Detection of Gear Wear and Faults in Spur Gear Systems Using Statistical Parameters and Univariate Statistical Process Control Charts

Maraş, Sinan; Arslan, Hakan; Birgören, Burak

doi:10.1007/s13369-021-05930-y

Cited by 14 publications

(4 citation statements)

References 39 publications

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“…If significant errors occur during the data collection process and the timeliness is not strong, subsequent experiments and data processing will also lose significance. The main function of this sensor node is to complete real-time collection and data fusion processing of lateral, longitudinal, and vertical vibration accelerations at different positions, and encapsulate the processed data, which is then transmitted to the upper computer for later analysis and processing [10].…”

Section: Main Hardware Designmentioning

confidence: 99%

Design and application of gearbox vibration testing system

Geng,

Chen,

2023

Vib. proced.

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Judging the working status of a gearbox through the characteristics of vibration signals was an effective fault diagnosis method. In order to improve the efficiency and reliability of vibration detection, a multi node testing system based on a microcontroller was designed and validated. The system mainly consists of signal processing equipment, signal collector, upper computer, sensors, and experimental workbench. Under different types of gear faults, the vibration signals of ten sets of gearboxes were synchronously collected and processed. In order to ensure the resolution of frequency conversion and accurately locate frequency conversion, the low-pass filtered spectrum with a filtering frequency of 1000 was verified and used to calculate the amplitude of frequency conversion. The normalization of amplitude was more conducive to feature recognition, and the frequency amplitude was proportional to its corresponding energy. According to standard spectral data and spectral analysis, wear, pitting, fracture, and bonding faults in the gearbox were identified and classified. Through the verification of three order feature spectrum, it can be seen that the accuracy of fault prediction for gearbox through vibration signals is high, which can effectively reduce maintenance and repair costs.

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Section: Main Hardware Designmentioning

confidence: 99%

Design and application of gearbox vibration testing system

Geng,

Chen,

2023

Vib. proced.

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“…Referring to the considered problem, which is the diagnostics of radial internal clearance under different operating conditions, more sophisticated methods have to be used due to its variability and fluctuations in time. In the previous paper, the accuracy of some recurrence quantificators was proven [4], but other statistical indicators can also be used for the preparation of the window analysis [36][37][38]. The current study refers to the previously discussed issue; however, the window analysis is based on nine statistical indicators.…”

mentioning

confidence: 93%

Intelligent Diagnostics of Radial Internal Clearance in Ball Bearings with Machine Learning Methods

Ambrożkiewicz

Syta

Georgiadis

et al. 2023

Sensors

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This article classifies the dynamic response of rolling bearings in terms of radial internal clearance values. The value of the radial internal clearance in rolling-element bearings cannot be described in a deterministic manner, which shows the challenge of its detection through the analysis of the bearing’s dynamics. In this article, we show the original approach to its intelligent detection through the analysis of short-time intervals and the calculation of chosen indicators, which can be assigned to the specific clearance class. The tests were carried out on a set of 10 brand new bearings of the same type (double row self-aligning ball bearing NTN 2309SK) with different radial internal clearances corresponding to individual classes of the ISO-1132 standard. The classification was carried out based on the time series of vibrations recorded by the accelerometer and then digitally processed. Window statistical indicators widely used in the diagnosis of rolling bearings, which served as features for the machine learning models, were calculated. The accuracy of the classification turned out to be unsatisfactory; therefore, it was decided to use a more advanced method of time series processing, which allows for the extraction of subsequent dominant frequencies into experimental modes (Variational Mode Decomposition (VMD)). Applying the same statistical indicators to the modes allowed for an increase in classification accuracy to over 90%.

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“…This method achieves fault diagnosis by collecting bearing vibration signals and performing their feature extraction, analysis and identification. The classical methods include root mean square [5], crest factor [6], fast Fourier transform [7], wavelet transform and wavelet packet transform [8], etc. The main advantages of the methods are that they can be used for noise reduction and feature extraction with no need of actual mathematical modeling [9].…”

Section: Introductionmentioning

confidence: 99%

A bearing fault diagnosis approach based on an improved neural network combined with transfer learning

Li,

Pan,

Fan

et al. 2024

Meas. Sci. Technol.

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In modern industrial systems, bearing failures account for 30–40% of industrial machinery faults. Traditional convolutional neural network suffers from gradient vanishing and overfitting, resulting in a poor diagnostic accuracy. To address the issues, a new bearing fault diagnosis approach was proposed based on an improved AlexNet neural network combined with transfer learning. After decomposition and noise-reduction, reconstructed vibration signals were transformed into 2D images, then input into the improved AlexNet for training and follow-up transfer learning. Program auto-tuning and image-enhancing techniques were employed to increase the diagnostic accuracy in this study. The approach was verified with the datasets from Case Western Reserve University (CWRU), Jiangnan University (JNU), and the Association for Mechanical Failure Prevention Technology (MFPT). The results showed that the diagnostic accuracies by normal learning were more than 97% for CWRU and JNU datasets, and 100% for MFPT dataset. After transfer learning, the accuracies all reached above 99.5%. The proposed approach was demonstrated to be able to effectively diagnose the bearing faults.

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Detection of Gear Wear and Faults in Spur Gear Systems Using Statistical Parameters and Univariate Statistical Process Control Charts

Cited by 14 publications

References 39 publications

Design and application of gearbox vibration testing system

Design and application of gearbox vibration testing system

Intelligent Diagnostics of Radial Internal Clearance in Ball Bearings with Machine Learning Methods

A bearing fault diagnosis approach based on an improved neural network combined with transfer learning

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