Early wear detection and its significance for condition monitoring

Lu, Ping; Powrie, H.E.G.; Wood, R.J.K.; Harvey, Terry J.; Harris, N.R.

doi:10.1016/j.triboint.2021.106946

Cited by 41 publications

(16 citation statements)

References 129 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Guided by the operation and maintenance requirements of the whole life cycle system efficiency of wood production quality visual inspection, the system architecture of the wood defect image reconstruction and quality evaluation model based on deep reinforcement learning is divided into wood defect image perception submodule, wood defect image reconstruction [15]. Construct submodule, quality evaluation, intelligent decision-making submodule, humancomputer interaction submodule, etc.…”

Section: Model Architecture Of Wood Defect Imagementioning

confidence: 99%

A Wood Quality Defect Detection System Based on Deep Learning and Multicriterion Framework

Sun

2022

Journal of Sensors

View full text Add to dashboard Cite

In order to solve the problems of image perception and quality decision-making of wood defects with typical bionic intelligent algorithms, the existence of multidimensional degradation factors causes serious real problems of image distortion; the author proposes a wood defect image reconstruction and quality evaluation model based on deep reinforcement learning. The model introduced the deep learning mechanism and realized real-time and efficient reconstruction of multidimensional defect images of different wood by using the deep residual network for iterative training. In this model, a panoramic autonomous perception model was constructed for the fine segmentation and feature extraction of multidimensional defects of different wood and a shared resource pool of wood defect features of the magnitude of big data was constructed. Introduce the reinforcement learning mechanism, use the deep deterministic policy gradient algorithm, and establish a high-dimensional decision mapping between the iterative update of defect features, autonomous decision-making, panoramic visualization, depth prediction, and wood quality evaluation; it realizes the horizontal sharing integration of multidimensional difference wood defect image reconstruction and quality evaluation. The results obtained are as follows: in a typical environment, systematic wood quality evaluation, and autonomous intelligent decision-making performance, the coincidence rate with artificial defect recognition and evaluation efficiency can reach 90% and the loss of the training set can be controlled below 0.2%. Compared with the traditional wood quality grading system, the wood defect image reconstruction, and quality evaluation model system designed by the author, the quality evaluation decision-making efficiency rate was 90.19%, an increase of about 20%, and the system decision-making operation and maintenance loss was 2.23%, a decrease of about 10%. It is proved that the system designed by the author can realize the timely detection of wood quality defects very effectively and save a lot of manpower and material resources.

show abstract

Section: Model Architecture Of Wood Defect Imagementioning

confidence: 99%

A Wood Quality Defect Detection System Based on Deep Learning and Multicriterion Framework

Sun

2022

Journal of Sensors

View full text Add to dashboard Cite

show abstract

“…The wear of key components of the equipment is one of the main factors that cause the failure of mechanical equipment [1]. Most of the existing research mainly focuses on wear rate and surface topography to evaluate the wear on the macro level [2,3]. Between them, the surface topography re ects the wear state of the surface more intuitively.…”

Section: Objectivementioning

confidence: 99%

Multi-source sensor data and worn surface topography for tribo-informatics research

Yin

et al. 2022

Preprint

View full text Add to dashboard Cite

Real-time monitoring of the wear state of key components of mechanical equipment is one of the key engineering applications of tribology. It can be realized with the help of data-driven technology. In this study, the multiple-source sensor data and the surface topography of the disc specimen during the tribo-test were collected using the pin-on-disc test. The pin material was polyimide, and the disc material was titanium alloy. During the test, the surface topography of the disc specimen was collected by restarting multiple times for single pair of specimens. The data collected by the sensors of the tribometer itself include the applied normal load, the measured friction force, the calculated friction coefficient, the position of the pin, and the rotational speed of the disc. The data collected by the data acquisition platform include the vibration data in three directions, sound pressure, and audio data. The data collected by the white-light interferometer are the surface topography of the four worn areas on the disc. These data can be used to explore the trend of surface topography during the friction process and to verify the performance of different data-driven methods in studying the correlation between surface topography and multi-source sensor data.

show abstract

“…The wear progression on any system or material is classified into three stages (Lu et al, 2021). These are running in, steady state (stable) and end of life.…”

Section: Sensors and Wearmentioning

confidence: 99%

“…Thus, to achieve the better mechanical and tribological systems, we need to have the better sensors and methods to detect the wear. The wear progression on any system or material is classified into three stages (Lu et al , 2021). These are running in, steady state (stable) and end of life.…”

Section: Sensors and Tribologymentioning

confidence: 99%

Sensors and tribological systems: applications for industry 4.0

Rouf

Raina

Haq

et al. 2021

View full text Add to dashboard Cite

Purpose The involvement of wear, friction and lubrication in engineering systems and industrial applications makes it imperative to study the various aspects of tribology in relation with advanced technologies and concepts. The concept of Industry 4.0 and its implementation further faces a lot of barriers, particularly in developing economies. Real-time and reliable data is an important enabler for the implementation of the concept of Industry 4.0. For availability of reliable and real-time data about various tribological systems is crucial in applying the various concepts of Industry 4.0. This paper aims to attempt to highlight the role of sensors related to friction, wear and lubrication in implementing Industry 4.0 in various tribology-related industries and equipment. Design/methodology/approach A through literature review has been done to study the interrelationships between the availability of tribology-related data and implementation of Industry 4.0 are also discussed. Relevant and recent research papers from prominent databases have been included. A detailed overview about the various types of sensors used in generating tribological data is also presented. Some studies related to the application of machine learning and artificial intelligence (AI) are also included in the paper. A discussion on fault diagnosis and cyber physical systems in connection with tribology has also been included. Findings Industry 4.0 and tribology are interconnected through various means and the various pillars of Industry 4.0 such as big data, AI can effectively be implemented in various tribological systems. Data is an important parameter in the effective application of concepts of Industry 4.0 in the tribological environment. Sensors have a vital role to play in the implementation of Industry 4.0 in tribological systems. Determining the machine health, carrying out maintenance in off-shore and remote mechanical systems is possible by applying online-real-time data acquisition. Originality/value The paper tries to relate the pillars of Industry 4.0 with various aspects of tribology. The paper is a first of its kind wherein the interdisciplinary field of tribology has been linked with Industry 4.0. The paper also highlights the role of sensors in generating tribological data related to the critical parameters, such as wear rate, coefficient of friction, surface roughness which is critical in implementing the various pillars of Industry 4.0.

show abstract

Early wear detection and its significance for condition monitoring

Cited by 41 publications

References 129 publications

A Wood Quality Defect Detection System Based on Deep Learning and Multicriterion Framework

A Wood Quality Defect Detection System Based on Deep Learning and Multicriterion Framework

Multi-source sensor data and worn surface topography for tribo-informatics research

Sensors and tribological systems: applications for industry 4.0

Contact Info

Product

Resources

About