With the digitization of manufacturing, we are currently in the midst of the Industry 4.0, or the fourth revolution in manufacturing. While the preceding revolution brought computers and automation in its wake, Industry 4.0 is characterized by the enhancement of smart and automated systems, with the use of big data and machine learning technologies.
Industry 4.0 Applications
There are several unique cases where the adoption of Industry 4.0 has significantly improved businesses. Manufacturers can optimize their operations with the smart automation of connected devices; huge volumes of data can be processed within minutes and informed decisions on equipment maintenance and performance can be made.
The Role of Data Analytics and AI
One of the key driving forces of Industry 4.0 is Data & AI, which leverages the cloud, to store and analyze data, optimizing internal operations, like never before.
The adoption of AI in industrial processes has allowed for highly advanced pattern recognition and the ability of systems to learn from experience. Predictive analytics, which is the benchmark of Deep Learning and Machine Learning, now enables industries to optimize their processes, find the right solutions and keep innovating.
According to the following research findings by GE, here’s the significant impact Industry 4.0 will have in industries such as Oil & Gas, Power and Healthcare:
But the adoption of AI in I4.0 is not without its challenges. Any AI-driven industrial initiative should take into consideration the following factors:
Data Ingestion Challenges
- Mix of protocols – legacy and current
- Internet Connectivity Issues
- Challenge of ‘data noise’
- Audit & Traceability Challenges
Data Processing Challenges
- Difficulty in correlating sensor and enterprise data
- Tough to process at scale and near real-time
- Challenges to support variety of consumption formats
AI Model Design Challenges
- How to make the right choice of AI models
- Trade off between Model Accuracy and Latency
- Frequency of model update
- Choice of deployment – Edge vs Cloud
AI Governance Challenges
- How to monitor accuracy of models
- How to monitor failures
- How to implement DevOps for AI world
- How to govern catalog of models
Where are the AI Opportunities?
Predictive Analytics to monitor Health
- Remote monitoring
- Plant health
- Equipment health
- Machine health
- Rules & alert notifications
AI to improve Asset Performance
- Simulation
- Anomaly detection
- Predictive maintenance
AI for better and faster decision making
- Visual anomalies
- Video & image analytics
- NLP & Search
- Knowledge graph
A Case Study on Azure Platform
Failure Prediction and Early Warning Notification on Pick and Place Machines
Objective
Apply business logic and machine learning to Pick and Place machine data to:
- Predict if nozzles are getting out of control
- Initiate notifications to technical personnel
- Provide analytics on live nozzle performance
Benefits
- Reduced losses due to predictive nozzle performance insights and proactive notifications sent to operator.
- Increased productivity due to reduced downtime and smart scheduled maintenance
- Technicians have deeper understanding of nozzle performance and maintenance.
As Industry 4.0 continues to evolve, more industries will come to realize the full potential of its technologies. At Knowledge Lens, we are on a sustained mission to bridge industry gaps, using our technological expertise in Big Data & AI. Comment below to learn how we can enable you to become a Smart Enterprise, starting today.
If you want further information, or would wish to send us feedback, shoot us an email at sales@knowledgelens.com.
