The application of artificial intelligence in the industrial field did not come out of nowhere, but is
a long journey accompanied by technological breakthroughs and industrial needs. Its development
reflects the manufacturing industry's continuous search for more efficient, smarter and more reliable
production methods. Looking back at this journey, we can clearly see how AI from the laboratory concept,
gradually penetrate into the core of modern automation system intelligence engine.
Phase 1: Exploration and germination (1980s - late 1990s)
The seeds of AI were first planted in industry with a strong academic exploration.
Attempts at Expert Systems: This was the most notable form of early AI application in industry. Engineers
attempted to encode the knowledge and rules of thumb of domain experts into computer programs to build
“knowledge base” systems for troubleshooting (e.g., equipment anomaly analysis), process optimization (e.g.,
recipe recommendation), and quality control (e.g., defect pattern recognition). They rely on a large number of
manually entered rules and show value in specific, well-defined scenarios. However, their limitations are obvious:
difficult access to knowledge (“knowledge bottlenecks”), complex maintenance of rules, and a lack of learning
ability to adapt to changing environments and complex problems.
Rule-based control extension: A more sophisticated fuzzy logic control is explored on the basis of traditional
PLC/DCS logic control. It shows stronger robustness than traditional PID control when dealing with processes
characterized by nonlinearity and uncertainty (e.g., precise regulation of temperature and liquid level), providing
new ideas for dealing with “fuzzy” problems in industry.
Challenges in the embryonic stage: At this stage, the serious shortage of arithmetic power, the difficulty of obtaining
industrial data (few sensors, weak network), the high cost of storage, as well as the immaturity of the AI technology
itself (especially machine learning), have greatly limited the large-scale application of AI in industrial automation. AI
is more like the “icing on the cake” on the edge of the automation system, rather than the core driving force.
Phase 2: Data accumulation and machine learning penetration
(2000s - early 2010s)
As the level of informatization increased and the cost of hardware decreased, the value of industrial data began to
be recognized, laying the foundation for the further development of AI.
Initial data-driven awakening: The popularization of SCADA, MES, and other systems allowed factories to accumulate
large amounts of operating parameters, equipment status, and production process data. Although the data quality
varies and is mostly structured, it provides raw material for subsequent analysis.
Statistical methods and early machine learning on the scene: Traditional statistical analysis (e.g., SPC) is more widely
used for quality control and process monitoring. At the same time, more practical machine learning algorithms begin
to be used on a smaller scale:
Fault Detection and Classification: Monitoring of abnormal deviations in equipment or processes based on statistical
models (e.g., PCA Principal Component Analysis) or simple classification algorithms.
Predictive maintenance in its infancy: Simple regression or time series models using historical data to predict the
remaining life of critical components (e.g. bearings), albeit with limited accuracy.
Initial vision applications: Machine vision systems based on traditional image processing algorithms (feature extraction +
classifiers) have made progress in localization, character recognition (OCR), and simple defect detection, but are less
capable of handling complex and changing scenarios.
The rise of “soft measurement”: using process variables and easy-to-measure parameters, through regression, neural
networks and other models, to estimate key process parameters that are difficult or expensive to measure (such as component
concentration, viscosity), has become an important application of AI in the process industry.
Barriers remain: AI applications at this stage still face huge challenges: arithmetic bottlenecks (especially at the edge), data
silos (difficult to connect data between systems), lack of efficient data processing and analysis tools, difficulties in deploying
complex models, and the industry's awareness and trust in AI still needs to be improved. Application points are relatively
isolated, and deep integration is limited.
Phase 3: Deep Learning Leads to Visual Revolution and Cognitive Leap
(Mid-2010s - Early 2020s)
Breakthroughs in deep learning, especially the phenomenal success of convolutional neural networks in image
recognition, revolutionize the landscape of AI applications in industrial automation, especially in machine vision.
Qualitative change in machine vision: deep learning has brought about a disruptive change:
Complex Defect Detection: Ability to recognize subtle, diverse, and complex background defects (e.g., fabric
imperfections, scratches on precision parts, surface texture anomalies) that are difficult for traditional algorithms to handle.
Classification and Recognition: Dramatically improves the accuracy and robustness of classification and recognition
of objects with changing shapes and different postures.
Improved guidance and localization accuracy: Highly accurate robot gripping guidance and part localization in
complex scenes.
Deepening of predictive maintenance: Deep learning models (e.g., LSTM long and short-term memory networks) are
better able to process time-series data from equipment sensors (vibration, acoustic, and electric current) to capture
complex failure modes and degradation characteristics, significantly improving the accuracy and lead time of failure
prediction.
Intelligence in process optimization: AI models are beginning to be used to analyze massive process
The AI models are being used to analyze the relationship between massive process parameters and final product
quality and energy consumption, to find the optimal operation point, and even to realize the automatic closed-loop
fine-tuning of parameters.
Initial application of natural language processing: voice interaction is used for equipment operation guidance and
fault reporting, and text analysis is used to process equipment logs and maintenance reports to assist diagnosis.
Mature foundation conditions: The outbreak of this stage is attributed to the exponential growth of hardware
arithmetic power such as GPUs, the popularization of industrial networks (industrial Ethernet, wireless) to reduce
the difficulty of data access, the cloud computing/edge computing architecture to provide flexible arithmetic support,
and the wide application of open source deep learning frameworks (e.g., TensorFlow, PyTorch) to reduce the
technological threshold.
Phase 4: Integration and Symbiosis and Full Process Empowerment (Current
and Future Trends)
AI is no longer limited to a single application point, and is accelerating its deep integration with industrial automation systems,
evolving to system-level, full-process intelligence.
System-level intelligent decision-making: AI engines are embedded in MES, APS and other systems for smarter and more dynamic
production scheduling (taking into account real-time equipment status, order priority, material supply), optimal allocation of
resources, and optimization of energy management.
Digital Twin + AI: The digital twin becomes a sandbox for AI training and validation, where AI models are predicted, optimized, and
“what-if-analyzed” in a virtual environment, and their outputs are used to optimize operating parameters and control strategies in
the physical world, forming a closed loop.
The Emergence of Autonomous Systems: AGVs/AMRs are moving towards a higher degree of autonomous navigation and
decision-making in limited scenarios (e.g., warehousing and logistics) by combining multi-sensor fusion (vision, LIDAR), SLAM mapping
and localization, and AI path-planning algorithms.
Rise of Edge Intelligence: To meet the real-time, low-latency, and privacy and security needs of industrial control, AI models are being
deployed to edge devices (e.g., smart sensors, edge gateways, industrial PCs) close to the data source to enable localized and fast
reasoning and decision-making.
Generative AI exploration: Large-scale language models are being explored for industrial applications, such as assisting in writing PLC
code, generating equipment maintenance reports, providing operator training support, and processing unstructured document knowledge.
Focus shift: from technology validation to engineering: model interpretability, robustness, continuous learning capability (to adapt to
changes in production lines), safety and reliability assurance (integration of functional safety and information security), and ease of
use (low-code/no-code tools) have become key challenges and R&D priorities. The importance of data governance (quality,
management, circulation) has never been more prominent.
Conclusion: From tool to core, the future is here
The development history of artificial intelligence in industrial automation is an evolutionary history from edge assistance to core
drive, and from single-point breakthrough to system integration. It has experienced the exploration of the early rule system, data
accumulation and statistical methods, and has achieved revolutionary breakthroughs in key areas catalyzed by deep learning, and
is now moving forward in the direction of in-depth integration, empowering the whole process, and building an autonomous
intelligent system.
AI is no longer a dispensable embellishment in industrial automation, but a core intelligence engine to improve production efficiency,
guarantee product quality, optimize energy consumption, realize flexible manufacturing, and build a predictive operation and
maintenance system. Understanding its development pulse helps us to grasp the current opportunities, actively respond to challenges,
and more effectively transform AI technology into a real competitiveness of the manufacturing industry, together shaping a smarter,
more efficient and more sustainable industrial future. This road is still extending and full of infinite possibilities.