An Improved Highthroughput Imaging Sensor For Macroscale And Microscale Features Of Wear Particles In Online Condition Monitoring

• doi: 10.1109/JSEN.2024.3513643
• title: An Improved High-Throughput Imaging Sensor for Macroscale and Microscale Features of Wear Particles in Online Condition Monitoring
• publisher: IEEE
• isbn:
• issn: 2379-9153
• rank: 1505
• access_type: LOCKED
• content_type: Journals
• abstract: The online oil monitoring sensor is widely used in evaluating the health stage of various equipment through analyzing the macroscale (concentration) and microscale feature (size and shape) of wear debris. However, existing sensors face limitations in acquiring those multiple features effectively with high throughput (100 mL/min). In light of this challenge, an imaging sensor is developed to capture both the macroscale and microscale features, which employs a magnetic field to manipulate particle sampling within a fixed period. In the sampling processes, the wear particles were absorbed and dispersed in turn, which was recorded by two complementary metal-oxide-semiconductor (CMOS) cameras to capture images of the macro and micro features, respectively. The optimal parameters of sensor were calibrated by various oil throughput and magnetic currents (100 mL/min and 0.9 A), which showed wide and accurate capability for measurement of particle shapes and size distribution from 10 to $150~\mu $ m. Finally, this sensor was tested for a gearbox wear experiment with 4000 min at a high throughput of 100 mL/min. It effectively identified changes across three different health stages, which showed a promising online condition monitoring application with multifeatures of wear debris at high throughput.
• article_number: 10801188
• pdf_url: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10801188
• html_url: https://ieeexplore.ieee.org/document/10801188/
• abstract_url: https://ieeexplore.ieee.org/document/10801188/
• publication_title: IEEE Sensors Journal
• conference_location:
• conference_dates:
• publication_number: 7361
• is_number: 10869829
• publication_year: 2025
• publication_date: 1 Feb.1, 2025
• start_page: 5349
• end_page: 5360
• citing_paper_count: 0
• citing_patent_count: 0
• download_count: 134
• insert_date: 20241213
• index_terms:
  • ieee_terms:
    • Sensors
    • Monitoring
    • Magnetic sensors
    • Feature extraction
    • Oils
    • Magnetic resonance imaging
    • Imaging
    • Optical sensors
    • Optical imaging
    • Throughput
  • author_terms:
    • High throughput
    • imaging sensor
    • lubricating oil
    • online monitoring
    • wear debris analysis
  • dynamic_index_terms:
    • Image Sensor
    • Camera Sensor
    • Characteristics Of Particles
    • Wear Particles
    • Microscale Features
    • Microscale Characteristics
    • Macroscale Features
    • Macroscale Characteristics
    • Online Condition Monitoring
    • Particle Size
    • Size Distribution
    • Magnetic Field
    • High Throughput
    • Particle Size Distribution
    • Grain Size Distribution
    • Shape Features
    • Characteristic Shape
    • Sensors For Monitoring
    • Wear Debris
    • Spherical Particles
    • Flow Field
    • Dispersion Of Particles
    • Channel Flow
    • High Flow Rate
    • Magnetic Force
    • Lorentz Force
    • Oil Particles
    • Lubricating Oil
    • Magnetic Strength
    • Capacitive Sensor
    • Online Monitoring
    • Severe Wear
    • Wear Mechanism
    • Wear Conditions
    • Wear State
    • Wear Status
    • Oil Samples
• authors:
  • Author Name: Yang Fang
    Affiliation: School of Mechanical Engineering, Shandong University, Jinan, China
    Author URL: https://ieeexplore.ieee.org/author/731802904673931
    ID: 731802904673931
    Order: 1
    Author Affiliations:
    • School of Mechanical Engineering, Shandong University, Jinan, China
  • Author Name: Yanyan Nie
    Affiliation: Engineering Training Center, Shandong University, Jinan, China
    Author URL: https://ieeexplore.ieee.org/author/510814990897105
    ID: 510814990897105
    Order: 2
    Author Affiliations:
    • Engineering Training Center, Shandong University, Jinan, China
  • Author Name: Yu Du
    Affiliation: School of Mechanical Engineering, Shandong University, Jinan, China
    Author URL: https://ieeexplore.ieee.org/author/37086855909
    ID: 37086855909
    Order: 3
    Author Affiliations:
    • School of Mechanical Engineering, Shandong University, Jinan, China
  • Author Name: Jianping Yang
    Affiliation: School of Mechanical Engineering, Shandong University, Jinan, China
    Author URL: https://ieeexplore.ieee.org/author/848945098361233
    ID: 848945098361233
    Order: 4
    Author Affiliations:
    • School of Mechanical Engineering, Shandong University, Jinan, China
  • Author Name: Liming Wang
    Affiliation: School of Mechanical Engineering, Shandong University, Jinan, China
    Author URL: https://ieeexplore.ieee.org/author/37088398132
    ID: 37088398132
    Order: 5
    Author Affiliations:
    • School of Mechanical Engineering, Shandong University, Jinan, China
  • Author Name: Zhenguo Zhang
    Affiliation: State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, China
    Author URL: https://ieeexplore.ieee.org/author/37089599719
    ID: 37089599719
    Order: 6
    Author Affiliations:
    • State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, China

Image Sensor

• sensor_type: CMOS
• resolution: 2592 x 1944
• dynamic_range: Presumed to be in report but not specified in extracted portion.
• pixel_size: Presumed to be in report but not specified in extracted portion.
• dark_current: Presumed to be in report but not specified in extracted portion.

Optical Data

• focal_length: Presumed to be in report but not specified in extracted portion.
• aperture: Presumed to be in report but not specified in extracted portion.
• field_of_view: Presumed to be in report but not specified in extracted portion.
• distortion: Presumed to be in report but not specified in extracted portion.

Performance Metrics

• frame_rate: Presumed to be in report but not specified in extracted portion.
• signal_to_noise_ratio: Presumed to be in report but not specified in extracted portion.
• sensitivity: Presumed to be in report but not specified in extracted portion.
• shutter_speed: Presumed to be in report but not specified in extracted portion.
• power_consumption: Presumed to be in report but not specified in extracted portion.
• noise: Presumed to be in report but not specified in extracted portion.

Applications & Benefits

• cell_imaging: For monitoring wear debris in systems like gearboxes.
• benefits: Continuous real-time monitoring of wear particles, useful in condition monitoring.

Supporting Organizations

• supported_by: Shandong Provincial Natural Science Foundation, National Natural Science Foundation of China.

Manuscript Details

• publication_date: 1 Feb.1, 2025

Relevancy Score

• score: 10

Processed JSON Filename

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