By Philipp H. F. Wallner, industry manager at MathWorks
The industrial world is rapidly changing with the emergence of smart industry. Today’s production machines and handling equipment have become highly integrated mechatronic systems with a significant portion of embedded software. This fact requires engineers of all three mechatronic domains – mechanical engineering, electrical engineering and software engineering – to work together concurrently and evolve the way of designing, testing and verifying machine software to reach the expected level of functionality and quality.
The Role of Embedded Software
As smart industry evolves, software components provide a significant part of the entire added value of machine or production plants. Embedded software running on PLCs, industrial PCs, or FPGAs (field programming gate arrays) involves closed-loop control functionality that ensures product quality as well as predictive maintenance algorithms for increased uptime without service intervention. Furthermore, supervisory logic for — in many cases even safety critical — state machine and error handling and automatic generation of optimized movement trajectories are all implemented in embedded software.
The growing trend to increase the size and complexity of the code based on production machinery is a challenge for classically trained machine builders. Many are mechanically focused and need to maintain experience with elaborate workflows and toolchains for mechanical construction. When it comes to software design, machine builders rely on traditional methods for programming and testing on hardware – but are often unaware of tools for modeling, simulation, automatic testing and code generation, which are widely used by their engineering peers in aerospace and automotive industries.
While it may be obvious for serious mechanical engineers to use a CAD tool and run simulations before physically building the mechanical structure of a machine, in the case of embedded software, it is entirely different. A major portion of machine software is still programmed manually and comprehensively tested when the machine is available.
Data Proliferation ― Extracting Valuable Insight
Another major driver of smart industry is the growing amount of data. Vision sensors, electric and hydraulic drives, production machines and power plants all collect a growing amount of measured data during production operation. However, merely collecting data does not provide any value. It is the information inside the data that has to be extracted and analyzed in order to gain knowledge about product quality, energy consumption, machine health status and other economically relevant parameters.
This is where the use of analytical and statistical algorithms for condition monitoring and predictive maintenance is beneficial; they can be used to derive actionable insights from data that has been collected and stored in files, databases or in the cloud. This concept is taken one step further with model-based predictive maintenance, when an observer model is installed that is capable of deriving states of factors that cannot be measured directly.
The large amount of measured data needed is enabled by powerful sensor hardware, which execute complex algorithms often under harsh conditions and using minimal space. The sensor hardware often provides preprocessing and then forwards the results to the controller or to another data collection point. The sensors act together to form a dense network known today as the Industrial Internet of Things (IIoT).
The Use of Model-Based Design
Providing sophisticated sensor networks presents one of the essential prerequisites for realizing the efficiency, cost and, therefore, competitive advantages that smart industry promises. To become innovative leaders in their market, equipment manufacturers need to rapidly develop skills and expertise in these new design approaches and technologies.
As mechanical engineers typically are not experts in software engineering, they can increase their productivity and system reliability by using Model-Based Design tools like MATLAB and Simulink. These tools facilitate modular development of automation components, hardware independent testing, and automatic code generation, which can implement algorithms for specific hardware platforms at the touch of a button.
Models enable the intuitive and clear construction from predefined building blocks and continuous verification. With this approach, design flaws are corrected early on, which considerably shortens design cycles. Next, the algorithms need to be implemented, which can be considerably challenging using traditional methods. Historically, algorithms typically had to be developed by experts in IEC 61131-3, C/C++, VHDL or Verilog. This practice is not only time consuming, but is also prone to errors with the ever-increasing complexity of the algorithms used in machinery. Manually implemented functions that have already been verified through simulations potentially do not behave the way they were intended to, may contain errors, and therefore can cause missed deadlines and problems that are only noticed on-site.
In contrast, real-time functionality is directly generated from simulation models using automatic code generation; this avoids the aforementioned sources of errors. The tested algorithm is directly translated into real-time C, C++, VHDL or Verilog code. Doing so not only saves time but also enables the creation of innovative solutions in small development teams. Model-Based Design with automatic code generation enables engineers to fully leverage their expertise in construction to build a machine or plant without worrying about programming language details.
The Race to Smart Industry Realization
Keeping up with and being a leader in the worldwide smart industry requires companies to offer increasingly efficient and cost effective products, as well as keep an open mind to the new business opportunities that smart industry and the IIoT present. Today’s production equipment has a lifespan of more than 20 years. During this time, these systems are rarely modified in order to avoid production loss. Being able to design and test new software separately from the machine will enable companies to offer revenue-generating upgrades to their customers in order to expand the capabilities of the machine. For instance, the software upgrades could offer improved controls strategies not available on standard machines. Innovative machine builders have already started to offer predictive maintenance service contracts to their customers to reduce production line standstills.
Smart industry encompasses the growing complexity of software and an ever-increasing amount of data. In the long term, the evolving trend will challenge engineers to become proficient in using new methods and tools in order to embrace this complexity. For now, industrial companies who manage to shift their focus towards interdisciplinary design thinking (rather than production thinking) will emerge from the transformation as leaders in their areas and with new business models for their market. Those who do not will likely not make it through this transformation and risk being left behind.