Self-Charging Sensor Node for Rotating Equipment Monitoring
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Solution Overview
Problem
Existing methods for monitoring rotating equipment, such as condition-based and predictive maintenance, rely on multiple sensors that require frequent battery replacements and are not optimal for early fault detection, leading to potential equipment failures and disruptions in production.
Innovation Solution
A self-charging sensor system that integrates vibration, acoustic emission, temperature, and magnetic flux sensors with an energy harvester, powered by a rechargeable battery, which communicates data to an application server for real-time fault diagnosis and remaining useful life prediction using neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensor nodes are deployed to monitor multiple parameters, then measurement precision is improved, but device complexity increases and sensor nodes interfere with each other
Solution Approach 1:
The patent combines multiple sensor types (vibration sensor, temperature sensor, acoustic sensor, magnetic flux sensor) into a single integrated sensor node. This merging approach allows the monitoring of multiple parameters simultaneously while reducing the overall number of sensor nodes required, thereby decreasing device complexity and eliminating interference between separate sensor nodes while maintaining comprehensive measurement precision
Solution Approach 2:
The sensor node is designed with multi-functionality, capable of detecting vibration, temperature, acoustic emissions, and magnetic flux simultaneously. This universal approach allows a single sensor node to perform the work of multiple specialized sensors, reducing system complexity while maintaining the ability to detect various fault conditions with high precision
2Ease of operation
If external batteries are used to power wireless sensors, then ease of operation is improved, but loss of energy occurs due to frequent battery replacements
Solution Approach 1:
The sensor node incorporates an energy harvester that automatically converts ambient vibrations into electrical energy to recharge its internal battery. This self-service mechanism eliminates the need for external battery replacements, reducing energy loss associated with frequent replacements while maintaining ease of operation during initial deployment
Solution Approach 2:
The system transitions from using external disposable batteries to an internal rechargeable battery powered by an energy harvester. This parameter change in the power source architecture transforms the energy supply from a consumable resource requiring replacement to a renewable resource that is continuously recharged from ambient vibrations, significantly reducing energy loss
3Reliability
If condition-based maintenance is used with real-time sensor measurements, then reliability is improved, but loss of time occurs due to immediate dispatch of maintenance workers
Solution Approach 1:
The system uses predictive maintenance with machine learning models to perform preliminary analysis of sensor data and predict future maintenance events before faults actually occur. This allows maintenance to be planned in advance rather than requiring immediate dispatch when parameters reach unacceptable levels, reducing unnecessary maintenance response time while maintaining high reliability through early fault detection
4Reliability
If predictive maintenance with machine learning models is implemented, then reliability is improved, but device complexity increases due to additional processing requirements
Solution Approach 1:
The system segments the processing tasks by performing initial data collection and preprocessing at the sensor node level, then transmitting only relevant features and data to the server for machine learning analysis. This segmentation reduces the processing complexity at each individual component while maintaining the overall reliability benefits of predictive maintenance through distributed intelligence
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables uninterrupted and precise monitoring of rotating equipment, reducing the likelihood of unexpected failures and allowing for proactive maintenance planning, thereby minimizing accidents and costs.
Implementation Method 1
The power source includes an energy harvester and a battery. The energy harvester is configured to recharge the battery based on vibrations detected by the vibration sensor.
Data Source
AI summary
A system for monitoring rotating equipment. The system includes a sensor device that acquires vibration data, acoustic emission data, temperature data, and magnetic flux data of the rotating equipment. The sensor device includes base, holding frame, first integrated circuit, housing, and power source. The first integrated circuit includes a plurality of sensors and a microcontroller configured to receive vibration data, acoustic emission data, temperature data, magnetic flux data from plurality of sensors and determine anomalies of the rotating equipment. The system further comprises an application server that receives vibration data and magnetic flux data, determines revolutions per minute (RPM) data for rotating equipment, and diagnose faults based on processed vibration data and RPM data. The application server further generates a set of features and corresponding feature values and analyzes them to diagnose faults, and predict remaining useful life of the rotating equipment.