Cover story
Turning big data to smart data locally with the open embedded platform iCOMOX
By Dzianis Lukashevich, Director of Platforms and Solutions, Analog Devices For the individual processing steps,
A
t the heart of Industry 4.0 is the premise to collect enormous volumes of data from sensors, actuators and other devices,
described as “big data”. This requires a detailed virtual view of machines, systems and processes, to be able to generate valuable and actionable information throughout the entire value chain. With data-processing systems and architectures becoming more complex, and with the number of data-generating devices constantly increasing, the question is how to best extract the most relevant, high-quality and useful information, or “smart data”. Collecting data and storing it in the
cloud for future analysis and use is the prevalent school of thought, but not a particularly effective one. Most data remains unused, and finding a solution later on becomes more complex and costly. The best way is to make conceptual considerations early on, to determine the most application-relevant information and where in the data flow to extract it from; see Figure 1. In effect, this means refining the data, to make smart data out of big data for the entire processing chain. At application level, a decision can
already be made regarding which AI algorithms have a high probability of success for the individual processing steps. This depends on boundary conditions such as available data, application type, available sensor modalities and background information about the lower-level physical processes.
06 May 2022
www.electronicsworld.co.uk
correct handling and interpretation of the data are extremely important, which largely depend on the application and the relevance and accuracy of the sensors’ data. Many parameters have a direct effect on the desired information, such as temporal behaviour, sensor multi-dependencies, and more. For complex tasks, simple threshold values and manually-determined logic are no longer sufficient or do not allow automated adaptation to changing environmental conditions. The overall data-processing chain with all the algorithms needed in each individual step must be implemented at all levels to generate best value; in most cases it is more advantageous to implement them next to the sensor. Through this, data is compressed and refined early on, and communication and storage costs are reduced. In addition, by early extraction of essential information from the data, the development of global algorithms at the higher levels becomes less complex. In most cases, algorithms from the streaming analytics area are also useful for avoiding unnecessary storage of data and, thus, high data transfer and storage costs. These algorithms use data points only once; for example, the complete information is extracted directly, without having to store it.
Embedded platform for condition-based monitoring The ARM Cortex-M4F processor-based open embedded platform iCOMOX from Shiratech Solutions, Arrow and Analog
Devices integrates power management with sensors and peripheral devices for data acquisition, processing, control and connectivity. The solution is perfect for local data processing and its early refinement with AI algorithms. iCOMOX stands for intelligent condition monitoring box (Figure 2), easily applied to the industrial world of structural-health and machine-condition monitoring based on vibration, magnetic fields, sound and temperature analysis. The platform can be customised with additional sensor capabilites, such as gyroscopes. The iCOMOX’s AI methods enable a better estimate of machine status through multi-sensor data fusion, classifying operating and fault conditions with greater granularity and higher probability. For wireless communication, the iCOMOX provides a solution with high reliability and robustness as well as extremely low power consumption. The SmartMesh IP network is made of a highly scaleable, self-forming/optimising multi-hop mesh of wireless nodes that collect and relay data. A network manager monitors and manages the network performance and security and exchanges data with the host application. The intelligent routing of the SmartMesh IP network determines an optimum path for each individual packet in consideration of the connection quality, the schedule for each packet transaction and the number of multi- hops in the communications link. Especially for wireless battery-
operated condition-monitoring systems, embedded AI can help extract the full
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