ExceLANCE™ is a highly flexible software built on PI System® historian. It combines the modelling capabilities of GE’s GateCycle* and Patsimo’s capabilities in process data analytics to create a powerful real time thermal performance analysis solution. 


ExceLANCE™ specially suits power plant fleet owners with diverse portfolios as it can be configured to execute calculations of various plants sequentially. ExceLANCE™, is currently being used for real time thermal performance monitoring of over 4200 MW of Power plant equipment. Results of analysis can be

viewed on various PI System® clients and can also be used to trigger various types of notifications (email, SMS etc.) for any required maintenance actions. 

ExceLANCE™ is designed keeping simplicity in mind. It can easily be configured by anyone with good knowledge of GateCycle and MS Excel. For combined cycle plants using PI System® as a historian, a real-time performance monitoring solution using ExceLANCE can be implemented in less than a few months .



  • Detailed component level performance analysis of power plant equipment using GE’s GateCycle. 
  • Seamlessly interacts with PI System®, MS SQL, MS Excel etc. for data pull and data push 
  • Can analyse historical data as well as current data (online mode)Can easily incorporate new “plug-ins” to talk to other historians based on customer requirements 
  • Very easy to configure – end users do not need software coding skills 
  • Very little software footprint. Runs as a service in the background and needs very little user intervention. 
  • Can run on regular desktops/laptops. 
  • Single instance of ExceLANCE™ can execute calculations of multiple projects sequentially 
  • Drastically reduces the cost of deployment for fleet owners 



Performance Analysis 

Component level thermal performance analysis helps quantify plant level and component level degradation. 


Applies to 

Simple cycle, Combined cycle, Thermal plants, Mechanical Drives & Aeroderivatives and Co-generation units 


Fleet level scheduling 

Single license of ExceLANCE can perform calculations for multiple sites in sequence there by drastically reducing the cost of ownership. 



Key technology components and aspects of InDB Historian are outlined below 


Modeling: GE’s GateCycle is used for modeling the equipment and plant thermodynamic models 


Language: .NET MVC 



The most important assets of any industry are its people, equipment and data. However, many industries lack proper historians to store the huge amounts of process data that gets generated daily. Some industries face limitations on the number of tags that can be configured on their historians – and some with the amount of data that can be stored and retrieved. Any such limitations in data storage capability reduce the availability of historical data. This information is critical for understanding the response and behavior of equipment and processes under various conditions.


InDB Industrial Data Historian is based on Apache “Cassandra” and is designed to help industries retain their valuable operating data with minimal cost.



  • Connects to OPC Servers (Classic and UA), MS SQL, MS Excel 
  • No restriction on the number of “tags” that can be configured or on the number of simultaneous “clients” that can be used
  • Handles multiple OPC interfaces 
  • Manual data input capability 
  • Bulk Load capability allows old data from discreet MS Excel and CSV files to be archived in the historian 
  • Multi-node architecture of Cassandra makes ‘high availability’ feature available by default 
  • MS Excel based tag creation and modification makes it very easy to use
  • Ability to run on desktops/laptops
  • Very little software footprint – runs as a service in the background and needs very little user intervention
  • Seamlessly interacts with our Data Analytics and Diagnostics software to detect anomalies in equipment and processes
  • Cloud connector of InDB allows data to be selectively sent to the Cloud at user specified intervals based on requirements




No limitations on the number of tags to be stored in the InDB historian or the amount of data for the tags 


Fleet Level 

Fleet owners can save data from multiple plants 


No compression 

Native resolution of the data is not lost as it is not compressed. 



Web based user interface for real time visualization, trends with the functionality of email, annotations and data download, report generation etc., No need to install any new software. 


Published API 

Third party applications can be connected. 



Key technology components and aspects of InDB Historian are outlined below 


Cassandra: Apache Cassandra is a distributed NoSQL database management system designed to handle large amounts of data across servers, providing high availability with no single point of failure. 


Spark: Spark is a fast and general engine for large-scale data processing. Spark has the ability to combine SQL, streaming and complex analytics. Spark is used in exporting large volumes of data into Cassandra in a very fast and efficient manner.


Languages: Java 



Process and manufacturing industries use a considerable amount of expensive equipment. Maintaining the health of such equipment is very important. The unforeseen breakdown of any critical equipment may result in the closure of the manufacturing process or the entire plant, possibly causing significant loss of profits.


ProcDNA is powerful software that analyzes the most important aspects of power and process industries – reliability and thermal performance. ProcDNA can monitor the health of critical components in real time.

A combination of artificial intelligence, statistical methods and thermodynamics analysis are used. ProcDNA’s hybrid architecture makes use of independent methods to analyze data and to detect anomalies in critical assets of any industry. Advance warnings given by ProcDNA help in significant reduction of forced outages.


ProcDNA uses SimTech’s IPSEpro thermodynamic engine to analyze thermal performance at component level, as well as plant level. “First principles/thermodynamic models” are used to perform mass and energy balance across equipment and provide expected value of the performance to quantify and detect reasons behind degraded performance. Such models are built using design information, HMBD, P&IDs, Correction curves, PG test information and steady state operating data.


With ProcDNA, centralized monitoring of health and reliability of critical assets becomes easy and inexpensive. Results of the analysis can be exported to InDB historian and can also be used to trigger notifications (email, SMS, etc.) for any required maintenance actions.


Our software and solutions are designed to store and analyze data for various types of anomalies in all critical equipment in real time. This capability helps reduce forced outages.


Our software suite is for fleet owners with diverse portfolios, as they can be configured to monitor live data on a continuous basis. They can identify equipment anomalies, process anomalies, data and sensor anomalies in all critical assets, in addition to highly accurate component level thermal performance analysis of all major equipment.



  • Artificial intelligence and statistical methods are used to detect anomalies in equipment and processes
  • OEM-independent solution – analyzes data of all critical equipment – rotating as well as nonrotating
  • Seamlessly interacts with industry standard historians and data sources like OSI PI, MS SQL, OPC etc.
  • Can easily incorporate new “plug-ins” to talk to non-standard historians based on customer requirements
  • Can analyze historical data as well as current data (online mode)
  • Can run “offline” for simulation and testing purposes
  • Very little software footprint-runs as a service in the background and needs very little user intervention
  • Can run on desktops/laptops
  • Single instance of ProcDNA™ can execute calculations of multiple projects sequentially – minimizes the cost of deployment for fleet owners.



Fleet Level 

Centralized monitoring of health and performance of critical assets on a continuous basis on live data from multiple plants makes it easy and less expensive for fleet owners. 


Reduction in forced outages 

This solution is designed to store and analyze data for various types of anomalies in all critical equipment in real time which helps in the reduction of forced outages. 



Equipment to plant level performance analysis improves performance.



  • Java 
  • Jython  
  • Java Script