K-means clustering approach for damage evolution monitoring in RCF tests

Chiesa, Marco; Bodini, Ileana; Petrogalli, Candida; Provezza, Luca; Faccoli, Michela; Mazzù, Angelo; Solazzi, Luigi; Sansoni, Giovanna; Lancini, Matteo

doi:10.1088/1742-6596/1065/10/102018

Cited by 3 publications

(3 citation statements)

References 13 publications

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“…The analog signals are recorded in packets of 0.2 s for faster post-processing, and further split in binary files representing 200 s to avoid the handling of big-size files. The feature extraction and the relative weight of the features follow the same approach as in [15]. Software developed in Labview computed in real-time for all three signals (two vibration signals and one torque) and for each record, 44 features, aiming to get a detailed representation of the phenomenon every 0.2 s. The features extracted are, for each channel: mean, variance, root mean square, 75th percentiles, median, and the centroid of the power spectrum density, as shown in Table 2.…”

Section: Data Processingmentioning

confidence: 99%

“…The proposed study aims to quantitatively estimate the damage progress of a specimen during a test with continuous and non-contact measurements, using a non-supervised machine learning technique [10][11][12][13], called k-means clustering [14], applied to vibrations and torque datasets. The idea was to validate a system developed in a previous work [15], to understand if the approach could be used to control the duration of the test. Preliminary RCF tests were carried out with the same wheel and rail steels to create a database used to train the algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…In the current literature, many studies have been focused on quantifying the damage evolution and improvement of the fault condition, especially without stopping the tests [1,8,15,21,22].…”

Section: Introductionmentioning

confidence: 99%

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Monitoring the Damage Evolution in Rolling Contact Fatigue Tests Using Machine Learning and Vibrations

Provezza¹,

Bodini²,

Petrogalli³

et al. 2021

Metals

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This study shows the application of a system to monitor the state of damage of railway wheel steel specimens during rolling contact fatigue tests. This system can make continuous measurements with an evaluation of damage without stopping the tests and without destructive measurements. Four tests were carried out to train the system by recording torque and vibration data. Both statistical and spectral features were extracted from the sensors signals. A Principal Component Analysis (PCA) was performed to reduce the volume of the initial dataset; then, the data were classified with the k-means algorithm. The results were then converted into probabilities curves. Metallurgical investigations (optical micrographs, wear curves) and hardness tests were carried out to assess the trends of machine learning analysis. The training tests were used to train the proposed algorithm. Three validation tests were performed by using the real-time results of the k-means algorithm as a stop condition. Metallurgical analysis was performed also in this case. The validation tests follow the results of the training test and metallurgical analysis confirms the damage found with the machine learning analysis: when the membership probability of the cluster corresponding to the damage state reaches a value higher than 0.5, the metallurgical analysis clearly shows the cracks on the surface of the specimen due to the rolling contact fatigue (RCF) damage mechanism. These preliminary results are positive, even if reproduced on a limited set of specimens. This approach could be integrated in rolling contact fatigue tests to provide additional information on damage progression.

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Section: Data Processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Monitoring the Damage Evolution in Rolling Contact Fatigue Tests Using Machine Learning and Vibrations

Provezza¹,

Bodini²,

Petrogalli³

et al. 2021

Metals

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A study of wear and rolling contact fatigue on a wheel steel in alternated dry-wet contact aided by innovative measurement systems

Bodini

Mazzù

Petrogalli

et al. 2019

Procedia Structural Integrity

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Application of condition-based monitoring in enhancing mechanical system reliability and proactive structural damage detection

Djidrov

2024

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Technologies, processes, and systems are not immune to failure, which is why robust monitoring systems are crucial to ensure their continued functionality and safety. An interdisciplinary approach that combines engineering, data science, and material science allows for more comprehensive measurement and analysis, enabling better decision-making and more accurate predictions of performance. The integration of these technologies leads to increased safety, reduced human error, and significant cost savings by preventing costly repairs and downtime. Continuous monitoring helps in avoiding catastrophic failures, allowing for early detection of issues before they escalate. Additionally, it opens opportunities for improving the design of mechanical systems and structures, optimizing the organization of maintenance. By reducing human impact and enhancing safety, these monitoring systems offer a more secure and efficient operation. Furthermore, through advanced predictive analytics, the remaining service life can be estimated, facilitating more effective planning. The development of such smart, intelligent mechanical systems and structures promises a future where maintenance is proactive rather than reactive, creating a safer, more sustainable environment for both operators and systems by leveraging advanced sensors, data analytics, and adaptive technologies for real-time monitoring and damage detection.

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K-means clustering approach for damage evolution monitoring in RCF tests

Cited by 3 publications

References 13 publications

Monitoring the Damage Evolution in Rolling Contact Fatigue Tests Using Machine Learning and Vibrations

Monitoring the Damage Evolution in Rolling Contact Fatigue Tests Using Machine Learning and Vibrations

A study of wear and rolling contact fatigue on a wheel steel in alternated dry-wet contact aided by innovative measurement systems

Application of condition-based monitoring in enhancing mechanical system reliability and proactive structural damage detection

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