The current product development and support cycle operates rapidly. Embedded products can detect software and hardware failures and gain insights into user behavior, providing engineers with the necessary data to ensure the normal operation and continuous improvement of devices.
But not all industrial equipment can be easily connected to support these embedded products. Even products designed specifically for the Internet of Things (IoT) may encounter connection issues such as electromagnetic interference (EMI), bandwidth limitations, and excessively long cables.
The emergence of Bluetooth enabled System on Chip (SoC) technology enables engineers to achieve seamless connectivity and powerful performance of microprocessors, enabling onboard machine learning (ML) support. Combining connectivity with intelligent analysis is an important tool in the design and support cycle of transitioning from passive response to proactive foresight.
Intelligent data collection has changed product development and support
Successful product development and support require the use of data. If designers do not understand how customers use the product, including which features they rely on, which features are cumbersome or have vulnerabilities, it is difficult to iterate and upgrade the product to the level that users want. Similarly, without understanding user behavior, system status, environmental conditions, and other critical data before or during the occurrence of a problem, support personnel cannot fully troubleshoot the issue.
Products with modern onboard connectivity and analytical 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 also sense multi axis acceleration, ambient light, and magnetic fields. By using real-time clock (RTC) timestamps, data can be associated with other system events when using onboard analysis functions or broadcasting to cloud servers via Bluetooth.
For example, smart beacons connected to linear motion systems in industrial environments may detect increased vibration when humidity rises. Then, the onboard processor can issue an alert to maintenance engineers, reminding them that additional lubrication is needed. 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 can be used for a longer period of time under humid conditions. They may also redesign the lubrication system to better protect it from external influences.
Implementing challenges and solutions
In order to achieve the advantages of enhanced data collection in the IoT environment, engineers must optimize data collection and analysis. Transmitting any information to the cloud for analysis incurs inherent latency and reduces data security. Embedded systems and smart beacons solve this problem by integrating AI and ML functions into the device itself. These edge AI and TinyML systems contain scaled down software models that allow processors to make intelligent inferences based on real-world data received.
The onboard ML function can be as simple as matching vibration data, environmental data, and global timestamps, or as complex as predicting maintenance needs based on data trends. Whether complex or simple, ML modules can receive and process real-time data without consuming network resources, enabling timely insights into various changes and minimizing energy consumption.
However, smart beacons and embedded systems ultimately need to communicate status with other devices or servers through a network. Many traditional system designs use protocols such as PROFIBUS, DeviceNet, CANOpen, and Modbus RTU for wired serial connections. More modern devices rely on low latency Ethernet protocols such as PROFINET, EtherCAT, EtherNet/IP, or Ethernet POWERLINK. However, both serial and Ethernet communication require laying data and power cables in the factory workshop, and the accompanying challenges include electromagnetic interference, signal attenuation during long cable transmission, and facility investment required to mitigate tripping hazards and provide access for driving or autonomous vehicles.
Short range radio frequency (RF) communication using Bluetooth protocol overcomes many of the challenges mentioned above. Some versions of Bluetooth, such as Low Energy Bluetooth (BLE), can use the power of button batteries to emit strong signals within a range of 150 meters, thus eliminating the need for power and data cables.
BLE signals operate on the 2.4 GHz frequency band, which also supports some cellular and Wi Fi networks. Although sharing frequency bands may lead to network interference and reduced signal integrity, it is also the most reliable frequency band to overcome line of sight obstacles such as walls and devices. In order to overcome the problem of line of sight and interference, many BLE systems can use mesh networks, and use the 6th Internet Protocol (IPv6) to connect BLE devices to each other and to the cloud (Figure 1). Strategically placing Bluetooth hotspots can also enhance signal strength and integrity within mesh networks.