“The vision of Industry 4.0 is to combine smart sensors with industrial equipment to increase productivity, increase reliability and reduce operating costs. Many of these sensors will be connected to each other wirelessly (for example, through a mesh configuration) or through a sensor gateway. The network will then connect to the cloud where the data can be collected, analyzed and processed. The Industrial Internet of Things (IIoT) differs from IoT in that devices must perform operations in real-time and meet industrial reliability standards.
The vision of Industry 4.0 is to combine smart sensors with industrial equipment to increase productivity, increase reliability and reduce operating costs. Many of these sensors will be connected to each other wirelessly (for example, through a mesh configuration) or through a sensor gateway. The network will then connect to the cloud where the data can be collected, analyzed and processed. The Industrial Internet of Things (IIoT) differs from IoT in that devices must perform operations in real-time and meet industrial reliability standards.
Industrial robots are a special class of industrial equipment that perform manual tasks efficiently, accurately, and repeatably. They have been used in factories and commercial facilities since the 1960s. With the advent of Industry 4.0, these robots are incorporating intelligence and new capabilities to make smart factories possible.
In addition to being more flexible in performing tasks, industrial robots are also able to collect and analyze data about themselves to improve productivity, service quality, and reliability, while reducing total total cost of ownership. When connected to the cloud, the operating patterns and trends of all robots can be identified.
For example, failures can be analyzed to create predictive maintenance algorithms that trigger alerts when there is an error in a robot’s operational configuration record file. Minimize losses by enabling problems to be resolved before they cause equipment failure and downtime.
The growth of industrial robots continues as more and more industrial automation OEMs invest in robotics. The robotics industry has grown at an annual rate of 7.6% (CAGR) since 2016.
1. OEMs continue to invest in industrial robotics, and the robotics industry has grown at a rate of 7.6% annually (CAGR) since 2016, even taking into account COVID-19.
In the coming years, developers of industrial robots will face a series of problems. As these systems become more complex, reliability becomes increasingly important. This article will explore the challenges of collecting operational data from industrial robots in the form of data logs, including how to handle the data that must be collected and how to minimize data loss during system failures.
The complexity of industrial robots
In its most basic form, an industrial robot consists of a manipulator and a controller. A manipulator, often referred to as a robotic arm, can move, rotate, and perform actions.
2. Block diagram of an industrial robot.
The parts of each robotic manipulator are connected by mechanical joints, each of which provides an axis of motion. A typical manipulator has six movable joints or six axes of motion.
Each axis handled by a high-precision servo or stepper motor is limited to a specific range of motion. Also, each axis moves at a different speed, usually listed in the datasheet in degrees moved per second. The greater the range of motion and the faster the joint’s top speed, the greater the precision required to control the motion. The need for greater coordination and precision also increases the amount of operational data that needs to be recorded from each sensor tracking the robot.
From a reliability standpoint, industrial robots must be able to recover from various power events, such as power outages. Ideally, once the power failure is removed, the robot can resume operation immediately from where it left off, even if the system has been reset.
To do this, each motor must be able to save key parameters and data states, including the rotation angle and position of the robotic arm. Likewise, the controller needs to maintain a detailed control log, recording the operating parameters of each axis, including its commanded position, encoder value, and payload.
Additionally, the controller must keep the servo motor records tracking speed, torque, motor feedback sensing (i.e. current, position, speed) and angle of motion. Reliably recording all this data requires some form of non-volatile memory so that the data is not lost in the event of a power outage.
Non-volatile memory for data logging
For decades, critical data has been kept in battery-backed SRAM. However, this approach has many disadvantages:
Requires several components (battery, power management controller), takes up more PCB space and increases the number of failure points.
To avoid heating the battery, it is often necessary to mount the battery after the reflow process, increasing the manufacturing cost.
Industrial robots are often exposed to vibrations, which can cause mechanical failures in the connectors that hold batteries in place, reducing overall reliability.
The battery requires maintenance and replacement.
Batteries are not RoHS compliant, creating disposal issues for manufacturers.
For these reasons and others, OEMs have turned to non-volatile memory devices to replace battery-backed SRAM. This table shows a range of nonvolatile memory technologies available to OEMs.
Due to their low endurance, EEPROM can be excluded from most applications. Industrial robots operate 24/7 and must record large amounts of real-time data. Since these robots may run continuously for years, the EEPROM will eventually wear out, so it’s not a viable option.
Flash’s battery life is also limited. However, flash endurance issues can often be resolved using wear-leveling software techniques on the host processor. When a block begins to experience errors that exceed a set threshold, the wear leveling algorithm moves data to a block that performs reliably.
Wear leveling effectively extends the lifespan of memory by distributing wear evenly across the flash memory. However, the process of tracking and moving data throughout memory increases host CPU load and introduces latency for write operations.
Perhaps the most important consideration when using flash memory for data logging is that it writes data in blocks. Log data must be collected in buffers until the entire block is ready to be written. A wear leveling algorithm might involve doing a software-based lookup in a large table and then selecting the blocks where the data should be written. Finally, flash memory must be erased before blocks can be written.
Only after these tasks are completed can log data be finally written. All of these factors lead to long delays directly between actual data acquisition and writing.
As mentioned earlier, the two main reasons for data logging are performance analysis over time and power event recovery. For both functions, arguably the most important information is the data collected in the event of a failure.
In the event of a power failure, the data will be used to recover and restore the exact location where the industrial robot stopped functioning. For performance analysis, this “last minute” data is critical to understanding what happened before the failure and what might have caused the failure.
When a system fails or a power issue occurs, there is little time to react. With flash and EEPROM, anything in the buffer will be lost. However, this is the most important data. The longer it takes to write to memory, the greater the risk of losing critical data. Consider a high-precision robot operating on expensive parts. If the robot experiences a power failure, the system needs to be able to reset to the interrupted position with high accuracy. Otherwise, the parts processed this time may be scrapped.
To maintain operating parameters and data logs with high reliability, data must be continuously captured and stored in non-volatile memory. For this reason, robotics developers are turning to ferroelectric random access memory (F-RAM). As can be seen from the table, F-RAM has many advantages that make it the first choice for storing critical operating parameters and data records.
F-RAM has an endurance of 10 to the power of 14 write cycles, and has unlimited endurance for data logging applications. In addition, F-RAM does not require wear leveling, which simplifies and reduces the latency of writing to memory.
F-RAM is a random access memory that does not require refresh cycles. There is no need to buffer blocks of data as the data can be immediately stored in non-volatile memory. In addition, the random access nature of F-RAM eliminates the latency introduced by memory paging. When data is captured, it is stored immediately.
Data Recording Market Trends
The developer must decide whether to record data centrally within the main controller or at the edge of each motor. Today, data logging on the edge side of the motor requires up to 1 Mb, while the controller requires up to 16 Mb.
For high-speed applications such as six-axis robot controllers, Infineon’s latest generation of non-volatile memory, Excelon F-RAM, offers higher density memory and a four-channel SPI interface to help increase throughput. For applications with smaller data logging requirements, there are products with lower density single-channel SPI.
However, as the number of axes and sensors in industrial robots continues to grow, the requirements for data logging will only expand (Figure 3). At the same time, AI-based performance and predictive maintenance algorithms will require access to a wider range of parameters with greater granularity, increasing the total amount of data that must be collected and stored.
3. With the increasing number of axes and sensors in industrial robots, data logging requirements will grow over time.
Another trend affecting non-volatile memory density is moving recording functions closer to the edge of the network. High-reliability and functionally safe edge computing and storage in each motor eliminates delays in sending data back to the main controller.
Many manufacturers use microcontrollers on each motor whose actions are coordinated by the main six-axis controller. Therefore, each motor tracks its own parameters and sensors. This, in turn, will enable the transfer of more advanced artificial intelligence and machine learning (ML) capabilities to the edge as well as to individual motors.
Additional memory in industrial robots
In addition to data logging memory, industrial robots employ many other memory technologies in the system, including storing boot code as extended memory. With the advent of Industry 4.0, the need to protect systems from cyber threats has exploded.
One of the main targets of hackers is flash memory devices, which store boot codes, security keys, and other critical data that are critical to the proper functioning of the system. In this regard, Infineon has developed SEMPER Secure NOR flash memory that complies with functional safety standards and integrates security features to protect the code from hackers.
The increasing complexity of robot controllers has resulted in many robot controllers also having their own TFT displays to support direct interaction with technicians as well as remote control. For buffering data, audio, images, and video, or as scratchpad for math and data-intensive operations, HyperRAM is ideal as an expansion memory for industrial displays. It has transfer rates of up to 800MB/s over a low pin count serial HyperBus interface.
Data logging is an essential function of industrial robots and can recover from faults and power events without negatively impacting production. Data logging also plays an important role in enabling emerging AI and ML capabilities, such as predictive maintenance, by providing the data that will drive innovation in these applications.
The infinite endurance of F-RAM combined with its real-time, non-volatility, high throughput, and reliable data capture makes it a strong choice for non-volatile memory for high-performance data logging in industrial robots. Since F-RAM guarantees minimal data loss during a power failure, high-precision recovery can be performed, and the robot can continue to operate where it left off before a reset or failure.
F-RAM is available in low-density and high-density options to meet the requirements of different applications. It also gives developers the flexibility to meet the changing needs of next-generation robotics as AI and ML capabilities move closer to the edge.
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