Enhancing predictive maintenance (PdM) with hardware in the loop (HIL) systems

Predictive maintenance (PdM) (opens in new tab) has been a popular topic related to the future of maintenance and maintenance strategy (opens in new tab). One of the technologies driving advancements in predictive maintenance is hardware in the loop (HIL) testing. By simulating real-world conditions and integrating with actual hardware, HIL systems enable early detection of potential issues and help extend the lifespan of equipment. In this blog, we will explore HIL in detail, and understand how it’s transforming PdM strategies, and why it is essential for modern manufacturing.

Predictive maintenance at a glance

It’s important to understand PdM before we dive into how it works with HIL systems. Predictive maintenance (PdM) uses data analysis to identify operational anomalies and potential equipment defects, enabling timely repairs before failures occur (opens in new tab). This approach contrasts with traditional reactive maintenance, which addresses problems only after they occur, and preventive maintenance, which schedules regular maintenance regardless of the equipment’s actual condition.

What is hardware in the loop?

Hardware in the loop (HIL) testing is a real-time simulation technique used in developing and testing complex systems, particularly in fields such as automotive, aerospace, and industrial automation. HIL testing involves integrating actual hardware components with a virtual environment that simulates the rest of the system. For example, a digital twin (opens in new tab) can be used alongside HIL to review the performance of a specific system or asset. HIL allows for rigorous and controlled testing of hardware in conditions that closely mimic real-world scenarios without the need for full system deployment.

During HIL testing, the equipment is connected to hardware components like sensors, actuators, and controllers. Each hardware component is used to mimic the real-world operations that the system under test (SUT) would perform in actual use. Controllers run what are called control algorithms, which dictate how the system should behave based on inputs. Actuators perform physical actions based on the control signals they receive from the HIL simulator. Sensors attached to these actuators measure the physical response (position, speed, force) and send feedback signals back to the simulator. This feedback loop ensures that the system’s response can be monitored and adjusted in real-time. Overall, HIL testing is beneficial because it minimizes the need for multiple physical prototypes, which can become costly and time-consuming to build.

Example of HIL testing

Now that we’ve understood PdM and HIL, let’s examine an example of HIL testing in the automotive industry. In automotive, a vehicle’s electronic control unit (ECU) is critical to controlling various functions such as engine management, braking, and transmission. Before an ECU is deployed in a vehicle, it must undergo rigorous testing to ensure it operates correctly under various conditions. HIL testing provides a safe, cost-effective, and comprehensive method to validate the ECU’s performance.

Imagine testing the ECU’s response to a sudden loss of traction on an icy road. The HIL system would simulate the vehicle dynamics of skidding on ice, providing inputs such as rapid changes in wheel speed and lateral acceleration to the ECU. The ECU’s traction control algorithms would process these inputs and attempt to mitigate the skid by adjusting engine torque and applying brakes to individual wheels. The HIL system would monitor the ECU’s responses in real-time, providing valuable data on its effectiveness and identifying any issues that need to be addressed. The design below illustrates how the different components of HIL operate in regard to automotive and ECUs, and how and which component communicates information to one another:

The role of HIL in predictive maintenance

HIL systems enhance PdM by offering a controlled environment where various scenarios can be tested without risking actual production equipment. Here’s how HIL contributes to predictive maintenance:

1. Realistic simulation of operating conditions: HIL systems can simulate various operating conditions, including extreme scenarios that might be difficult or dangerous to replicate in a real-world setting. For example, engineers and maintenance personnel can observe how equipment responds to different stressors in a controlled environment and identify potential failure points.
2. Integration with sensor data: HIL systems can integrate with sensors and data acquisition systems used in industrial equipment. By correlating simulation data with real-time sensor data, HIL can help develop more accurate predictive models for various operational variables.
3. Accelerated testing cycles: HIL testing can accelerate the maintenance process. By simulating years of wear and tear in days or weeks, HIL enables the rapid identification of vulnerabilities and the validation of maintenance strategies without prolonged downtime.
4. Software and firmware validation: Predictive maintenance often relies on advanced algorithms and machine learning models that run on embedded systems within industrial equipment. HIL systems can be used to validate these software components under realistic conditions, ensuring their reliability and effectiveness in predicting failures.

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Benefits of HIL in predictive maintenance

HIL testing is critical in the development and validation of complex systems, particularly in industries such as automotive, aerospace, and industrial automation. Its importance stems from several key benefits that significantly enhance these systems’ reliability, safety, and efficiency. Below are the benefits of HIL in predictive maintenance:

• Early detection of failures: By identifying potential issues before they manifest as actual failures, HIL-enhanced predictive maintenance minimizes the risk of unexpected breakdowns, ensuring smoother operations.
• Cost savings: Early intervention prevents costly repairs and optimizes maintenance resources. This can lead to significant cost savings over time.
• Increased equipment lifespan: Regularly updating and refining PdM maintenance strategies using HIL data helps maintain equipment in optimal condition, extending its lifespan.
• Improved safety: Detecting and addressing potential failures early reduces the risk of accidents and improves overall workplace safety.

Additional benefits for PdM can be seen when HIL is integrated with software in the loop (SIL). When used together, they provide a comprehensive approach to ensuring software and hardware components function correctly and reliably. However, it’s important to understand that both HIL and SIL have different focuses.

Hardware in the loop (HIL) versus software in the loop (SIL)

Although HIL and software in the loop (SIL) are often interchangeable, both are different methodologies used in developing and validating complex systems. Additionally, they have very different focuses regarding maintenance, specifically PdM.

While HIL testing focuses on the integration and performance of hardware with real-world interactions, SIL testing emphasizes the verification and validation of software in a controlled virtual environment. Below is a table that details the differences between the two methods:

The role of HIL in relevant industries

HIL testing is relevant across multiple industries due to its ability to simulate real-world conditions, below are industry examples of how HIL testing is utilized:

1. Automotive industry: HIL testing is used to validate ECUs responsible for engine control, transmission, braking systems, and more. It’s also used for testing Advanced Driver Assistance Systems (ADAS) to ensure the reliability and safety of systems like adaptive cruise control, lane-keeping assist, and automatic emergency braking. Additionally, it’s used in testing powertrain components in electric vehicles (EVs) and validates components like battery management systems, motor controllers, and charging systems.
2. Aerospace and defense: HIL is used to validate flight control systems and ensures that avionics systems (like navigation for example) are performing correctly.
3. Robotics and industrial automation: In smart manufacturing, HIL testing on control systems and automation processes ensures efficient and accurate operation of assets and equipment. HIL testing also validates the performance of Internet of Things (IoT) devices and their interactions within smart manufacturing environments.
4. Renewable energy systems: HIL validates wind turbine controllers to ensure they can handle various wind conditions and optimize their energy production. HIL also tests the performance of solar panel control systems under different sunlight conditions and ensures testing with grid integration systems. Additionally, it tests all renewable energy sources that integrate with the power grid, maintaining stability and efficiency. Lastly, it validates energy storage systems and battery management to ensure they are functioning correctly.

Future considerations for HIL

As technologies advance within the maintenance and manufacturing sectors, innovations like HIL, real-time data analytics, machine learning, and the Internet of Things (IoT) will continue to enhance the capabilities of HIL systems, enabling even more precision predictions and interventions.