native Industrial Data Foundation, Data Historian
Asset-Centric Modeling: The Foundation of Industrial Data Context
To fully understand industrial operations, data models must move beyond structure and begin to represent behavior.
Jeff Tao
March 24, 2026 | AI-Native Industrial Data Foundation, Data Historian
From Wonderware to TDengine: Modernizing Data Infrastructure at Dali Cigarette Factory
Leveraging TDengine, Dali Cigarette Factory has implemented comprehensive data collection, storage, and analysis for cigarette rolling and packaging equipment, covering more than 40,000 monitoring points.
TDengine Team
March 20, 2026 | Case Studies, Manufacturing
Seeing Throughput, Blend Quality, and Margin Together in Refinery Operations
Refinery performance does not live in one screen. Throughput, blend quality, and site economics move together, and operations teams need a way to see them together if they want to respond earlier and operate more consistently.
Jim Fan
March 19, 2026 | Data Historian
