Adaptive Sensor Data Compression for PLC Cloud Acquisition
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Solution Overview
Problem
Industrial automation systems face challenges in efficiently storing and transmitting large amounts of time-series data from programmable logic controllers (PLC) to cloud platforms like Siemens Sinalytics and GE Predix, as traditional compression methods result in either large data sizes or loss of original data quality.
Innovation Solution
A method involving a field device with a data compressor and decompressor using a representation to generate and restore sensor data, with a comparison unit detecting deviations and triggering cloud-based learning to adapt the compression algorithm, allowing for efficient compression and decompression while ensuring data quality by transmitting raw data when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If lossless compression is used to store sensor data, then data quality is preserved, but data size remains large and bandwidth consumption increases
Solution Approach 1:
The system applies partial lossy compression by selectively discarding only the minimal amount of data necessary to achieve acceptable compression ratios. The comparison unit validates that decompressed data remains within acceptable deviation thresholds, allowing the system to use less compression than traditional lossy methods while still achieving significant data size reduction compared to lossless compression.
Solution Approach 2:
The comparison unit provides feedback by comparing original sensor data with decompressed data and detecting deviations. This feedback loop allows the system to adjust compression parameters dynamically, ensuring data quality is maintained while optimizing compression ratios. When deviations exceed thresholds, the system can trigger alerts or adjust compression settings.
2Quantity of substance
If lossy compression is used to reduce data size, then bandwidth consumption decreases, but data quality and reconstruction accuracy deteriorate
Solution Approach 1:
The system applies partial lossy compression by selectively discarding only the minimal amount of data necessary to achieve acceptable compression ratios. The comparison unit validates that decompressed data remains within acceptable deviation thresholds, allowing the system to use less compression than traditional lossy methods while still achieving significant data size reduction compared to lossless compression.
Solution Approach 2:
The compression algorithm dynamically adjusts compression parameters based on data characteristics and required quality levels. By changing parameters such as compression ratio, threshold values, and algorithm selection, the system optimizes the balance between data size reduction and quality preservation for different sensor data types and operational conditions.
3Ease of manufacture
If traditional compression algorithms are used, then implementation is simple, but adaptability to changing data characteristics is poor
Solution Approach 1:
The system transitions from static compression algorithms to dynamic adaptive compression. The comparison unit continuously monitors data characteristics and compression effectiveness, allowing the system to adapt compression parameters and algorithms in real-time based on changing sensor data patterns, environmental conditions, and operational requirements.
Solution Approach 2:
The system performs self-optimization by automatically adjusting compression parameters based on feedback from the comparison unit. The cloud infrastructure can automatically learn optimal compression settings from aggregated data and push updates to field devices, enabling the system to self-adapt to changing conditions without manual reconfiguration.
4Adaptability or versatility
If cloud infrastructure learns new representations by analyzing all stored data, then compression algorithms are optimized, but computational resources and time are consumed
Solution Approach 1:
The cloud infrastructure performs preliminary analysis of data characteristics and pre-learns optimal compression representations during off-peak times or using historical data. This allows the system to have compression algorithms ready in advance, reducing the time required for real-time adaptation and minimizing the impact on operational performance.
Solution Approach 2:
The system applies partial learning by focusing computational resources on learning only the most critical or frequently occurring data patterns. Rather than continuously relearning all representations, the cloud infrastructure selectively updates compression algorithms based on significant changes in data characteristics, reducing overall learning time and resource consumption.
Data Source
AI summary
A sensor data are compressed on field devices using a representation is provided. The field device immediately decompresses the compressed data in order to detect a deviation. If there is a deviation, then a cloud storage receives the sensor data as raw uncompressed data. A cloud component receives a trigger signal from the field device, indicating that the representation used by the field device for compression does not sufficiently describe the sensor data. The cloud component then learns a new representation by retrieving and analyzing all data stored in the cloud storage. The method and field device provide robust, compression-based data acquisition. They improve quality and precision of the data captured by the field devices. As the representation in the field device can be updated, it becomes possible to accommodate changes in the device setup. The cloud infrastructure provides automatic learning of the representation in the cloud.