Imagine a factory where thousands of sensors are “breathing” day and night: temperature probes sense the
heat of the equipment, vibration sensors capture subtle mechanical rhythms, and ammeters record the pulse
of energy. ...... These silent observers are generating massive amounts of data every minute and every second.
These silent observers are generating massive amounts of data every second. However, the data itself is not a
treasure, only when they are accurately “decoded”, can be transformed into the core fuel to drive the industry
towards intelligence. Data analytics in the field of industrial automation is the key driver of this silent revolution.
Data torrent: the basic raw material of industrial automation system
The “sensory nerve” of industrial automation system is everywhere:
Equipment layer: Sensors deployed on motors, pumps, valves, robotic joints and other equipment constantly output
operational status data.
Control layer: PLC (Programmable Logic Controller), DCS (Distributed Control System) real-time records of control logic
implementation, loop status, alarm information.
Production Execution Layer: MES (Manufacturing Execution System) collects the progress of work order execution, material
consumption, OEE (Overall Equipment Efficiency), and product quality inspection results.
Environment and Energy Consumption: Temperature and humidity sensors, electric meters, gas meters, etc. monitor the
production environment and resource consumption.
These heterogeneous data converge into a huge ocean of information, which constitutes the original deposit of
industrial data analysis.
Data analytics: the core engine that drives the intelligent upgrading
of automation systems
When data analytics technology is deeply integrated with automation, its value explodes in multiple dimensions:
Predictive maintenance: let the machine “know beforehand”.
Say goodbye to the traditional “fix it when it breaks” or rigid regular maintenance. By analyzing the vibration, temperature,
current and other time series data of equipment operation, combined with machine learning algorithms, early signs of
failure (such as bearing wear, rotor imbalance, poor lubrication) can be accurately identified.
By deploying a vibration analysis model, an automotive parts factory was able to issue an early warning weeks before
the complete failure of a motor bearing, avoiding a multi-million dollar accidental production line shutdown and
reducing maintenance costs by more than 30%.
Production process optimization: from “stable” to “lean”.
Analyze in real time the complex correlation between process parameters (temperature, pressure, flow, speed) and the
quality of the final product (dimensions, defect rate, performance indicators).
Statistical or machine learning models are built to dynamically find the optimal combination of process parameters,
significantly reducing scrap, rework and energy consumption. By optimizing a key reactor temperature control model
in real time, a chemical company improved the target product qualification rate by 5.2%, while reducing unit energy
consumption.
Intelligent Quality Control: Preventing Problems Before They Happen
Using data generated from online visual inspection, acoustic analysis, spectral analysis, etc., combined with AI models
(e.g., deep learning image recognition), it realizes millisecond-level online full inspection and automatic sorting of
product quality.
By analyzing data from the entire production process, we build quality prediction models to identify risk points and
automatically adjust upstream processes before defects actually occur, shifting quality control from “after-the-fact
inspection” to “before-the-fact prevention”.
Energy Efficiency Management: Tapping Invisible Profits
Integrate equipment operation status, environmental data, production planning, energy consumption and other data
to establish factory/line-level energy consumption models.
It identifies energy consumption anomalies, optimizes equipment start/stop strategies, balances load distribution, and
significantly reduces energy costs. By deploying an energy data analysis platform, a large manufacturing base saves
more than 10 million yuan in electricity costs annually.
Resource Scheduling and Supply Chain Collaboration: Global Optimization
Analyzing historical orders, equipment status, material inventory, personnel scheduling and other data, and applying
operations research optimization algorithms, the company realizes efficient collaboration among production planning,
material distribution, and personnel scheduling, shortening the delivery cycle and reducing work-in-process inventory.
Challenges and Future: The Road to Intelligence
Despite the promising future, industrial data analytics still faces challenges:
Data Silos: Difficulty in interconnecting data from different systems (OT/IT).
Data quality: Noise, missing, inconsistency and other issues affect the reliability of analysis results.
Talent Barrier: Scarcity of composite talents who are proficient in industrial automation, data analytics and domain
knowledge at the same time.
Difficulty in landing models: Stable deployment of lab models to harsh industrial field environments requires
engineering capabilities.
The future trend is clear:
The rise of edge intelligence: data analysis models sink to the device side and edge side, realizing millisecond real-time response.
AI deep empowerment: deep learning, reinforcement learning, etc. will deal with more complex images, sound, text and
other unstructured data, unlocking deeper insights.
Digital twin popularization: Data-based virtual factory models for more accurate simulation, prediction and optimization.
Low-code/no-code platform: Lowering the threshold of data analytics applications and empowering frontline engineers.
Industrial automation has long gone beyond simple mechanical replacement and program control. Data is becoming
the new “blood” that drives its evolution, and data analytics is the core organ that transforms blood into ‘wisdom’ and
“action”. Whoever can take the lead in mastering and harnessing this data-driven force, transforming massive information
into practical productivity improvement, cost optimization and quality leap, will be able to seize the commanding heights
in the wave of industrial intelligence. This is not only a technological upgrade, but also a key layout for future
competitiveness. The data alchemy of industry is quietly reshaping the value chain of manufacturing.