Achieve business outcomes that matter
Measure, manage, and operationalize your sustainability goals – including decarbonization, energy resources management, and reduced WAGES.
Achieve operations visibility and AI-based optimization, linking plant-floor actions to your enterprise sustainability initiatives.
A system of record to automate accurate GHG data collection, provide valuable insights, and identify ways to reduce carbon emissions
One modular solution to connect, see, control, and optimize DERs from a technical and an economic standpoint
Reduced operational costs and risks using Digital Twins, machine learning and predictive models
Increased network reliability
Advanced analytics to predict future asset and process performance for reduced variability and improved operations
Optimized asset performance to reduce risk and improve safety, reliability, compliance, and efficiency
Optimize assets and processes – from plant-level operations to the enterprise – with self-service process analytics software.
Minimized potential impact of anomalies
Comprehensive visibility of asset health for rapid situational adjustments with quality information
Streamlined mechanical integrity solution to reduce risk, maintain compliance and optimize resources
Develop, implement, maintain, and optimize asset strategies to effectively balance cost and risk
Operational visibility and analysis to reduce asset failures, control costs and increase availability
Performance Intelligence with APM Reliability is your partner in meeting your plant and fleet performance goals.
Predictive analytics software, helps prevent equipment downtime by detecting, diagnosing, forecasting & preventing emerging failures.
The AI-powered product automatically explores the space of operation of gas turbines, builds a machine learning model, and continuously finds the optimal flame temperatures and fuel splits to minimize emissions
BoilerOpt works within existing plant technology to improve boiler productivity and air-fuel ratios in a closed-loop system
Pre-built templates for equipment health monitoring, asset strategies, and process workflows
Operator rounds efficiency and operational impact
Secure and scalable data connectivity, analytics, and application services
End-to-end digital solutions reducing costs, empowering crews, and improving the passenger experience
Addressing fuel usage, carbon emissions, airspace efficiency, predictive maintenance, and more
Increased fuel efficiency and reduced waste
Early detection of aircraft and component degradation
Reduced costs related to disruptions
Visualizations and analytics to help airline decision makers identify waste in an airspace
Fuel efficiency reports, helping airlines operate at peak safety and efficiency while reducing their carbon footprint
Operational excellence and improved safety
Outcomes that move your business forward in fuel, safety, and predictive maintenance
Analysis of multiple flights, routes, and assets across years
Reduced environmental footprint
Services and solutions to reduce vulnerability and identify, detect, prevent and protect
Turnkey solutions to reduce vulnerability and identify, detect, prevent and protect assets and systems
A globally recognized benchmark for procurement of OT secure products.
Strengthened device security across the development lifecycle
Informed decision making with data and insights from across the enterprise
Native cloud service for a data historian.
Safe and secure management and orchestration of the distribution grid
Network-level optimization with high-performing distribution power applications
Overcome foreseeable load variations
Minimized disruption of service even in extreme weather conditions
Effective management and orchestration to unlock the power of renewables and DERs
AI/ML energy market recommendations to improve profit for renewables and thermal generation assets
Increased output and energy production at times of highest demand
A common network view to ensure electrical integrity, network validity and infrastructure management
Accurately model your asset network, support traceability, help assure data completeness, & support integrity management
End-to-end network connectivity modeling and data workflow management
Software designed to help grid operators orchestrate the grid
Increased efficiency and reduced costs
Secure-by-design connectivity and certification management, and faster operator response
Faster operator response and increased efficiency
Centralized visualization and configuration, digitized processes and intelligence
Full visualization and control seamlessly across devices, including phones, tablets and desktops
Best practices and proven deployment learnings
In-depth understanding of how GE Digital software can help your operations
Holistic performance management for today’s connected enterprise
Management of fast-moving processes as well as slower moving, labor-intensive jobs
Cost savings with improved manufacturing overall equipment effectiveness
Batch automation, regardless of the underlying equipment
Data analysis for quick identification of defects and better optimization of processes
Unified manufacturing data from disparate systems to better meet changing consumer demands
Procedures managed in an electronic format for consistency and predictability
Optimized production with better planning
Improved throughput with greater efficiency and lower costs
Materials to help you better understand GE Digital software and its robust functionality
Integrated solutions for improved efficiency and sustainability while supporting business growth
Mobile operator access to essential on-site HMI monitoring and control functions
Enable remote staffing, flexible resourcing and centralized monitoring across facilities
Boost worker productivity, automate tasks, keep safety first
Energy management for the zero carbon grid
Reliable mobilization of network assets to ensure maximum transmission of energy from multiple sources
Integrated solutions suite for energy market management
Decentralized data collection, data volume handling, and remote management
Getting the most benefit out of digitization and industrial IoT
Services that deliver best-in-class results
Rapid digital transformation wins based on industry-proven value cases and ROI
Best practices for your industrial processes to help build and maintain operational resilience
GE Digital’s expert service and support teams create value and deliver on business objectives
Expert service and support teams to maximize the benefits from your IIoT software
Improved efficiencies, optimized production and quality and reduced unplanned downtime
Increased reliability and availability, minimized costs, and reduced operational risks
Increased value from your equipment, process data, and business models
Facilitate documentation between airlines and lessors
Reduced costs related to disruptions with real-time visibility
The cornerstone of your journey to operational excellence
Operational excellence including improved reliability, reduced costs and managed risk
GridOS, the first grid software portfolio designed for grid orchestration
Reduced operational costs and risks using predictive models
Enhanced overall situational awareness
Field-connected operations and management
One modular solution that enables grid operators to connect, see, control, and optimize DERs from a technical and an economic standpoint
Operational efficiency and reduction in build costs while meeting regulatory regulations
Reduced operational and new build costs and improved field inspection productivity
A holistic picture of the grid, reducing cost and complexity from traditional inspection approaches
Optimized operations to best meet changing consumer needs
Reduced variability and improved operations.
In-depth understanding of our software and its functionality
A clear a path to operational transformation
Maintain consistent quality and reduce cost per ton
Optimized costs and improved reliability while reducing risk to keep your teams and communities safe
Streamlined end-to-end operations driving high-volume, high-quality production
GE Digital software is the backbone of modern plant operations
Improved reliability, increased availability, and reduced O&M costs
AI/ML to make your gas turbine's fuel and air controls smarter
Increase energy production at times of highest demand without costly maintenance adders or adversely impacting the maintenance interval
Operate from anywhere with secure remote/mobile operator controls
Inclusive outsourcing services that deliver best-in-class results
Achieve digital transformation
Expert service teams to maximize the benefits from your IIoT software
Reduced costs, lower risk, and faster response times
Analytics to predict future asset and process performance for reduced variability & improved operations
A common network view to ensure integrity, network validity and infrastructure management
Mission critical software to better operate, optimize and analyze your work to deliver results
Locate the best partners to meet your needs
Digital transformation acceleration
Technical and domain expertise that complements GE Digital’s industry leading applications
Assistance to accelerate your digital transformation and put your industrial data to work
Deep domain knowledge and technical expertise
Product training, industry education, and rigorous certification programs
More efficient and secure electric grid, greater sustainability and waste reduction
Solutions for today, scale for tomorrow
Increased reliability and reduced reactive maintenance leading to higher efficiency and reduced costs
Using Digital Twin blueprints, GE's Industrial Managed Services team monitors 7,000+ global assets
Understanding of the latest thought leadership that can be applied to your operations
Understand how our software and services help our customers solve today's toughest challenges
Experienced team dedicated to customer success
Success stories and product updates from the world of Electrification Software
Analyst and third-party expert opinions of Electrification Software and our software and services
White papers, product overviews, and other content to help you put your industrial data to work
Experience in leading edge software development and business working with best-in-class leaders
Understand how Electrification software and services helps our customers solve today's toughest challenges
Blog
The Industrial Internet of Things (IIoT) produces massive amounts of data at record speed. The sheer volume, variety, and velocity of this data can be overwhelming, especially to industrial companies--many of whom struggle to translate big data into meaningful and effective process improvements.
That’s where data science comes in.
Data science is the art of analyzing data and applying scientific principles to uncover key patterns that drive significant business value. Leveraging data science and its’ techniques such as machine learning—an algorithm’s ability to gain insight from data patterns—can further drive value by providing clearer insight into massive, confusing, and siloed industrial datasets.
More and more companies are embracing IIoT to drive better business outcomes, but they still aren’t making the most of the data they collect. The following examples showcase how data science can help solve some of the biggest and most common problems industrial organizations face with big data.
In the first part of this blog, I will discuss the application of machine learning to system monitoring, prediction and forecasting, and system survival analysis. Several other use cases will be covered in the second part of the blog, so stay tuned for more.
You need to make sure your systems operate properly and take appropriate action (raise alarms, create work orders, etc.) as soon as there is abnormal behavior or failure in the system or its components.
Many companies are accustomed to monitoring system components and reporting failures. One way around the manual monitoring approach is to implement Business Rules, which are logics defined by domain experts to detect abnormal behavior in a system and take proper actions when needed. A major challenge with the Business Rules approach is that people do not have the analytic capacity to simultaneously take hundreds and thousands of parameters into account while also considering every edge case. Also, these rules might be contradictory and could therefore end up generating numerous false alarms and target misses.
This is where data science, and, more specifically, machine learning can help. Instead of trying to define Business Rules, your data science team or a partner can use a machine learning algorithm that will process the data coming from your system and automatically separate failure operating conditions from normal ones. This process is well known as anomaly or outlier detection. Unsupervised machine learning algorithms such as LOF (Local Outlier Factor), k-NN (k-Nearest Neighbors), PCA (Principle Component Analysis), and unsupervised SVM (Support Vector Machine)can be used to address the anomaly detection problem, among others. The algorithms may produce different accuracy of detection dependent on your specific domain and corresponding data, and data scientists will explore and compare the accuracy to suggest the best approach for your specific needs. Few data science experts, including GE Digital’s Data Science Services team, will also incorporate physical modelling into anomaly detection to further improve the quality of outcomes needed for industrial applications.
By utilizing machine learning to automate the monitoring of processes, you will be able to not only avoid time-consuming business rules creation, but also “catch” failure situations that you’d never thought about before and weren’t captured by the rules.
Once your team has the results of automatic monitoring performed by the algorithm, your subject matter experts can review the outcomes and confirm or reject the failures that were detected. The machine learning algorithm will learn from such operator’s input, and the accuracy of failure detections will be improved automatically.
Even if you are able to identify and quickly respond to system failures, downtime costs are still a major factor to consider. Is there a way to predict how the system will work under future performance parameters and forecast any potential failures?
Instead of relying on the reactive strategies used in traditional monitoring and visualization methods, consider a predictive approach. By feeding data into a predictive model, you can simulate the relationship between multiple variables and forecast how the system will perform under a variety of conditions. This approach also allows your staff to take proactive action to manage future requirements for incoming events.
For business users, in the majority of such cases, data scientists will create a Health Index for an asset that will show the probability of asset or system health in the prediction interval. This creates an easy-to-understand metric that requires a sophisticated mathematical approach. For example, Linxia Liao from GE Digital’s Data Science Services team recently co-authored a paper for the International Journal of Prognostics and Health Management (of assets) in which he proposed a method to integrate feature extraction and prediction as a single optimization task by stacking a three-layer model as a deep learning structure.
The proposed method was tested on a small dataset collected from a fleet of mining haul trucks. The model resulted in the ``individualized'' failure probability representation for assessing the health condition of each individual asset, which well separates the in-service and failed trucks. The proposed method was also tested on a large open source hard drive dataset and it showed promising results.
A good example of the benefits received with the predictive approach is Intel, who saved $3 million using predictive analytics to prioritize silicon chips inspections in just one year alone.
Real-world equipment reliability can be significantly different from the stats provided by manufacturers. Is there a way to predict reliability using actual data?
By analyzing actual field data, machine learning and artificial intelligence can predict the likelihood of equipment failures and warn you ahead of time, regardless of what the manufacturer’s predicted failure rate may be. This allows your team to replace equipment and key system components before they fail, which reduces downtime and revenue loss due to system failure.
Here’s how you can get your company started in data science.
First, you need to choose the problems that you’ll most benefit from resolving. Building a data science solution will require investments of time and capital, so make sure first that the potential benefits outstand costs. Our Data Science Services team has developed a methodology delivered in a two-day workout to help our customers systemize problems or key performance indicators (KPIs) they’d like to solve or improve, and prioritize them.
Second, before investing fully investing in data science, I highly recommend assessing the quality of the data available to you in order to develop an understanding of the relationships between data sources. The goal of this step is to ensure that the quality of data is sufficient enough for analysis, and that the data has entitlement in regard to the problem you would like to solve. If there is no value in the data in regard to the KPIs you would like to predict, you’ll have to discuss the data gaps and instrument processes with data collection.
Once you’ve proven that data is good enough to use in modelling, you may continue with testing different machine learning algorithms or other modelling techniques to develop a solution. Your data science team should be designed to allow “citizen data scientists”—users with some domain knowledge and a surface-level understanding of machine learning algorithms—to apply different techniques and algorithms and evaluate the results. This will help your organization determine whether the system is truly working more efficiently and if the value justifies your investment in data science.
As is frequently the case in large industrial companies, different departments have access to different data resources with little cross visibility. This must be addressed early on, because data convergence is a critical component in using data to drive better business decisions. To that end, you need a platform that allows the analytical framework to easily access representative data samples from a wide variety of data sources along with random sampling, data aggregation, data cleansing, missing value protocols, data normalization, rescaling, and more.
GE has developed both an industrial applications platform, Predix, that has extensive data ingestion and analytic capabilities, as well as services, such as GE Digital’s Data Science Services, that you can leverage to speed up value extraction from the Industrial Internet.
With data science, industrial organizations can derive more value from data and use that intelligence and insight to drive better business outcomes and intelligent business decisions.
Editor’s note: The original idea and inspiration for this blog post was drawn from our fellow data scientist, Massoud Seifi.
Putting industrial data to work.
GE Digital is a leading industrial software company - transforming how our customers solve the toughest challenges by putting industrial data to work. We bring simplicity, speed, and scale to our customers' digital transformation with industrial software that delivers breakthrough business outcomes. By partnering with our customers to transform industry, our software is enabling power-generating assets to be more efficient and reliable, the electrical grid to be more secure and resilient, flights to be more efficient, and manufacturing to be smarter and reduce waste.
Build a data science solution.
Reap benefits of data science and rapidly create value.