New does not necessarily mean better.
New technology for industrial automation and control systems is often sold with the promise of increased efficiency, advanced functionality, and superior performance. This may be true in some instances; however, is not necessarily the case when it comes to troubleshooting and maintenance. We sometimes loose sight of the fact that newer is not always synonymous with better and older technologies work just fine in modern factories. Where the adoption of cutting-edge technologies can lead to innovation and increases in productivity, they can also present unique challenges that unnecessarily complicate the troubleshooting processes.
Create separate levels of complexity
The introduction of new technology in control systems can involve complex software, intricate hardware configurations, and sophisticated network communications. This complexity can lead to scenarios where diagnosing and fixing problems becomes more challenging, requiring a higher level of expertise and understanding. For technicians and engineers, the learning curve can be steep, potentially leading to longer downtimes during problem resolution especially when accustomed to more traditional systems.
New technologies can also come with dependencies on specific vendors or proprietary solutions, limiting the availability of tools and information necessary for effective troubleshooting. On top of this, the push for interconnected, smart factory environments further complicates this scenario, introducing variables like cybersecurity threats and compatibility issues with legacy systems. Troubleshooting these systems can be demanding due to their complexity and the critical nature of the processes they interface with. The ability to quickly identify and resolve issues is even now more vital to minimize downtime and maintain productivity.
The Role of PLC in Industrial Automation
PLC systems are the backbone of modern industrial automation. They automate complex processes, control and monitor a vast array of processes thus ensuring precision, reliability, and efficiency. In the modern factory, the role of the PLC now extends from controlling basic machine operations to managing intricate details of manufacturing processes. PLC’s are more than just control devices; they are the cornerstone of smart factories and Industrial Internet of Things (IIoT), serving as a bridge between physical operations and data collection and analysis. This complexity makes it more difficult to create a PLC program that is easy to troubleshoot. Design teams need to understand the role troubleshooting plays in efficient maintenance and operation of industrial environments. Specific strategies to design programs that facilitate ease of troubleshooting for the maintenance team will go a long way in ensuring maximum machine uptime and throughput.
Challenges in Troubleshooting
Troubleshooting control systems is a critical skill, given their central role in manufacturing and assembly industries. Effective system repair can be hampered by several key reasons such as:
· Complexity of Systems: Modern industrial processes are highly complex, making it difficult to easily pinpoint issues. Constant technological advancements mean that technicians must continually update their knowledge.
· Interconnected Systems: Control systems are often part of a larger network, where faults can have cascading effects. Root cause analysis can be daunting requiring time consuming investigation and analysis.
Best Practices in Program Design
These challenges can be overcome, or certainly minimized, if effort is put into creating a program using good programming practices. This effort must begin at the design stage and continue throughout the duration of the project. Some suggestions fall into the following categories:
· Clarity and Organization
· Modular Design
· Simulation and Testing
· Diagnostic and Monitoring Tools
· Training and Skill Development
Clarity and Organization
· Structured Programming: Implement a structured approach, dividing the program into manageable sections or modules each handling a specific part of the process. This approach simplifies understanding the program’s flow, making it easier to locate and address issues.
· Naming Conventions: Adopt clear and consistent naming conventions for variables, routines, and functions. This aids in understanding the program’s flow and function. For instance, a variable controlling a conveyor belt might be named `ConveyorBeltSpeed` rather than a vague `Speed1` or `Var_a`.
· Commenting and Documentation: Comprehensive commenting within the code and thorough documentation, such as change control logs, are essential. Comments should explain the purpose of code sections, the reasoning behind complex functions, and any peculiarities in the code. A good repair technician can read the code, so avoid creating comments that explain the exact logic of the program but rather tell the “story” of what the program is trying to accomplish. Documentation provided should also provide an overview of the program structure and flow.
Modular Design
· System Design: As part of Structured Programming, design the system in a modular fashion, allowing for isolation and testing of individual modules. This approach is particularly effective in large systems where isolating problems can be challenging.
· Reusability: Utilize function blocks for common or repetitive tasks. This not only saves time but also makes the system more uniform, allowing for easier diagnostics. One function can also be made for different but similar tasks. Of course, there are limits on how modular you should attempt. Avoid falling into the trap of creating over-parametrized functions in the drive to create flexibility.
Simulation and Testing – Advanced Strategies for Troubleshooting
· Simulating Processes: Before deployment, use simulation tools to test the logic and functionality of the control program. This step can reveal issues that might be missed in a static review.
· Comprehensive Testing: Perform rigorous testing under various scenarios to ensure the program behaves as expected under different conditions. This includes testing for normal operation, abnormal operations, and failure modes.
Diagnostic and Monitoring Tools
· Built-In Diagnostics: Leverage PLCs’ built-in diagnostic functions and capabilities. These can provide real-time feedback on the status of inputs/outputs, module health, and network status.
· External Monitoring Tools: Use external software tools for monitoring and logging system operations. These tools can log operational data, providing a historical record that can be invaluable for diagnosing intermittent or complex issues. One underutilized tool is the contact histogram which can map the history of bit changes making root cause analysis easier.
· Error Handling Routines: Implement robust error handling routines that can detect, log, and, where possible, rectify errors autonomously. A well-designed diagnostic system can provide extremely useful real-time data.
· Alert Systems: Develop alert systems that notify technicians of issues promptly, specifying the nature and location of the problem. For example, a routine might detect a motor overload and shut down the system to prevent damage but, at the time same, notify the correct departments electronically of the failure.
Training and Skill Development – Importance of Training
· Best Practices in Industry: Examine real-world case studies where efficient PLC program design aided in quick troubleshooting and how different environments influence PLC programming strategies. These might include examples from manufacturing, process industries, or building automation.
· Technician Training: Emphasize the importance of training technicians not just in troubleshooting but also in understanding the logic and structure of the PLC programs. Continuous learning and staying updated with the latest PLC technologies and programming practices should be part of this strategy. Technicians with a deep understanding of the program structure and logic are better equipped to find and fix issues.
The Future of PLC Programming
It should be noted that the future of PLC programming is likely to be shaped by advances in AI and machine learning, which could offer even more sophisticated diagnostic and troubleshooting capabilities. The ability to collect and analyse large amounts of data can not only solve current problems but predict and prevent future issues.
