Asset Data Platform Detecting Communication Abnormalities
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Solution Overview
Problem
Existing asset data platforms face challenges in efficiently handling data from non-communicative assets, leading to unnecessary resource expenditure and inaccurate predictions due to incomplete datasets and irregular data transmission.
Innovation Solution
An asset data platform is configured to detect communication abnormalities in assets, such as gaps, sparse, or delayed data transmissions, and designates non-communicative assets, suspending data analytics and isolating their data to preserve computing resources and ensure accurate predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the asset data platform continues to perform data analytics on non-communicative assets, then the analytics coverage is maintained, but computing resources are wasted and predictions become inaccurate
Solution Approach 1:
The system dynamically adjusts the data analytics process based on the communication status of assets. When an asset is identified as non-communicative (exhibiting gaps, sparsity, or delays in data transmission), the system automatically suspends analytics for that asset. This dynamic adaptation prevents waste of computing resources on assets that cannot provide sufficient data while maintaining analytics continuity for communicative assets.
Solution Approach 2:
The system implements a feedback mechanism that monitors data transmission patterns from assets and uses this information to control the analytics process. By detecting communication abnormalities and feeding this information back to the analytics engine, the system can make informed decisions about whether to continue or suspend analytics, thereby improving prediction accuracy and resource utilization.
2Reliability
If the asset data platform processes data from all assets uniformly, then the processing logic is simple, but the quality of analysis deteriorates due to incomplete datasets from non-communicative assets
Solution Approach 1:
The system applies different processing qualities to different assets based on their communication status. For communicative assets, full data analytics are performed with complete processing logic. For non-communicative assets, the system suspends analytics or applies reduced processing. This local differentiation of processing quality ensures high analysis quality for assets with complete data while avoiding the complexity of trying to handle incomplete data uniformly across all assets.
3Measurement precision
If the system monitors communication status for all assets, then non-communicative assets are identified accurately, but the system complexity increases
Solution Approach 1:
The system uses the data transmission patterns from assets themselves to identify non-communicative assets, rather than requiring separate monitoring infrastructure. By analyzing the inherent communication behavior (gaps, sparsity, delays) of each asset's data transmissions, the system achieves accurate identification of non-communicative assets using the existing data flow, thereby avoiding additional system complexity.
Data Source
AI summary
The example systems, methods, and devices disclosed herein generally relate to handling operating data from non-communicative assets. In some instances, a data-analytics platform receives operating data points from a given asset of a plurality of assets. Based on that data, the data-analytics platform detects a communication abnormality at the given asset, in accordance with one or more techniques disclosed herein. In response to detecting the communication abnormality, the data-analytics platform designates the given asset as being non-communicative. The data-analytics platform handles operating data points received from the given asset in accordance with the non-communicative designation.