Laboratory Instrument Maintenance With Edge Anomaly Prediction
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Solution Overview
Problem
Existing laboratory maintenance methods face challenges with reactive and preventive maintenance, and limitations in predictive maintenance due to data volume and privacy concerns, leading to inefficiencies and increased costs.
Innovation Solution
A maintenance method for laboratory systems that combines local data collection and anomaly detection with centralized data analysis, using data collection components to gather operational data, detect anomalies, and transmit context data to a remote maintenance system for correlation and predictive rule determination, allowing timely and precise anomaly prediction and mitigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If predictive maintenance uses centralized data analysis with advanced analytics, then anomaly prediction accuracy is improved, but data transmission volume and privacy concerns increase
Solution Approach 1:
The system segments the centralized predictive maintenance system into distributed edge computing nodes deployed across multiple laboratories. Each edge node performs local data processing and anomaly detection, transmitting only essential results to the central server. This segmentation reduces data transmission volume while maintaining prediction accuracy through distributed advanced analytics capability.
Solution Approach 2:
The patent introduces an edge computing intermediary layer between local laboratory instruments and the central predictive maintenance server. This intermediary performs data preprocessing, feature extraction, and local anomaly detection, filtering and aggregating data before transmission to the central system, thereby reducing the volume of data that needs to be transmitted while preserving the information necessary for accurate anomaly prediction.
2Reliability
If all laboratories share data centrally for predictive maintenance, then prediction reliability is improved, but data privacy and security risks increase
Solution Approach 1:
The system implements segmentation by allowing each laboratory to maintain local data sovereignty while participating in the distributed predictive maintenance network. Edge computing nodes enable local processing of sensitive data without requiring its transmission to central servers, thus improving prediction reliability through aggregated insights while preserving data privacy and security at each location.
Solution Approach 2:
The patent employs copying by creating virtual replicas of the predictive maintenance analytics capability at each edge node. Instead of centralizing sensitive data, the system copies the analytical models and algorithms to distributed edge locations, where they process local data independently. This approach maintains prediction reliability through model consistency while eliminating the privacy risks associated with centralizing sensitive laboratory data.
3Device complexity
If predictive maintenance is implemented without edge computing, then system complexity is reduced, but implementation flexibility and scalability are limited
Solution Approach 1:
The system implements dynamics by designing a flexible hybrid architecture where edge computing capabilities can be dynamically deployed and scaled based on specific laboratory needs. The edge nodes can be added or removed independently, and the system adapts its data processing workflow to accommodate varying levels of edge computing capability across different locations, thereby enhancing implementation flexibility without significantly increasing overall system complexity.
Data Source
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AI summary
Disclosed herein is a maintenance method for a laboratory system comprising a first and second group of laboratory instruments for processing biological samples; data collection components connected to the groups of instruments and a remote maintenance system connected to the data collection components. The method comprising: collecting operational data from the laboratory instruments by the data collection components; detecting an anomaly related to the laboratory instruments by a first data collection component; transmitting context data to the remote maintenance system upon detection of an anomaly; determining correlation(s) between the operational data and the anomaly(s); validating the correlation(s); determining - at the remote maintenance system - predictive rules corresponding to validated correlations; transmitting the predictive rule(s) to the data collection components; and predicting occurrence of an anomaly of laboratory instruments based on the one or more predictive rule(s) by the data collection components.