Source URL: https://cloud.google.com/blog/topics/manufacturing/tata-steel-enhances-equipment-and-operations-monitoring-with-google-cloud/
Source: Cloud Blog
Title: Tata Steel enhances equipment and operations monitoring with the Manufacturing Data Engine
Feedly Summary: Tata Steel is one of the world’s largest steel producers, with an annual crude steel capacity exceeding 35 millions tons. With such a large and global output, we needed a way to improve asset availability, product quality, operational safety, and environmental monitoring. By centralizing data from diverse sources and implementing advanced analytics with Google Cloud, we’re driving a more proactive and comprehensive approach to worker safety and environmental stewardship.
To achieve these objectives, we designed and implemented a robust multi-cloud architecture. This setup unifies manufacturing data across various platforms, establishing the Tata Steel Data Lake on Google Cloud as the centralized repository for seamless data aggregation and analytics.
High level IIOT data integration architecture
Building a unified data foundation on Google Cloud
Our comprehensive data acquisition framework spans multiple plant locations, including Jamshedpur, in the eastern Indian state of Jharkhand, where we leverage Litmus and ClearBlade — both available on Google Cloud Marketplace — to collect real-time telemetry data from programmable logic controllers (PLCs) via LAN, SIM cards, and process networks.
As alternatives, we employ an internal data staging setup using SAP Business Objective Data Services (BODS) and Web APIs. We have also developed in-house smart sensors that use LoRaWAN and Web APIs to upstage data. These diverse approaches ensure seamless integration of both Operational Technology (OT) data from PLCs and Information Technology (IT) data from SAP into Google Cloud BigQuery, enabling unified and efficient data consumption.
Initially, Google Cloud IoT Core was used for ingesting crane data. Following its deprecation, we redesigned the data pipeline to integrate ClearBlade IoT Services, ensuring seamless and secure data ingestion into Google Cloud.
Our OT Data Lake is architected on Manufacturing Data Engine (MDE) and BigQuery, which provides decoupled storage and compute capabilities for scalable, cost-efficient data processing. We developed a visualization layer with hourly and daily table partitioning to support both real-time insights and long-term trend analysis, strategically archiving older datasets in Google Cloud Storage for cost optimization.
We also implemented a secure, multi-path data ingestion architecture to upstage OT data with minimal latency, utilizing Litmus and ClearBlade IoT Core. Finally, we developed custom solutions to extract OPC Data Access and OPC Unified Access data from remote OPC servers, staging it through on-premise databases before secure transfer to Google Cloud.
Together, this comprehensive architecture provides immediate access to real-time device data while facilitating batch processing of information from SAP and other on-premise databases. This integrated approach to OT and IT data delivers a holistic view of operations, enabling more informed decision-making for critical initiatives like Asset Health Monitoring, Environment Canvas, and the Central Quality Management System, across all Tata Steel locations.
Crane health monitoring with IoT data
Monitoring health parameters of crane sub devices
Overcoming legacy challenges for real-time operations
Before deploying Industrial IoT with Google Cloud, high-velocity data was not readily accessible in our central storage. Instead, the data resided in local systems, such as mediation servers and IBA, where limited storage capacity led to automatic purging after a defined retention period. This approach, combined with legacy infrastructure, significantly constrained data availability and hindered informed business decision-making. Furthermore, edge analytics and visualization capabilities were limited, and data latency remained high due to processing bottlenecks at the mediation layer.
Addressing these issues, particularly implementing a secure OT data pipeline within a DMZ environment, posed significant challenges. To mitigate cybersecurity risks and maintain data integrity, we designed multiple architectural data paths that incorporate one-way data transfer mechanisms (data diodes) to ensure the secure and controlled upstaging of OT data to the cloud.
Our Google Cloud implementation has since enabled the seamless acquisition of high-volume and high-velocity data for analyzing manufacturing assets and processes, all while ensuring compliance with security protocols across both the IT and OT layers. This initiative has enhanced operational efficiency and delivered cost savings.
Our collaboration with Google Cloud to evaluate and implement secure, more resilient manufacturing operations solutions marks a key milestone in Tata Steel’s digital transformation journey. The new unified data foundation empowered data-driven decision-making through AI-enabled capabilities, including:
Asset health monitoring
Event-based alerting mechanisms
Real-time data monitoring
Advanced data analytics for enhanced user experience
The iMEC: Powering predictive maintenance and efficiency
Tata Steel’s Integrated Maintenance Excellence Centre (iMEC) utilizes MDE to build and deploy monitoring solutions. This involves leveraging data analytics, predictive maintenance strategies, and real-time monitoring to enhance equipment reliability and enable proactive asset management.
MDE, which provides a zero code pre-configured set of Google Cloud infrastructure, acts as a central hub for ingesting, processing, and analyzing data from various sensors and systems across the steel plant, enabling the development and implementation of solutions for improved operational efficiency and reduced downtime.
With monitoring solutions helping to deliver real-time advice, maintenance teams can reduce the physical human footprint at hazardous shop floor locations while providing more ergonomic and comfortable working environments to employees compared to near-location control rooms. These solutions also help us centralize asset management and maintenance expertise, employing real-time data to enable significant operational improvements and cost-effectiveness goals, including:
Reducing unplanned outages and increasing equipment availability.
Transitioning from Time-Based Maintenance (TBM) to predictive maintenance.
Optimizing resource use, reducing power costs, and minimizing delays.
Driving safety with video analytics and cloud storage
To strengthen worker safety, we have also deployed a safety violation monitoring system powered by on-premise, in-house video analytics. Detected violation images are automatically uploaded to a Cloud Storage bucket for further analysis and reporting.
We developed and trained a video analytics model in-house, using specific samples of violations and non-violations tailored to each use case. This innovative approach has enabled us to efficiently store a growing catalog of safety violation images on Cloud Storage, harnessing its elastic storage capabilities.
Our Central Quality Management System — which ensures our data is complete, accurate, consistent, and reliable — is also built on Google Cloud, utilizing BigQuery for scalable data storage and analysis, and Looker Studio for intuitive data visualization and reporting.
Google Cloud for environmental monitoring
Tata Steel’s commitment to sustainability is evident in our comprehensive environment monitoring system, which operates entirely on the Google Cloud. Our Environment Canvas system captures a wide array of environmental Key Performance Indicators (KPIs), including stack emission and fugitive emission.
Environment Canvas – Data office & visualization architecture
Environmental parameters
We capture the data for these KPIs through sensors, SAP, and manual entries. While some sensor data from certain plants is initially sent to a different cloud or on-premises systems, we eventually transfer it to Google Cloud for unified consumption and visualization.
By leveraging the power of Google Cloud’s data and AI technologies, we are advancing operational monitoring and safety through a unified data foundation, real-time monitoring, and predictive maintenance — all enabled by iMEC. At the same time, we are reinforcing our commitment to environmental responsibility with a Google Cloud-based system that enables comprehensive monitoring and real-time reporting of environmental KPIs, delivering actionable insights for responsible operations.
Learn more about how BigQuery and the Manufacturing Data Engine can help your organization achieve your business goals.
AI Summary and Description: Yes
**Summary:** Tata Steel’s implementation of a multi-cloud architecture utilizing Google Cloud has transformed its data management and operational capabilities. By centralizing data and employing advanced analytics, the company has significantly enhanced asset availability, product quality, operational safety, and environmental monitoring. This shift enabled real-time data processing and predictive maintenance, demonstrating essential advancements in the manufacturing sector through cloud integration.
**Detailed Description:**
The text discusses Tata Steel’s efforts to revolutionize its operational efficiency and environmental monitoring through a centralized and refined data architecture on Google Cloud. Significant aspects include:
– **Multi-Cloud Architecture**: The organization designed a robust architecture that brings together diverse manufacturing data via Google Cloud, allowing seamless data aggregation and analytics. This centralization focuses on improving operational safety and environmental stewardship.
– **Data Acquisition Framework**:
– The framework integrates data from various plant locations, employing technologies like Litmus, ClearBlade, and internal smart sensors using LoRaWAN.
– Operational Technology (OT) data from PLCs and Information Technology (IT) data from SAP are harmonized in Google Cloud BigQuery.
– **Data Processing and Visualization**:
– The architecture includes a robust OT Data Lake on Manufacturing Data Engine (MDE) that supports scalable processing and efficient cost management.
– Custom solutions were developed for extracting data from OPC servers and an innovative solution for monitoring crane health using IoT data.
– **Challenges Addressed**:
– They overcame legacy challenges that limited the accessibility of real-time data, addressing issues of data latency and infrastructure limitations.
– The implementation of secure data pipelines, including one-way data transfer mechanisms, enhanced data integrity while mitigating cybersecurity risks.
– **Integrative Solutions**:
– The adoption of actionable AI-enabled capabilities, such as asset health monitoring and event-based alerting mechanisms, significantly improved decision-making processes.
– A shift to predictive maintenance has been facilitated through the Integrated Maintenance Excellence Centre (iMEC), leading to reduced unplanned outages and improved equipment availability.
– **Worker Safety Enhancement**:
– Safety monitoring solutions, driven by in-house video analytics, and environmental monitoring systems highlight Tata Steel’s dedication to operational safety and environmental responsibility.
– **Commitment to Sustainability**:
– The Environment Canvas system captures various environmental KPIs, underscoring the organization’s commitment to sustainability while processing data through Google Cloud.
Overall, this transformation illustrates how Tata Steel is leveraging cutting-edge cloud and analytics technologies to enhance operational efficiency, safety, and environmental monitoring, setting a precedent in the manufacturing industry for future advancements in data integration and utilization.