Slow-Rotating Component Failure Detection With IIoT Expert Monitoring

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Solution Overview

Problem

Industrial environments face challenges in data collection and utilization due to complex machines, variable operating conditions, and limited flexibility in sensing configurations, leading to inefficient monitoring and optimization of operations.

Innovation Solution

The implementation of a system that includes a data collector connected to multiple input channels, a data acquisition circuit, and an expert system analysis circuit to monitor and analyze data from rotating machine components, enabling real-time detection of failure states and anomalous conditions using tri-axial sensors and neural networks.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If traditional data collection methods are used in industrial environments, then data can be collected from machines, but the monitoring efficiency and operational optimization are insufficient due to complex machines and variable operating conditions

Engineering Contradiction:

Improvemonitoring efficiencyVSAvoidcomplexity of sensing configurations

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system employs multi-functional sensors that can detect multiple parameters (vibration, temperature, acoustic emissions) simultaneously, and the data collection device is designed to handle various sensor types and operating conditions through unified processing algorithms, thereby improving monitoring efficiency without proportionally increasing system complexity

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system dynamically adjusts detection parameters and sampling rates based on operating conditions and component speed, allowing efficient data collection across variable operating conditions while maintaining manageable system complexity through adaptive rather than static configurations

Inventive Principle:

Principle #35Parameter changes

2Reliability

If traditional batch data analysis methods are used, then data can be processed, but real-time detection of failure states and anomalous conditions is not achieved

Engineering Contradiction:

Improvedetection accuracy for failure statesVSAvoidtime delay in detecting failures

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs preliminary processing and analysis of data streams in real-time using embedded processing capabilities, preparing data for immediate failure detection without waiting for batch processing cycles, thereby reducing time loss while maintaining reliable detection accuracy

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements continuous feedback loops where detection results immediately trigger alerts or adjustments to monitoring parameters, enabling real-time response to failure states and anomalous conditions rather than delayed batch analysis

Inventive Principle:

Principle #23Feedback

3Loss of information

If comprehensive data collection from multiple sensors is implemented, then monitoring coverage is improved, but data processing complexity and computational requirements increase

Engineering Contradiction:

Improvecompleteness of machine state informationVSAvoidcomplexity of data processing system

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The system extracts and prioritizes only the most relevant features and parameters from multi-sensor data streams for further processing, discarding redundant information, thereby maintaining complete machine state information while reducing processing complexity through selective extraction of critical data elements

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The data processing system is segmented into modular components that handle different sensor types and analysis tasks independently, allowing comprehensive data collection to be processed through specialized modules rather than a monolithic complex system

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS20230403087A1Methods and systems for detection in an industrial internet of things data collection environment with expert systems to predict failures and system state for slow rotating components

Publication Date: 2023.12.14 STRONG FORCE IOT PORTFOLIO 2016 LLC

AI summary

Methods and systems for a monitoring system for data collection in an industrial environment including a data collector communicatively coupled to a plurality of input channels connected to data collection points related to machine components, wherein at least one of the plurality of input channels is connected to a data collection point on a rotating machine component; a data acquisition circuit structured to interpret a plurality of detection values from the collected data, each of the plurality of detection values corresponding to at least one of the plurality of input channels; and an expert system analysis circuit structured to analyze the collected data, wherein the expert system analysis circuit determines a failure state for the rotating machine component based on analysis of the plurality of detection values, wherein upon determining the failure state the expert system analysis circuit provides the failure state to a data storage.