Evaluation of a non-contact method for measuring vibration of rolling bearings in industrial conditions

Adamczak, S.; Wrzochal, Mateusz; Durakbasa, Numan M.

doi:10.17814/mechanik.2020.2.5

Cited by 2 publications

(5 citation statements)

References 11 publications

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“…By improving this prediction quality, it is possible to find (quasi-)optimal solutions to manufacturing processes, enabling an improvement in material and energy efficiency [58]. Adamczak et al [59] examined a regression model that analyses data from a ball bearing sensor. Minimization of vibrations can reduce material wear in the long term, as well as energy and, thus, CO 2 emissions, through lower friction [59].…”

Section: Resultsmentioning

confidence: 99%

“…Adamczak et al [59] examined a regression model that analyses data from a ball bearing sensor. Minimization of vibrations can reduce material wear in the long term, as well as energy and, thus, CO 2 emissions, through lower friction [59]. Kuhlmann and Sauer [60] developed a model with linear regression to evaluate energy measures and increase the energetic agility within a manufacturing plant.…”

Section: Resultsmentioning

confidence: 99%

“…-Predictive Maintenance [57,59,69,70,80,83,84,86,95,113]; -Production planning [52,54,61,65,72,77,78,101,102]; -Fault detection and prediction/predictive quality [58,62,74,82,87,89,93,94,110,111,115,118]; -Increasing energy efficiency in production [56,63,85,99,100,103,108,114,119] and facility management [53,67,76,107,109].…”

Section: Identification Of Typical Use Cases Of Ai Application Increasing Resource Efficiencymentioning

confidence: 99%

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Artificial Intelligence Applications for Increasing Resource Efficiency in Manufacturing Companies—A Comprehensive Review

et al. 2021

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Sustainability improvements in industrial production are essential for tackling climate change and the resulting ecological crisis. In this context, resource efficiency can directly lead to significant advancements in the ecological performance of manufacturing companies. The application of Artificial Intelligence (AI) also plays an increasingly important role. However, the potential influence of AI applications on resource efficiency has not been investigated. Against this background, this article provides an overview of thecurrent AI applications and how they affect resource efficiency. In line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this paper identifies, categorizes, and analyzes seventy papers with a focus on AI tasks, AI methods, business units, and their influence on resource efficiency. Only a minority of papers was found to address resource efficiency as an explicit objective. Subsequently, typical use cases of the identified AI applications are described with a focus on predictive maintenance, production planning, fault detection and predictive quality, as well as the increase in energy efficiency. In general, more research is needed that explicitly considers sustainability in the development and use phase of AI solutions, including Green AI. This paper contributes to research in this field by systematically examining papers and revealing research deficits. Additionally, practitioners are offered the first indications of AI applications increasing resource efficiency.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Identification Of Typical Use Cases Of Ai Application Increasing Resource Efficiencymentioning

confidence: 99%

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Artificial Intelligence Applications for Increasing Resource Efficiency in Manufacturing Companies—A Comprehensive Review

et al. 2021

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“…Equation (7) shows the frequency shift is linearly proportional to the vibration velocity of the object 3 [10]. According to it, the laser vibrometer obtains the vibration velocity signals by measuring Doppler shift.…”

Section: Figure 1 Laser Doppler Effect Optical Pathmentioning

confidence: 99%

“…In addition, the laser Doppler technology has a large measurement range, fast dynamic response. Nowadays, the detection method based on laser Doppler technique have begun to be applied to the fault detection of faulty bearings [7], gears [8] and other parts [9].…”

Section: Introductionmentioning

confidence: 99%

Research on bearing fault diagnosis based on laser vibrometer

Chen,

Jiang,

Zhou

et al. 2024

J. Phys.: Conf. Ser.

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As general components in machinery, rolling bearings are also fault prone components. And unexpected faults will bring immeasurable loss to economy, personnel, etc. Therefore, fault detection and diagnosis are of paramount importance. As the carrier of fault information, component response signal is the most concerned part in fault diagnosis researches. The detection methods for response signal can be divided into contact and non-contact detection. Compared with the contact detection, laser Doppler vibrometer has the advantage of no contact, no damage and dynamic measurement. Thus, this paper progresses theoretical and experimental researches on non-contact detection of bearing wear fault based on laser Doppler vibrometer. Firstly, the vibration measurement principle of laser Doppler vibrometer is analyzed in this paper. On account of the principle, the internal optical path and signal flow of the single point laser vibrometer is established and analyzed. Secondly, in order to carry out the non-contact detection test, an optical measuring system based on a single point laser vibrometer is built to collect signals. Also, a bearing wear fault simulated test rig is built to provide fault response signals. Based on the above built test devices, the detection tests of the intact and faulty bearings are carried out. Lastly, through the analysis of collected signals of bearings with wear of different parts in time domain and frequency domain, the characterizations of the corresponding fault are obtained so as to implement the fault diagnosis, which prove the effectiveness of laser vibration detection method for fault detection.

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Evaluation of a non-contact method for measuring vibration of rolling bearings in industrial conditions

Cited by 2 publications

References 11 publications

Artificial Intelligence Applications for Increasing Resource Efficiency in Manufacturing Companies—A Comprehensive Review

Artificial Intelligence Applications for Increasing Resource Efficiency in Manufacturing Companies—A Comprehensive Review

Research on bearing fault diagnosis based on laser vibrometer

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