What Is GE Industrial IoT?
GE Industrial IoT refers to GE Digital's approach to connecting industrial equipment, turbines, engines, assembly-line machinery, to software that can read its output and act on it. It sits within the broader industrial internet of things, the practice of instrumenting physical assets so their condition can be monitored and predicted rather than inspected.
The distinction from consumer IoT is scale and consequence. A domestic sensor reports a temperature. An industrial asset produces continuous, high-volume telemetry, and a missed signal can mean an unplanned outage on equipment worth millions. That raises the bar on three things at once: ingest has to keep up with the data rate, analysis has to distinguish a real fault from noise, and the whole pipeline has to stay secure when competing manufacturers share the same platform.
Most industrial estates also run equipment from many different manufacturers, built over decades, with few shared interfaces or data formats. Some of it sits in places with no reliable network connection at all. So the engineering problem is rarely the sensors themselves. It is integrating inconsistent streams into something an operations team can act on, and doing it fast enough that the insight still matters.
The payoff, when it works, is a shift in how maintenance gets scheduled. Instead of fixed service intervals and reactive callouts, an operations team can see which assets are drifting toward failure and plan around them. That turns a cost centre into something closer to a forecast.
That is the problem this engagement addressed for GE Digital: a data pipeline that could ingest in real time, enrich and quality-check as it went, support machine learning downstream, and keep each tenant's data isolated throughout.
GE Digital, a pioneer in the Industrial IoT (IIoT) landscape, wants to harness the power of IoT data streams to drive digital transformation across various sectors of the global economy. This task involves integrating vast amounts of data from diverse sources, ensuring real-time processing, and providing actionable insights, all while maintaining data security and scalability.
The Challenge
GE Digital needed a resilient and scalable SaaS architecture to manage and integrate IoT data, including real-time data ingestion, processing, and integration with various systems. The company also needed to ensure data security, especially in multi-tenant environments where competitors might share the same platform. Complicating matters further, the rapidly evolving landscape of signals, sensors, and analytics demands agility and flexibility.
All of this existed against the need for monetizing digital products and business models via IoT while ensuring seamless integration with enterprise IT.
The Solution
To improve GE Digital’s control over both the structure and direction of its architecture, CloudGeometry instituted multiple AI Machine Learning improvements, including:
- Advanced Analytics and Machine Learning: Leveraged Python and AI to provide real-time insights from the data, enabling GE Digital to achieve data-driven efficiency, productivity, and profitability. Specifically, these changes enabled predictive maintenance, anomaly detection, and optimization algorithms.
- Data Pipeline Logic and Integration: AWS Sagemaker services provide a flexible data pipeline that integrates event data for downstream analytics, ranging from BI dashboards to machine learning models, enables data quality checks, and data enrichment, as well as drift detection and automated retraining alerts and triggers.
- Real-time Stream Processing: Unified edge sensors and centralized systems use real-time stream processing, employing AI for real-time decision-making and event predictions.
- Multi-Tenancy and Data Security: A secure multi-tenant application platform allows multiple customers and groups to share the same platform. It also provides the opportunity for future AI algorithms to provide intrusion detection, access control, and threat analysis.
- Automation and Orchestration: Kubernetes provided a platform for deployment, scaling, and orchestration, enabling AI to optimize resource allocation based on predicted workloads.
The Benefits
Transitioned to a future-proof technology strategy, GE Digital can now scale and manage IoT data integration effectively and enjoy continuous, rapid introduction of new features and improvements, optimizing software changes with high velocity and confidence.
<div class="case__txt--cols"><div><h4>Real-time Insights</h4><p>Achieve faster access to data, providing actionable insights across physical plants, customers, and platform partners.</p></div><div><h4>Operational Efficiency</h4><p>Automate IIoT workload deployment, reducing onboarding time for new industrial customers.</p></div><div><h4>Data Security</h4><p>Ensure data privacy and security in multi-tenant environments, giving confidence to industry competitors sharing the platform.</p></div></div>
