Wireless acceleration sensor of moving elements for condition monitoring of mechanisms

Sinitsin, Vladimir V; Shestakov, A. L.

doi:10.1088/1361-6501/aa7ab6

Cited by 15 publications

(16 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The method described above has been implemented in simulation and applied to the signals of the wireless sensor with random white noise. The parameters and the initial conditions of the sensor are the same as in [15]. We assumed that the shaft moved with the accelerations (5).…”

Section: Computational Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Determination of Shaft Rotation Angle from Accelerations of the Wireless Sensor by the Novel Numerical Method

Sinitsin¹,

Yaparova²

2018

CTAC&R

View full text Add to dashboard Cite

show abstract

Section: Computational Resultsmentioning

confidence: 99%

“…The mathematical model of the wireless sensor, which is reported in [15], is described by the equation system…”

Section: Statement Of the Problemmentioning

confidence: 99%

Determination of Shaft Rotation Angle from Accelerations of the Wireless Sensor by the Novel Numerical Method

Sinitsin¹,

Yaparova²

2018

CTAC&R

View full text Add to dashboard Cite

show abstract

“…There is a method to detect vibration signals by installing an acceleration sensor used for state detection inside a bearing and decomposing the detected acceleration signals into angular and linear components. The analyzed data can be used for bearing fault diagnosis (Vladimir V Sinitsin et al, 2017) [ 80 ]. This has reference value.…”

Section: Detection Methods Based On One-dimensional Signalsmentioning

confidence: 99%

A Review on Rolling Bearing Fault Signal Detection Methods Based on Different Sensors

Yan

Yang

et al. 2022

Sensors

View full text Add to dashboard Cite

As a precision mechanical component to reduce friction between components, the rolling bearing is widely used in many fields because of its slight friction loss, strong bearing capacity, high precision, low power consumption, and high mechanical efficiency. This paper reviews several excellent kinds of study and their relevance to the fault detection of rolling bearings. We summarize the fault location, sensor types, bearing fault types, and fault signal analysis of rolling bearings. The fault signal types are divided into one-dimensional and two-dimensional images, which account for 40.14% and 31.69%, respectively, and their classification is clarified and discussed. We counted the proportions of various methods in the references cited in this paper. Among them, the method of one-dimensional signal detection with external sensors accounted for 3.52%, the method of one-dimensional signal detection with internal sensors accounted for 36.62%, and the method of two-dimensional signal detection with external sensors accounted for 19.72%. The method of two-dimensional signal detection with internal sensors accounted for 11.97%. Among these methods, the highest detection rate is 100%, and the lowest detection rate is more than 70%. The similarities between the different methods are compared. The research results summarized in this paper show that with the progress of the times, a variety of new and better research methods have emerged, which have sped up the detection and diagnosis of rolling bearing faults. For example, the technology using artificial intelligence is still developing rapidly, such as artificial neural networks, convolutional neural networks, and machine learning. Although there are still defects, such methods can quickly discover a fault and its cause, enrich the database, and accumulate experience. More and more advanced techniques are applied in this field, and the detection method has better robustness and superiority.

show abstract

“…More frequently separated movements of the shaft are studied (see [1,[7][8][9]). Sinitsin and Shestakov [10] present a comprehensive analysis of the angular and linear accelerations of moving elements (shafts, gears) by wireless acceleration sensor of moving elements. The combined motions are presented in paper [2].…”

Section: Transfer Matrix For Elastic Bearingmentioning

confidence: 99%

Modelling of Critical Velocities of the Cardan Mechanism using Transfer Matrix Method

Hrubý¹,

Náhlík²

2021

Komunikácie

View full text Add to dashboard Cite

The presented paper focuses to rotating components of mechanical constructions. The problem of the spatial combined bending-gyratory vibration and calculation of the Eigen frequencies is studied. The model of Cardan Mechanism is solved by the transfer matrix method. Transfer matrices were derived for shaft, concentrated mass and elastic bearing. The physical and mechanical properties of each part of the mechanism are hidden in these matrices. A procedure for calculating Eigen frequencies was proposed.

show abstract

Wireless acceleration sensor of moving elements for condition monitoring of mechanisms

Cited by 15 publications

References 27 publications

Determination of Shaft Rotation Angle from Accelerations of the Wireless Sensor by the Novel Numerical Method

Determination of Shaft Rotation Angle from Accelerations of the Wireless Sensor by the Novel Numerical Method

A Review on Rolling Bearing Fault Signal Detection Methods Based on Different Sensors

Modelling of Critical Velocities of the Cardan Mechanism using Transfer Matrix Method

Contact Info

Product

Resources

About