Today's product development and support cycle is fast. Embedded products detect software and hardware failures and provide insight into user behavior to provide engineers with the data needed to ensure equipment is operating properly and continually improving.
Not all industrial equipment can be easily connected to support these embedded products. Even products designed for the Internet of Things (IoT) experience connection problems such as electromagnetic interference (EMI), bandwidth restrictions, and long cables.
The advent of Bluetooth enabled System on Chip (SoC) technology provides engineers with seamless connectivity and microprocessor power for on-board machine learning (ML) support. Combining connectivity with intelligent analytics is an important tool in the design and support cycle from passive to proactive.
Intelligent data collection changes product development and support
Successful product development and support require the use of data. Designers who do not understand how the customer uses the product, including what they rely on, which features are cumbersome or have vulnerabilities, can find it difficult to iteratively upgrade the product to the level the user wants.g. Similarly, support personnel cannot adequately troubleshoot without knowing user behavior, system status, environmental conditions, and other critical data prior to or at the time of the problem.
Products with modern onboard connectivity and analysis capabilities can make design iterations and support more effective. Embedded products and intelligent beacons can detect environmental conditions such as temperature, humidity, and air pressure, and can sense multi-axis acceleration, ambient light, and magnetic fields. The timestamp of the Real Time Clock (RTC) allows data to be associated with other system events when using on-board analytics or when broadcasting to a cloud server via Bluetooth.
For example, a smart beacon connected to a linear motion system in an industrial environment may detect increased vibration as humidity increases. The onboard processor can then alert the maintenance engineer to the need for additional lubrication. This proactive fault diagnosis can reduce equipment downtime and maintenance costs.
Product designers can also use recorded vibration and environmental data to improve future versions of linear motion systems. For example, they may recommend a different lubricant that will last longer in wet conditions. They may also redesign the lubrication system to better protect it from external influences.
Implementing Challenges and Solutions
To realize the advantage of enhanced data collection in the IOT environment, engineers must optimize data collection and analysis. The transfer of any information to the cloud for analysis is inherently delayed and reduces data security. Embedded systems and intelligent beacons solve this problem by integrating AI and ML capabilities into the device itself. These Edge AI and TinyML systems contain scaled software models that allow the processor to intelligently extrapolate based on received real-world data.
Onboard ML functions can be simple to match vibration data, environmental data, and global time stamps, or complex to predict maintenance requirements based on data trends. Whether complex or simple, the ML module can receive and process real-time data without occupying network resources, thereby providing timely insight into changes and minimizing energy consumption.
Ultimately, however, smart beacons and embedded systems need to communicate status with other devices or servers over the network. Many traditional system designs have wired serial connections via protocols such as PROFIBUS, DeviceNet, CANOpen, and Modbus RTUs. More modern devices rely on low latency Ethernet protocols such as PROFINET, EtherCAT, EtherNet/IP, or Ethernet POWER. However, both serial and Ethernet communications require data and power cables to be laid in the plant shop, and the following challenges include EMI, signal attenuation during long cable transmission, and investment in facilities required to mitigate tripping hazards and provide access for driving or self-driving vehicles.
Short-range radio frequency (RF) communication using the Bluetooth protocol overcomes many of these challenges. Some versions of Bluetooth, such as Low-Power Bluetooth (BLE), utilize the power of a button battery to emit strong signals over a range of 150 meters, eliminating the need for power and data lines.
The BLE signal operates in the 2.4 GHz band, which also supports some cellular and Wi-Fi networks. While shared bands can result in network interference and reduced signal integrity, they are the most reliable bands to overcome sight barriers such as walls and equipment. To overcome LOS and interference problems, many BLE systems can employ meshed networks, using Internet Protocol Version 6 (IPv6) to interconnect BLE devices and connect them to the cloud (Figure 1). Strategic placement of Bluetooth hotspots also increases signal strength and integrity within the mesh network.