Online Wear Particle Detection Sensors for Wear Monitoring of Mechanical Equipment—A Review

Jia, Ran; Wang, Liyong; Zheng, Changsong; Chen, Tao

doi:10.1109/jsen.2021.3131467

Cited by 36 publications

(15 citation statements)

References 134 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Craig [ 12 ] improved the accuracy of bearing damage diagnosis by combining the online oil debris monitoring method with the offline detection method. The development of high-precision oil debris sensors is also an important prerequisite for improving the accuracy of bearing diagnosis [ 13 , 14 ]. Liu [ 15 ] proposed an online optical oil debris sensor.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis for Abnormal Wear of Rolling Element Bearing Fusing Oil Debris Monitoring

Zhao

Wang

Han

et al. 2023

Sensors

View full text Add to dashboard Cite

The abnormal wear of a rolling element bearing caused by early failures, such as pitting and spalling, will deteriorate the running state and reduce the life. This paper demonstrates the importance of oil debris monitoring and its effective feature extraction for bearing health assessment. In this paper, a rolling bearing-rotor test rig with forced lubrication is set up and the nonferrous contaminants with higher hardness were introduced artificially to accelerate the occurrence of pitting and spalling. The early failure and abnormal wear of rolling bearings cannot be effectively detected only through the vibration signal; the temperature and oil debris monitoring data are also collected synchronously. Two features regarding the ferrous particle size distribution are extracted and fused with vibration based-features to form a feature set. The sensitive features are extracted from the features set using the Neighborhood Component Analysis method to avoid feature redundancy. Finally, the importance of the oil debris based-features for the diagnosis of abnormal bearing wear is analyzed with different machine learning algorithms. Taking SVM classifier as an example, the experiment results show that the introduction of oil debris based-features increases the diagnostic accuracy by 15.7%.

show abstract

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis for Abnormal Wear of Rolling Element Bearing Fusing Oil Debris Monitoring

Zhao

Wang

Han

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…These sensors provide data and insights into wear patterns, allowing for data-driven decision-making regarding maintenance schedules, component replacements, and system upgrades [34]. Timely detection and maintenance can prolong the lifespan of ship equipment, reducing the need for costly replacements and improving the overall efficiency of the vessel [35,36]. In summary, on-line oil wear debris detection sensors are instrumental in marine engineering for their ability to enhance safety, reduce operational costs, prolong equipment lifespan, and support environmentally responsible practices by efficiently monitoring and managing wear in ship equipment [37,38].…”

Section: Introductionmentioning

confidence: 99%

A Critical Review of On-Line Oil Wear Debris Particle Detection Sensors

Han,

Mu,

Liu

et al. 2023

JMSE

View full text Add to dashboard Cite

In the field of marine engineering, the friction and wear experienced by rotating mechanisms are recognized as significant contributors to the failure of marine machinery. In order to enhance the safety and dependability of marine ship operations, the implementation of on-line oil wear debris particle detection sensors enables the on-line monitoring of oil and facilitates the rapid identification of abnormal wear locations. This paper provides a critical review of the recent research progress and development trends in the field of sensors for on-line detection of oil wear debris particles. According to the method of sensor detection, wear debris particle detection sensors can be classified into two distinct categories: electrical and non-electrical sensors. Electrical sensors encompass a range of types, including inductive, capacitive, and resistive sensors. Non-electrical sensors encompass a range of technologies, such as image processing sensors, optical sensors, and ultrasonic sensors. Finally, this review addresses the future research directions for wear debris particle detection sensors in light of the challenging problems currently faced by these sensors.

show abstract

“…However, vessels generally sail offshore for months before they can be brought back to dock for laboratory tests of oil. At present, for oil cleanliness testing studies, a laboratory delivery method is often used, collecting lubricants from operating equipment and sending them to a laboratory for oil quality analysis through methods such as iron spectrum analysis [ 6 , 7 ], ultrasonic testing, and optical testing [ 8 , 9 ]. Results of the testing arrive after a delay and the method cannot monitor equipment status in real-time.…”

Section: Introductionmentioning

confidence: 99%

Micro-Three-Coil Sensor with Dual Excitation Signals Use Asymmetric Magnetic Fields to Distinguish between Non-Ferrous Metals

Hong

Xie

Zhang

et al. 2023

Sensors

View full text Add to dashboard Cite

Intelligent operation and maintenance technology for vessels can ensure the safety of the entire system, especially for the development of intelligent and unmanned marine technology. The material properties of metal abrasive particles in oil could demonstrate the wear areas of the marine mechanical system because different components consist of different materials. However, most sensors can only roughly separate metallic contaminants into ferromagnetic and non-ferromagnetic particles but cannot differentiate them in greater detail. A micro-three-coil sensor is designed in this paper; the device applies different excitation signals to two excitation coils to differentiate materials, based on the different effects of different material particles in the asymmetric magnetic field. Therefore, a particle’s material can be judged by the shape of the induction electromotive force output signal from the induction coil, while the particle size can be judged by the amplitude of the signal. Experimental results show that the material differentiation of four different types of particles can be achieved, namely, of aluminum, iron, 304 stainless steel, and carbon steel. This newly designed sensor provides a new research prospect for the realization of an inductive detection method to distinguish non-ferrous metals and a reference for the subsequent detection of metal contaminants in oil and other liquids.

show abstract

Online Wear Particle Detection Sensors for Wear Monitoring of Mechanical Equipment—A Review

Cited by 36 publications

References 134 publications

Fault Diagnosis for Abnormal Wear of Rolling Element Bearing Fusing Oil Debris Monitoring

Fault Diagnosis for Abnormal Wear of Rolling Element Bearing Fusing Oil Debris Monitoring

A Critical Review of On-Line Oil Wear Debris Particle Detection Sensors

Micro-Three-Coil Sensor with Dual Excitation Signals Use Asymmetric Magnetic Fields to Distinguish between Non-Ferrous Metals

Contact Info

Product

Resources

About