Cover story
selection strongly depends on the application. A typical algorithm pipeline for model-based approaches that can be implemented on embedded platforms such as the iCOMOX are composed of three parts: (1) outlier detection, (2) prediction step and (3) filtering step; see Figure 4. During outlier detection, sensor data
far removed from the system condition’s estimate is either fractionally weighted or taken out completely in further processing, achieving more robust data processing. In the prediction step, the current
system condition is updated over time with a probabilistic system model that describes a prediction of the future system condition. This model is often derived from a deterministic system equation that describes the dependence of the future system condition on the current system condition, as well as other input parameters and disturbances. In condition monitoring of an industrial robot for example, this would be the dynamic equation for its individual arms, which only allow certain directions of motion at any point in time. In the filtering step, the predicted
system condition is then processed with a given measurement, and the condition estimate thereby updated. There is a measurement equation equivalent to the system equation that allows the relationship between the system condition and the measurement to be described in a formula. Combination of the data-driven and model-based approaches is both conceivable and advantageous for certain applications. The parameters of the underlying models for the model- based approaches can, for example, be determined through the data-driven approaches or dynamically adapted to the respective environmental condition. In addition, the system condition from the model-based approach can be part of a feature vector for the data- driven approaches. However, all of this strongly depends on the application.
Figure 5: Precise dynamic angle estimation on an embedded platform. The implemented algorithm shows much better performance when compared to the direct calculation and IIR filtering
The previously-mentioned
algorithm pipeline was implemented on the iCOMOX and evaluated for precise dynamic pose estimation in an industrial robot end effector. Accelerometer and gyroscope data with a sampling rate of 200Hz each were used as input data. The iCOMOX was attached to the end effector of the robot and its pose – consisting of position and orientation – determined; see Figure 5. As shown, the direct calculation leads to very fast reactions, but also to a lot of noise with many outliers. An IIR filter (commonly used in practice) leads to a very smooth signal, but it very poorly follows the true pose. In contrast, the algorithms presented here lead to a very smooth signal, where the estimated pose precisely and dynamically follows the motion of the robot’s end effector.
Self-reliance Ideally, through the corresponding local data analysis, the AI algorithms should also be able to decide themselves what sensors and algorithms are relevant for the application; i.e., this is smart scaleability of the platform. At present it is still the engineer who must find the best algorithm for each application, even though the AI algorithms can
already be scaled with minimal effort for various applications in machine- condition and structural-health monitoring.
The embedded AI should also
make a decision regarding the quality of the data and, if it is inadequate, find and make the optimal settings for the sensors and the entire signal processing chain. If several different sensor modalities are used for the fusion, the weaknesses and disadvantages of certain sensors and methods can be compensated for with an AI algorithm. Through this, data quality and system reliability are increased. If a sensor is classified as “not relevant” or “not very relevant” to the application by the AI algorithm, its data flow can be throttled. The open embedded platform iCOMOX contains a free software development kit and example projects for hardware and software to accelerate prototype creation, facilitate development and realise ideas. A robust and reliable wireless mesh network of smart sensors for condition-based monitoring can be created using multi-sensor data fusion and embedded AI. With it, big data is turned into smart data locally.
www.analog.com
www.electronicsworld.co.uk May 2022 09
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52
