Adaptive Sensor Monitoring for Precision-on-Demand Diagnostics

Solution Overview

Problem

Existing technical systems face challenges in monitoring their status effectively due to sporadic and low-precision sensor data collection, which limits the accuracy of predictive maintenance and resource allocation, while increasing data frequency and accuracy would consume more energy and bandwidth.

Innovation Solution

A computer system with an interface module, machine learning module, and command generator module that operates in low-precision mode until anomalies are detected, then switches to high-precision mode by adjusting sensor data frequency and accuracy, using additional sensors or reducing data preprocessing to enhance prediction accuracy.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If sensor data collection frequency and precision are increased to improve prediction accuracy, then measurement precision and reliability are improved, but energy consumption and data bandwidth usage increase

Engineering Contradiction:

Improvesensor data precisionVSAvoidenergy consumption

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The system dynamically adjusts the precision and sampling frequency of sensor data collection based on the detected technical status. When abnormal indicators are detected, the system automatically increases data collection precision and frequency to improve prediction accuracy, while maintaining lower precision during normal operation to conserve energy and bandwidth.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system changes the parameters of data collection (precision, sampling frequency) based on the technical status of the monitored system. A low-precision mode is used for normal operation, while a high-precision mode is activated when anomalies are detected, allowing the system to adapt measurement parameters to actual needs.

Inventive Principle:

Principle #35Parameter changes

2Measurement precision

If sensor data collection frequency and precision are increased to improve prediction accuracy, then measurement precision is improved, but data bandwidth usage increases

Engineering Contradiction:

Improvesensor data precisionVSAvoiddata bandwidth

Core Design Contradiction:

Measurement precisionVSQuantity of substance

Solution Approach 1:

The system dynamically adjusts the precision and sampling frequency of sensor data collection based on the detected technical status. When abnormal indicators are detected, the system automatically increases data collection precision and frequency to improve prediction accuracy, while maintaining lower precision during normal operation to conserve energy and bandwidth.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system changes the parameters of data collection (precision, sampling frequency) based on the technical status of the monitored system. A low-precision mode is used for normal operation, while a high-precision mode is activated when anomalies are detected, allowing the system to adapt measurement parameters to actual needs.

Inventive Principle:

Principle #35Parameter changes

3Loss of energy

If low-precision mode is used to reduce energy and bandwidth consumption, then resource efficiency is improved, but prediction accuracy deteriorates

Engineering Contradiction:

Improveenergy efficiencyVSAvoidprediction accuracy

Core Design Contradiction:

Loss of energyVSReliability

Solution Approach 1:

The system uses a machine learning module to continuously analyze sensor data and provide feedback on the technical status. When the feedback indicates abnormal conditions, the system responds by switching to high-precision data collection mode, ensuring prediction accuracy is maintained when needed while optimizing energy efficiency during normal operation.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system dynamically adjusts the precision and sampling frequency of sensor data collection based on the detected technical status. When abnormal indicators are detected, the system automatically increases data collection precision and frequency to improve prediction accuracy, while maintaining lower precision during normal operation to conserve energy and bandwidth.

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS11238371B2Computer system and method for monitoring the status of a technical system

Publication Date: 2022.02.01 ABB (SCHWEIZ) AG

AI summary

A computer system can be configured to: receive, in a low-precision mode, first status data generated by one or more sensors, the first status data reflecting technical parameters of a technical system, the first status data exhibiting a first precision level; apply a low-precision machine learning model to analyze the first status data for one or more indicators of an abnormal technical status, the machine learning model having been trained with data exhibiting the first precision level; send, based on an abnormal technical status being indicated, instructions for the one or more sensors to generate second status data exhibiting a second precision level, the second precision level being associated with greater accuracy than the first precision level; receive the second status data exhibiting the second precision level based on the sent instructions; providing the second status data to a data analyzer.