Cloud Data Processing for Industrial KPI Analysis
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Solution Overview
Problem
Current systems face challenges in efficiently analyzing large volumes of data from industrial machines and manufacturing lines, leading to slow processing times and ineffective analysis of key performance indicators.
Innovation Solution
A cloud-based data processing apparatus with multiple data nodes and an aggregation circuit that performs parallel data slicing and processing, enabling quick and efficient analysis of vast data sets to determine key performance indicators across multiple plants and machines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data processing methods are used to analyze large volumes of data from industrial machines, then data accuracy is maintained, but processing time increases significantly
Solution Approach 1:
The patent segments the large volume of industrial data into smaller manageable chunks or partitions, allowing parallel processing across multiple processing units. This segmentation enables the system to maintain data accuracy while significantly reducing overall processing time by dividing the analytical workload into concurrent operations.
Solution Approach 2:
The system performs preliminary data preprocessing, filtering, and organization before the main analysis phase. By preparing data in advance through aggregation circuits and preliminary processing stages, the system reduces the complexity of subsequent analysis, maintaining accuracy while minimizing the time required for key performance indicator determination.
2Adaptability or versatility
If data from multiple plants and machines is aggregated for comprehensive analysis, then the scope of analysis is improved, but system complexity increases
Solution Approach 1:
The patent implements a segmented architecture where each plant or machine has its own data collection and preliminary processing units, which then feed into aggregation circuits. This modular segmentation allows the system to handle data from multiple sources simultaneously, expanding analysis scope while managing complexity through standardized interfaces and hierarchical organization.
Solution Approach 2:
The system employs universal data processing circuits and standardized protocols that can handle diverse data types from different machines and plants. The aggregation circuits are designed to process various data formats and sources through a common framework, enabling comprehensive multi-plant analysis without proportionally increasing system complexity.
Data Source
AI summary
A data processing apparatus is deployed at a cloud network, and includes a plurality of data nodes, a receiver circuit, and an aggregation circuit. The receiver circuit is coupled to the plurality of data nodes and is configured to receive downtime records from a plurality of industrial machines, and to selectively route the downtime records to selected ones of the plurality of data nodes based upon a predetermined criteria. Each of the plurality of data nodes comprises a control circuit and a memory. Each control circuit is configured to, in parallel with the other control circuits, to further populate the downtime records with other data related to the operation of the machines, determine one or more time windows for each of the records, and divide the records according to the time windows.