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Siemens Energy application engineers leveraged metaphactory's low-code approach to build a smart turbine spare parts application that allows turbine service engineers to save thousands of hours on manual effort.
Gas turbines of Siemens Energy are used worldwide in different environments and with customer specific configurations. Managing a broad variety of spare parts and configurations for each turbine is a challenge. metaphactory and Amazon Neptune enabled Siemens Energy to build a Turbine Knowledge Graph and visualize the connections between similar parts across the entire fleet of large gas turbines.
The customer
With its products, solutions, systems, and services, Siemens Energy addresses the extraction, processing, and transport of oil and gas as well as power and heat generation in central and distributed thermal power plants, and power transmission and technologies for the energy transformation, including storage and sector- coupling solutions. The Generation Service business unit of Siemens Energy is responsible for global services and maintenance around large gas and steam turbines and generators.
The challenge
Siemens Energy large gas turbines are used in power plants around the world in different environments, be it with the ultimate goal of power generation for households or for industrial complexes.
The turbines generally require customer-specific configurations, fitting the customers' environment and line of business. This results in heterogenous digital representations of turbine configurations. Lacking a standardized way for modeling machine structures, a broad variety of tools was in use for documenting these heterogeneous representations.
When large gas turbines are scheduled for maintenance or repairs, the correct spare parts need to be provisioned in the right amount and at the right time in order to avoid extended turbine downtimes. However, because these turbines are configured to fit the customer environment, each customer needs an individual spare part catalog and packages for maintenance. Since turbine configurations were maintained in heterogenous tools and formats, high manual effort was associated with creating and maintaining customer-specific digital machine structures.
To address these challenges, the Generation Service business unit of Siemens Energy put together a team dedicated to creating and perfecting a smart and targeted solution for maintaining spare parts of large gas turbines.
The solution
Siemens Energy decided to build a software application that would provide business users, i.e., turbine service engineers, with a unified overview of all large gas turbine configurations and spare parts catalogs previously stored over multiple, diverse data sources. This software application would allow Siemens Energy turbine service engineers to compare existing configurations, identify identical parts, and give recommendations on the quantities for required spare parts, thus reducing warehouse inventory and avoiding extended turbine downtimes. For this, Siemens Energy opted to leverage the power of Knowledge Graphs and develop a solution comprising metaphactory and Amazon Neptune.
Knowledge Graphs – A Unified View over Your Data
When configuring a new turbine for a customer, turbine service engineers generate a machine structure documentation. However, this was previously done in heterogenous formats and custom documentations often listed spare parts in differing sections. This also resulted in identical spare parts often being recorded multiple times in the internal systems.
The data represented in a turbine documentation is hierarchical, which means that the structure of the documentation can be very well represented as a graph.
Excerpt of the Knowledge Graph showing a request item and connected resources
The flexibility provided by Knowledge Graphs to introduce new links and connections between spare parts and enable insights into similarities between machine structures despite them being documented differently was unprecedented. "Already first experiments with the RDF [Resource Description Framework] graph data model and graph databases were very successful and we could really see this working not only for this use case, but for many others in the future," said Lutz Lukas, IT Solution Architect at Siemens Energy.
Visual exploration of spare parts
"The Siemens Energy solution engineers managed to truly tap the potential of Knowledge Graphs and show what is possible, laying the stepping stone for further projects that leverage semantic and knowledge graph technologies," said Dr. Daniel Herzig-Sommer, COO at metaphacts.
Amazon Neptune, a managed graph database service, fits perfectly into the cloud-first strategy driven by Siemens Energy IT, which focuses on reliability, scalability, reduction of maintenance and integration with their existing platform on Amazon Web Services (AWS). metaphactory for Amazon Neptune was purchased via the AWS Marketplace, which allowed Siemens to directly get metaphactory running in their private AWS cloud and connected to their Amazon Neptune service.
Agile Development of End-user Applications
metaphactory enabled the Generation Service business unit of Siemens Energy to build a Knowledge Graph on top of Amazon Neptune. The Knowledge Graph provides a unified view of the data coming from multiple, diverse data sources and allows business users to explore and analyze gas turbine configurations from different perspectives.
The goal was to bring data to business users as fast as possible and ensure that the final product matches business requirements. metaphactory is a low-code platform used to rapidly build data-driven, end-user facing applications matching individual needs. The involved Siemens Energy solution engineers used the metaphactory platform and its out-of-the-box components to rapidly develop a custom application on top of Amazon Neptune. The development of the application was done in-house with metaphacts providing support for metaphactory deployment and configuration questions.
metaphactory allowed business users to already use the application for real world tasks during the development process. As such, feedback was collected early on and could be implemented in an agile way, allowing for new features to be added incrementally along the way. "metaphactory offers many out-of-the-box components and allowed us a shorter time to market. The fact that we could prototype and develop our application in-house without much coding convinced us," said Amit Vaidya, IT Project Lead at Siemens Energy.
Data Curation and Data Quality Assurance
The data quality workbench delivered with metaphactory allowed Siemens Energy to find inconsistencies, curate the data and constantly monitor data quality. Identified data quality issues can now be traced back to the data source and resolved at their origin.
Fleet-wide Search and Visualization
metaphactory's search interpretation engine leverages advanced algorithms to recognize the data structure and schema and return relevant results within seconds. In this case, it uses the data model to support business users in building targeted, natural language queries to quickly find spare parts of large gas turbines and analyze where these spare parts are in use. And all this without the user having to know the data model and the relations between concepts.
Intuitive end-user search interface across the fleet of large gas turbiness
From there, users can explore further using metaphactory's rich set of components for interactive visualization and exploration and gain meaningful insights into relations between spare parts, turbines, customer-specific configurations, maintenance schedules, and maintenance history.
The results
"A key differentiator of the metaphactory platform was that it delivers a great combination of graph data management, custom visualizations, data quality assurance, and natural language keyword search in one platform," said Amit Vaidya, IT Project Lead at Siemens Energy. metaphactory allowed business users to test and interact with the data and provide feedback while the data experts were building the data model and integrating data from various sources into the Knowledge Graph. The fast development of the spare parts management application resulted in shorter time to market for the internal business solution.
Data quality assurance is another key aspect which was critical to the business solution. metaphactory alongside the in-house developed application helps business users identify and correct inconsistencies in gas turbine documentation, thus reducing the number of error rectification requests and delivering an intuitive user experience.
Access to the right data through the intelligent keyword search framework resulted in higher productivity and an intuitive experience when exploring the Knowledge Graph. The previous approach, which implied asking a different department for a custom SAP report, was replaced by an on-demand report generation engine which not only reveals more insights into the data but also resulted in time savings of up to 1,500 hours in the first year already.
"Through fleet-wide analysis our business users can take data-driven decisions when optimizing outage- specific spare part packages. An optimized package recommendation means happy customers, as it allows us to give them more comprehensive and precise recommendations as to which spare parts to order for which outage," concluded Lutz Lukas, IT Solution Architect at Siemens Energy.
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Siemens AG is one of the leading providers of factory automation solutions and components as well as Manufacturing Execution Systems and Product Lifecycle Management Software. To support human manufacturing planners and line operators in their daily tasks and increase the autonomy of production machinery, the research group for "Semantics and Reasoning" of Siemens Corporate Technology initiated a Manufacturing Knowledge Graph leveraging metaphactory. The goal was to test the feasibility of Smart Manufacturing Planning and Smart Manufacturing Execution concepts utilizing semantic technology. For this, the team created a Manufacturing Knowledge Graph to capture heterogeneous data sources and expert knowledge and built an AI-based knowledge graph application to automate the allocation of suitable production equipment.
The customer
Siemens AG is one of the leading providers of factory automation solutions and components as well as Manufacturing Execution Systems (MES) and Product Lifecycle Management (PLM) Software. This case study focuses on the results achieved in a feasibility study for Smart Manufacturing Planning and Smart Manufacturing Execution that was conducted by the "Semantics and Reasoning" research group of Siemens Corporate Technology in cooperation with Siemens business units active in the field of manufacturing.
It opens the door for including knowledge-level information integration into future manufacturing solutions as part of the Siemens product portfolio, such as Opcenter.
The challenge
Knowledge-level Transparency for Flexible Manufacturing
Industry 4.0 promises to increase the autonomy of machines through smart factories, where cyber-physical systems help bring more flexibility and adaptability into production. The Knowledge Graph experts in the Semantics and Reasoning" group of Siemens Corporate Technology initiated a Manufacturing Knowledge Graph to support flexible manufacturing in engineering as well as operations. Using the Manufacturing Knowledge Graph, machines can be equipped with explicit semantic descriptions about their characteristics and capabilities (aka skills). This allows cyber-physical systems to compare these descriptions to production requests and decide on suitable candidate machines when manufacturing new product orders.
The challenge that many industry players face here is the integration and representation of data required for automating allocation decisions. Information from a variety of sources needs to be connected at the "right" level of granularity. At the same time, expert knowledge about machine capabilities, which is today locked in the engineer's mind, needs to be made machine-interpretable. Moreover, transparency over the decisions autonomously taken by machines needs to be ensured and is critical for human operators to trust the automated decision making process.
Fortunately, the explicit semantics encoded in Knowledge Graphs help to implement automated explanation techniques that inform operators and planners about the reasons why a machine is allocated to a task or not.
To fully reap the benefits of semantic technology and Knowledge Graphs in addressing the challenges described above, Siemens needed to tackle the following three aspects:
- Find the right level of detail for knowledge representation of the manufacturing domain that is sufficient for automating allocation decisions but also manageable by developers and end users.
- Control the knowledge acquisition bottleneck by integrating information from other data sources into the Manufacturing Knowledge Graph, e.g., such as product design information like Bill of Materials and Bill of Process from PLM tools like Teamcenter.
- Provide expressive visualizations and interfaces for end users such as planners and line operators to easily and intuitively access the knowledge structures, configure the matchmaking process, and influence the automated decision making.
The solution
metaphactory, the innovative platform for building Knowledge Graph applications, was deployed to manage the Manufacturing Knowledge Graph and help Siemens build an
intuitive application for producibility checks on top. This resulted in the design of an AI-based solution that utilizes techniques of automated reasoning to decide whether a production step can be allocated to a particular machine based on the semantic descriptions of the machine's capabilities. The overall solution was designed to address two core use cases:
- Smart Manufacturing Planning: Support human manufacturing planners during the manufacturing planning process prior to production by providing suggestions for valid production plans.
- Smart Manufacturing Execution: Introduce additional functionalities to MES software to fully automate the setup and preparation of machines and route products to suitable machinery. This is particularly relevant to and significantly reduces the cost and effort for realizing low-volume orders.
The different aspects of the solution developed by Siemens Corporate Technology on top of the metaphactory Knowledge Graph platform are described in the following sections.
Supporting Automated Decision Making with Knowledge Graphs
Representing domain knowledge about equipment, products, materials and processes, matching machine skills against required production steps, planning production workflows based on skill knowledge, and the justification of automated decisions are all made possible using the Manufacturing Knowledge Graph. Figure 1 depicts the three pillars in the manufacturing model which need to be accounted for: materials, processes, and equipment. An unprecedented benefit of the Manufacturing Knowledge Graph is that various data sources can be integrated and federated on-demand using metaphactory, and that the schema used can be flexibly and easily expanded to accommodate for these heterogeneous data sources. The Knowledge Graph solution was deployed in an AWS environment using an open-source graph database.
Managing and Querying the Manufacturing Knowledge Graph
For this feasibility study, metaphactory was leveraged as a rapid application development platform, allowing for the flexible integration of the Manufacturing Knowledge Graph with Siemens' PLM and MES software.
Components for workflow planning and skill matching were hosted in a cloud-environment alongside the Knowledge Graph and connected to metaphactory via SPARQL for access to knowledge graph content.
Using its federation engine, metaphactory enables a unified overview of the triple store and external, heterogenous data sources, thus being a highly efficient middleware solution for managing and querying the Manufacturing Knowledge Graph.
Visualizing and Editing the Manufacturing Knowledge Graph
As a frontend solution, metaphactory enables transparency for end users. Utilizing these features, manufacturing planners and line operators have access to knowledge graph content and are provided insights into machine setup and states, connections, skill characteristics, etc., as exemplified in the screenshot below.
Visualization of manufacturing knowledge in metaphactory for user interaction and transparency over the knowledge graph
Using simple search interfaces to define specific queries or by navigating through graph structures in the Web browser, end users can obtain answers to a number of production-specific questions, such as:
- Which tasks need to be performed to produce a product?
- Which tasks can be performed utilizing existing equipment?
- Which required tasks can be mapped to existing equipment?
- Can this new product model be produced using the existing equipment?
- Which part goes to which machine?
- In which order should the production steps be performed?
Answers to these questions and more are provided flexibly in various formats, be it interactive and intuitive graph visualizations, tables or charts. Moreover, other Web-based visualization tools for complementary visualizations are also seamlessly integrated via Webbased standards and interfaces like SPARQL and REST.
The screenshot below exemplifies the integration of a REST service for an AI-enabled semantic matchmaking backend component through metaphactory's federation engine Ephedra. The component applies interactive skill matching to data in the knowledge graph and displays the results on the fly.
Interactive skill matching of production operations against the skills of a production line
The results
"Mass personalization is a new norm in manufacturing. Production resources can leverage Knowledge Graph Semantics to dynamically match their capabilities and availabilities to individual product configurations," says Raffaello Lepratti within Siemens Manufacturing Operations Management.
This feasibility study demonstrated that significant time and cost savings can be achieved through Smart Manufacturing Planning, should this solution be deployed into the production environment. Manufacturing planers went from needing to review approx. 1,400 to just 40 plans for the production of new orders. This reduction could be calculated based on automated machine allocation and material-flow constraints, and promises significant savings in working hours for the planners.
In the Smart Manufacturing Execution use case, the complete semantic technology stack could be successfully integrated in a running automation plant seup. Knowledge-based allocation decisions became part of the loop between readily available Siemens PLM and MES software and the underlying Siemens automation hardware.
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Aiming to support engineers in developing and introducing new materials for production, Bosch is building a knowledge graph that exposes material science knowledge and supplies high-quality answers about existing materials in a timely manner.
In a paper presented at the ISWC 2019 Industry Track, Bosch describes how the knowledge graph integrates information stored across multiple sources, and how metaphactory is used to satisfy complex query needs through a user-friendly interface for standard keyword search and semantic-based faceted search.
Read paper here »
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Leveraging semantic technologies and the metaphactory platform, the Industrial Knowledge Graph has become an integral element in Siemens's strategy towards intelligent engineering and manufacturing. It powers various business use cases, including gas turbine maintenance, building automation, risk management, factory monitoring, and internal R&D management.
To learn more about Siemens's Industrial Knowledge Graph, have a look at the following material: