Assessment of Gearbox Fault DetectionUsing Vibration Signal Analysis and Acoustic Emission Technique

Saxena, Vimal

doi:10.9790/1684-0745260

Cited by 16 publications

(7 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By analyzing the signal in the frequency domain, we can detect anomalies caused by friction and stresses that may be symptoms of deterioration. In the case of the detection of gearboxes defects, acoustic analysis can complement vibration monitoring for the detection of more minor defects [50]. On the other hand, ultrasonic analysis is used to detect leaks in valves and pipes.…”

Section: Acoustic Analysismentioning

confidence: 99%

From Corrective to Predictive Maintenance—A Review of Maintenance Approaches for the Power Industry

Moleda

Małysiak-Mrozek

Ding

et al. 2023

Sensors

View full text Add to dashboard Cite

Appropriate maintenance of industrial equipment keeps production systems in good health and ensures the stability of production processes. In specific production sectors, such as the electrical power industry, equipment failures are rare but may lead to high costs and substantial economic losses not only for the power plant but for consumers and the larger society. Therefore, the power production industry relies on a variety of approaches to maintenance tasks, ranging from traditional solutions and engineering know-how to smart, AI-based analytics to avoid potential downtimes. This review shows the evolution of maintenance approaches to support maintenance planning, equipment monitoring and supervision. We present older techniques traditionally used in maintenance tasks and those that rely on IT analytics to automate tasks and perform the inference process for failure detection. We analyze prognostics and health-management techniques in detail, including their requirements, advantages and limitations. The review focuses on the power-generation sector. However, some of the issues addressed are common to other industries. The article also presents concepts and solutions that utilize emerging technologies related to Industry 4.0, touching on prescriptive analysis, Big Data and the Internet of Things. The primary motivation and purpose of the article are to present the existing practices and classic methods used by engineers, as well as modern approaches drawing from Artificial Intelligence and the concept of Industry 4.0. The summary of existing practices and the state of the art in the area of predictive maintenance provides two benefits. On the one hand, it leads to improving processes by matching existing tools and methods. On the other hand, it shows researchers potential directions for further analysis and new developments.

show abstract

Section: Acoustic Analysismentioning

confidence: 99%

From Corrective to Predictive Maintenance—A Review of Maintenance Approaches for the Power Industry

Moleda

Małysiak-Mrozek

Ding

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…According to different transform methods and signal properties, amplitude spectrum and power spectrum are commonly used in the spectrum analysis [18][19][20][21]. The amplitude spectrum represents the amplitude corresponding to the harmonic vibration components of each frequency, whereas the height of the spectral line represents the amplitude of the frequency components.…”

Section: Fast Fourier Transform (Fft)mentioning

confidence: 99%

Effect of Multiple Factors on Identification and Diagnosis of Skidding Damage in Rolling Bearings under Time-Varying Slip Conditions

Chen

Xue

et al. 2019

Applied Sciences

View full text Add to dashboard Cite

Skidding damage mechanism of rolling bearings is not clear, due to the influence of various coupling factors. To solve this problem, it is important to identify and diagnose skidding damage and study the vibration characteristics in rolling bearings. Based on Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT), vibration signals of rolling bearings are extracted and analyzed, and then the skidding damage of rolling bearings from multiple signals perspectives is identified. The relationship between the variation in the radial load, temperature, slip and the skidding damage of rolling bearings under time-varying slip conditions is analyzed comprehensively, and then the influence of different factors on bearing skidding damage is studied. The integrated analysis of the vibration, load, temperature, slip rate and other multivariate signals information shows the starting time of skidding damage. This research can be conducive to reduce vibration and prolong the life of rolling bearings.

show abstract

“…[7][8][9][10][11][12][13][14][15] Although there are many kinds of methods such as vibration, acoustic, temperature, and wear debris analysis have been reported for diagnosis and analysis of bearing defects, the vibration spectral measurement is the most widely used approach. [16][17][18][19] The discrete Fourier transform (DFT) is a popular tool in spectrum analysis. If the vibration is under the stationary conditions, it may be efficient for measurement.…”

Section: Introductionmentioning

confidence: 99%

Reviews of bearing vibration measurement using fast Fourier transform and enhanced fast Fourier transform algorithms

Lin

2019

Advances in Mechanical Engineering

View full text Add to dashboard Cite

The rolling element bearing is one of the most critical components in a machine. Vibration signals resulting from these bearings imply important bearing defect information related to the machinery faults. Any defect in a bearing may cause a certain vibration with specific frequencies and amplitudes depending on the nature of the defect. Therefore, the vibration analysis plays a key role for fault detection, diagnosis, and prognosis to reach the reliability of the machines. Although fast Fourier transform for time-frequency analysis is still widely used in industry, it cannot extract enough frequencies without enough samples. If the real frequency does not match fast Fourier transform frequency grid exactly, the spectrum is spreading mostly among neighboring frequency bins. To resolve this drawback, the recent proposed enhanced fast Fourier transform algorithm was reported to improve this situation. This article reviews and compares both fast Fourier transform and enhanced fast Fourier transform for vibration signal analysis in both simulation and practical work. The comparative results verify that the enhanced fast Fourier transform can provide a better solution than traditional fast Fourier transform.

show abstract

Assessment of Gearbox Fault DetectionUsing Vibration Signal Analysis and Acoustic Emission Technique

Cited by 16 publications

References 9 publications

From Corrective to Predictive Maintenance—A Review of Maintenance Approaches for the Power Industry

From Corrective to Predictive Maintenance—A Review of Maintenance Approaches for the Power Industry

Effect of Multiple Factors on Identification and Diagnosis of Skidding Damage in Rolling Bearings under Time-Varying Slip Conditions

Reviews of bearing vibration measurement using fast Fourier transform and enhanced fast Fourier transform algorithms

Contact Info

Product

Resources

About