The company’s Industry 4.0 initiative is aimed at generating useful information from massive volumes of operational data.

Gunnar Zergiebel, head of Tenova TAKRAF’s Electrical & Control Systems group, sketched out the company’s strategy to advance Industry 4.0 concepts and benefits for its mining clients:

In recent years, Industry 4.0 has evolved from an abstract marketing term to a real-life beneficial use concept. Often mentioned in conjunction with terms like the Industrial Internet of Things (IIoT) or cyber-physical systems, it describes the continuous development of three previous Industrial Revolutions toward a fourth: the digitalization of almost all areas of someone’s life, including industrial automation and IT systems. Today, increased acceptance of autonomous automation, real-time processing of huge amounts of data, Artificial Intelligence (AI), robotics, pay-per-use business models or cloud computing, to mention just a few, is enabling a more efficient and effective economy.

Pre-empting the Pyramid

In the past, control system developments were often based on the so-called classic automation pyramid. In this pyramid, various levels of process automation are separated from each other and employ specialized communication protocols between the individual control layers. In the world of IIoT, this type of structure is not useful as (theoretically) even an individual sensor in the field can receive data from or send data to high-level analytical systems without going through all the levels of the traditional automation pyramid.

As much as such IIoT concepts increase flexibility, they also make standardization of and interconnection between various systems — including legacy control equipment — difficult. In the recent past, several attempts have been seen to standardize data exchange between field equipment and a cloud environment by means of common protocols, such as MQTT or standardized interfaces such as OPC UA. In addition, practical issues such as ensuring sufficient bandwidth between remote systems and cloud server architectures are often a unique challenge when it comes to isolated machines and sensors working in remote areas within a mining environment.

Many of the common IIoT systems on the market are based upon a structure consisting of some form of edge computing within local devices (such as data collectors or a local PLC), which cull useless data, forwarding only useful data to a database structure in a cloud environment. In such a cloud, there are virtually unlimited possibilities and computing power in order to process this collected data, enrich it with information from other sources or third-party applications, extract valuable information, perform predictions, anomaly detection or derive complex KPIs. In addition, there are excellent tools available for presenting the results of such analyses to different user levels and on a large variety of different devices in a very efficient way. Plus, system architecture makes it possible to relocate the workplace of operators or service engineers, traditionally located on site, to any remote location around the world.

With context-sensitive user interfaces such as Virtual Reality (VR) or Augmented Reality (AR), the user is able to perform and handle tasks in a more effective and safer manner. AR provides the user with additional information projected into a real-world view via a tablet or smart glasses. VR in turn sends the user to a complete, computer-generated virtual world established through a special device such as the Oculus Rift or the HTC Vive.

Digitalization and IIoT developments are more like a journey that has just begun, rather than a completely defined approach to a well-defined destination. Many applications seen on the market can be considered pilot applications, developed in order to explore options and demonstrate their usefulness toward finally obtaining acceptance by clients and/or users. In today’s IIoT applications, human interaction is often required as machine learning and AI capabilities remain basic in many aspects.

Current and Future IIoT Offerings

TAKRAF has a large base of installed machines that daily generate valuable data from which it can gain information regarding optimization of design, operation and maintenance or even troubleshooting. Leveraging this opportunity, TAKRAF rolled out an initiative for the collection of operational data for selected representative machines and/or key components. This “data lake” is being used for the development of concepts to generate insights regarding machine condition and machine KPIs of great value for customers and internal use. Within this process, it was learned that even though data is the basis for almost all IIoT strategies, there is a significant difference between data and information. The most important and possibly most difficult step is generating useful information from collected operational data.

Following this logic, the company developed a proprietary cloud environment, together with their colleagues from Tenova in Italy, based upon the Microsoft Azure platform. This environment provides the capability to transfer and store data from edge computers in the field and process them in a cloud environment with a wide variety of available technologies.

As a side benefit, such data sets within the cloud can be easily exchanged with systems that may already exist within a mining operation’s IT infrastructure. For example, they have developed a web-based pilot application that shows basic KPIs and machine condition in a simplified and easy-to-understand manner on dashboards. This tool, called WIDE, can be used by clients and/or by design specialists with a view to combining operational experience with TAKRAF’s deep knowledge of the system’s design. The possibilities to develop further applications that suit the needs of clients are virtually unlimited.

Another topic on the R&D roadmap addresses “digital twins.” As the company invests significant effort in the design of machines utilizing digital 3-D design tools, it is a logical step to reuse this engineering data through the entire lifecycle of a machine. There are also promising tools available that link design data with live data coming from machine control systems or with asset management data such as documentation or maintenance activities into one common plant model.

Other outcomes from R&D activities include remote support solutions employing “smart” glasses. With these, they are able to connect experts in an office with their commissioning engineers or their clients’ maintenance staff on site in order to solve specific problems quickly and effectively, avoiding long trips and associated travel costs.

Another smart tool, presented at the bauma 2019 mining exhibition in Munich, is based on Schneider Electric’s Augmented Operator Advisor. This tablet-compatible software tool is able to determine its position in the field based upon image recognition of the various components around the user. Based upon this recognition, various “points of interest” can be defined together with context sensitive information, such as manuals and maintenance procedures. Live data can also be made available to the user.

Another important item on the R&D agenda is electronic spare parts catalogues to simplify and improve client experience. Linking predictive maintenance tools into such electronic spare parts offerings will enable clients to conduct proactive maintenance in an effective and cost-efficient manner, thus increasing the availability of our equipment.

Data Acquisition and Cloud-connection Tools

TAKRAF has recognized the benefits that IIoT technologies can provide its customers, together with the improvement of their own internal processes. As a result, IIoT is fully supported throughout the organization and plays an increasing role across all its product lines.

For example, using currently available IIoT technologies TAKRAF has equipped a select sample of machines with remote data logging systems as a part of its R&D initiatives. One of the first successfully integrated systems is on a high-capacity spreader and tripper car at a large overburden-handling project in Southeast Asia. This system was designed and runs on hardware and software components supplied by iba AG, a data logging and analysis specialist. A machine connector reads defined data from the Profibus DP of the machine control system — without jeopardizing its security and/or integrity — and writes it to the iba PDA server where it is locally stored and analyzed to provide automatically generated reports. Recorded data is then sent to the head office simultaneously or in batches.

Selected data can be transferred from the iba server to the Tenova cloud environment, which is hosted on the Microsoft Azure platform. Communication is based upon OPC, with the server hosted in the field and the cloud acting as a client.  Several APIs have access to the stored data, providing the end user with services such as access to an online monitoring system. Logged data include status and error text, temperatures, process parameters such as conveying capacity, environmental conditions and much more. Presentation of recorded information is handled by our proprietary WIDE dashboards.