The aim of this thesis was to develop a practical solution for industrial remote monitoring of automation equipment including real-time data acquisition from Siemens automation devices and storage of the obtained data to the SAP HANA Cloud database. Because HANA will be implemented by Siemens as a Cloud service, it was also reasonable to introduce important features and benefits of HANA technology for the industrial sector.
The implementation of data acquisition solution was divided into three sections. The first part consists of a small research about HANA features and benefits and the search for technologies for data acquisition application. The second part includes the development of remote monitoring interface and data acquisition application. Finally, s120 frequency converter was used for testing and demonstration purposes of the developed remote monitoring solution.
As a result, UA Connector application was developed as a service for data acquisition using web technologies. The data acquisition application uses the OPC UA technology to get values of Tags from s7-1500 programmable logic controller or WinCC RT Advanced application in real time. The application was developed using Nodejs, Angular.js and Web Socket technology.
The application complies with assigned requirements. Additionally, the remote monitoring interface was developed using HANAXS and SAPUI5 tools. Via the user interface power unit’s cooling fan the operating hours of the s120 frequency converter can be monitored as one example. Because the data acquisition application can be provide das a service and SAP HANA is also a service, the presented solution is very close to the Internet of Services concept.
BACKGROUND OF THE HANA PLATFORM
Overview of the HANA Platform as a Service:
The HANA platform is built for enterprise-grade applications with prebuilt tools and technologies for data real time analytics. Real time means operation speed of milliseconds to seconds. To make a custom application on the platform, HANA includes software development components and programming libraries.
In-memory Processing Engine:
In contrast to traditional database management systems, the primary physical storage type of in-memory database is the computer’s main memory. The main memory is the medium between persistence and operation systems such as SSD and CPU. The main memory is made of DRAM volatile memory type providing a constant access time to any memory location. Nowadays, the main memory capacity in enterprise-grade Cloud systems can reacha capacity of many terabytes. An in-memory database is combined with a traditional database system for data logging and recovery as well as a storage for outdated data.
Service Security Overview:
Data protection is important in manufacturing, as well as in any other business. In this section data processing security of the industrial cloud service will be described. Data acquisition to a remote database includes data transfer over internet environment and storage on remote service. Especially, data handling in a secure way is an important issue in the connection of industrial systems and devices to internet environment.
BACKGROUND OF THE DATA ACQUISITION SERVICE
The HANA connector is a client-side software which works as a communication interface for acquisition service on a client system. The connector maintains security and consistency of data between the service and Remote HANA Database.
The OPC Unified Architecture (UA) is a platform independent service -oriented architecture that integrates all the functionality of the individual OPC Classic specification into one extensible framework” OPC UA is standardized by OPC Foundation and thus is supported by many industrial automation equipment providers and manufacturers.
Other Tools and Technologies:
MongoDB is NoSQL cross-platform document database that stores data as complex structured documents in JSON format.
The assignment was to develop a software that can transfer data from local industrial systems and devices to remote HANA database where from data can be used for remote system monitoring and data analytics. Additionally, to test the developed solution the requirement was to make a lightweight demonstrative remote monitoring user interface.As a useful demo case, a frequency converter was chosen with its power unit’s cooling fan working hours as an example of remote monitoring.
Acquisition Service Functionality Should Include
- Transfer tag value from PLC S7–1500.
- Acquisition interval at least 1s.
- Data should not be lost on online connection breaks.
- Data should be automatically replicated when connection is established again.
- At least INT and REAL data types should be supported.
- Tag’s value needs to be stored with its source information as name and source timestamp.
Demo Application Functionality Requirements were the Following
- Show some simple time axis trend line for stored “fan operating hours” parameter’s value.
- HANA web service user interface.
Data Acquisition Service Development:
Name: UA CONNECTOR(Generic name). Version: 0.1.0. Versioning model: Major.Mi-nor.Patch
- Nodejs v.4.x.x LTS.
- MongoDB v.3.x.x or greater1.
- DB tunnel: SAP Neo-java-web SDK 1.92 or higher.Alternative: SAP Cloud Connector.
After discussion with support specialists about useful maintenance cases of SINAMICS s120 frequency converter, it was decided to check power unit module’s fan operating hours via developed UA CONNECTOR acquisition service and HANA SAPUI5 application. A reason for remote monitoring is that if the Power Unit fan’s operating hours reach value 4000, the converter’s operation should be stopped until the fan is replaced with a new one.
As a conclusion of this work overall possible benefits of using data acquisition as a Cloud service can be examined . First of all, it can be mentioned that cloud services are extending the customer’s local IT infrastructure without extra hardware, software and maintenance costs. This reduces Total Cost of Ownership for implementing real-time data analytics and massive data storage.
Cloud service based on HANA would be useful platform for manufacturing execution system and integrated with the ERP system. Vertical system integration provides possibilities for tracking production process through the entire business, and thus production can be optimized and its cost reduced. Industrial machinery such as complete generators, pumps or motors are valuable sources of information. Information about machine environment and operation values can be monitored in real-time, which enables a new service business model.
A concept of the service model is that over cloud service, machine operators or support engineers are able to remotely monitor a device’s health and predict future maintenance milestones and carry out preventive maintenance. All that enables new service agreements such as process optimization analytics, maintenance service with real -time tracking and data aggregations , or the industrial machine itself can be offered by its manufacturer as a service for manufacturing business.
For example, the pump manufacturer ABS offers pump working power as 1h/1€ for factory FZR, the data about the pump is available over the internet via third party SMN cloud service. In this service, a pump maintenance service specialist from ABS monitors the pump’s health over an application that they have built on top of a Cloud service platform. The service predicts the next maintenance milestones and required pump parts etc.
The pump is still owned by ABS and the data is stored on SMN database. As a result, FZR has addition quality service and reduced TCO, and ABS does not need costly IT infrastructure for data storage and analytics, and SMN as a Cloud service provider get more supplementary service contracts which increases the revenue of the company. Figure 19 shows a potential use case for the service.
Furthermore, HANA Cloud service’s real-time analytics feature with build-in predictive analytics libraries enables for the industry predictive maintenance that is the most demanded feature for Cloud Service applications. It mostly applies to machinery related maintenance. Regular maintenance prevents failures passively but active tracking can prevent even unexpected failures. Also, gathered data can be used for increasing production yield through clarifying relationships between important parameters and production output.
Quality issues in production can be identified in detail that makes it possible to identify a time when defects began and may help with product mass rollback to clarify how much and what products must be returned. Additionally, data can be stored with source geographical information and used for production related logistics optimization(Plattner 2015).
Following features should be added before using UA CONNECTOR data acquisition service in a customer’s system:
- Software automated unit and behavior tests with test coverage at least >90%.
- SAML-based HANA DB user authentication.
- Tag properties read on-demand.
- User login system and settings protection.
- Multi-session OPC connection.
- Event logging features.
- Online help documentation.
- Make OPC UA client component of the service more unified with support all OPC UA compatible products.
- Remote UA CONNECTOR service health monitoring.
- Make Alarm & Events system.
Authors: Ivan | Stankevichus