Prediction and classification of tool wear and its state in sustainable machining of Bohler steel with different machine learning models

Korkmaz, Mehmet Erdi; Gupta, Munish Kumar; Kuntoğlu, Mustafa; Patange, Abhishek D.; Ross, Nimel Sworna; Yılmaz, Hakan; Chauhan, Sumika; Vashishtha, Govind

doi:10.1016/j.measurement.2023.113825

Cited by 42 publications

(17 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Data-driven fault diagnosis methods remove the requirement of prior knowledge and accurate dynamic models, and identify fault types by feature engineering and pattern recognition technology without dismantling mechanical devices. Korkmaz et al [ 15 ] investigates the application of machine learning algorithms in prediction and classification of tool wear and its state. Vashishtha et al [ 16 ] developed a robust intelligent fault diagnosis scheme of worm gearbox based on adaptive Convolutional Neural Networks (CNN).…”

Section: Introductionmentioning

confidence: 99%

Hidden Markov Models based intelligent health assessment and fault diagnosis of rolling element bearings

Qifeng,

Longsheng,

Naeem

2024

PLoS ONE

View full text Add to dashboard Cite

Hidden Markov Models (HMMs) have become an immensely popular tool for health assessment and fault diagnosis of rolling element bearings. The advantages of an HMM include its simplicity, robustness, and interpretability, while the generalization capability of the model still needs to be enhanced. The Dempster-Shafer theory of evidence can be used to conduct a comprehensive evaluation, and Stacking provides a novel training strategy. Therefore, the HMM-based fusion method and ensemble learning method are proposed to increase the credibility of quantitative analysis and optimize classifiers respectively. Firstly, vibration signals captured from bearings are decomposed into intrinsic mode functions (IMFs) using ensemble empirical mode decomposition (EEMD), and then the Hilbert envelope spectra of main components are obtained; Secondly, multi-domain features are extracted as model input from preprocessed signals; Finally, HMM-based intelligent health assessment framework and fault diagnosis framework are established. In this work, the life cycle health assessment modeling is performed using a few training samples, the bearing degradation state is quantitatively evaluated, normal and abnormal samples are effectively distinguished, and the accuracy of fault diagnosis is significantly improved.

show abstract

Section: Introductionmentioning

confidence: 99%

Hidden Markov Models based intelligent health assessment and fault diagnosis of rolling element bearings

Qifeng,

Longsheng,

Naeem

2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Machine learning algorithms can analyze large amounts of data intelligently, providing new insights for the operation and maintenance of TBMs. This improves the efficiency and quality of tunneling projects [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

SSFLNet: A Novel Fault Diagnosis Method for Double Shield TBM Tool System

Zhou,

Liu,

et al. 2024

Sensors

View full text Add to dashboard Cite

In tunnel boring projects, wear and tear in the tooling system can have significant consequences, such as decreased boring efficiency, heightened maintenance costs, and potential safety hazards. In this paper, a fault diagnosis method for TBM tooling systems based on SAV−SVDD failure location (SSFL) is proposed. The aim of this method is to detect faults caused by disk cutter wear during the boring process, which diminishes the boring efficiency and is challenging to detect during construction. This paper uses SolidWorks to create a complete three−dimensional model of the TBM hydraulic thrust system and tool system. Then, dynamic simulations are performed with Adams. This helps us understand how the load on the propulsion hydraulic cylinder changes as the TBM tunneling tool wears to different degrees during construction. The hydraulic propulsion system was modeled and simulated using AMESIM software. Utilizing the load on the hydraulic propulsion cylinder as an input signal, pressure signals from the two chambers of the hydraulic cylinder and the system’s flow signal were acquired. This enabled an in−depth exploration of the correlation between these acquired signals and the extent of the tooling system failure. Following this analysis, a collection of normal sample data and sample data representing different degrees of disk cutter abrasions was amassed for further study. Next, an SSFL network model for locating the failure area of the cutter was established. Fault sample data were used as the input, and the accuracy of the fault diagnosis model was tested. The test results show that the performance of the SSFL network model is better than that of the SAE−SVM and SVDD network models. The SSFL model achieves 90% accuracy in determining the failure area of the cutter head. The model effectively identifies the failure regions, enabling timely tool replacement to avoid decreased boring efficiency under wear conditions. The experimental findings validate the feasibility of this approach.

show abstract

“…In addition, with the rapid development of artificial intelligence technology, some intelligent methods have begun to be applied to the field of rotating machinery [ 35 , 36 ]. Kumar et al [ 37 ] presented the development of a new convolutional neural network (NCNN) to efficiently identify bearing defects in rotating machinery from small samples.…”

Section: Introductionmentioning

confidence: 99%

Aerodynamic noise characteristics of a centrifugal fan in high-altitude environments

Liu,

Liu

2024

PLoS ONE

View full text Add to dashboard Cite

In high-altitude areas, the air is thin and the atmospheric pressure is low, which can affect the performance of centrifugal fans and aerodynamic noise. In this paper, steady and unsteady simulations of a centrifugal fan flow field are performed at altitudes of 0, 1000, 2000, 3000, 4000, and 5000 m, and the Ffowcs Williams-Hawkings equation is used to predict the aerodynamic noise of the fan. The results indicate that the tonal and broadband noise generated by the fan decrease with increasing altitude, and the A-weighted sound pressure level of each frequency band of the fan decreases when the air volume is held fixed. The maximum sound power level Lwmax, sound pressure pulsation interval, and total noise sound pressure level Lp decrease linearly with increasing altitude. For every 1000 m increase in altitude, Lwmax and Lp decrease by 0.45 dB and 1.05 dB respectively. The fan noise characteristics, performance parameters, and human auditory perception are the main factors that affect the establishment of fan noise standards in high-altitude areas.

show abstract

Prediction and classification of tool wear and its state in sustainable machining of Bohler steel with different machine learning models

Cited by 42 publications

References 48 publications

Hidden Markov Models based intelligent health assessment and fault diagnosis of rolling element bearings

Hidden Markov Models based intelligent health assessment and fault diagnosis of rolling element bearings

SSFLNet: A Novel Fault Diagnosis Method for Double Shield TBM Tool System

Aerodynamic noise characteristics of a centrifugal fan in high-altitude environments

Contact Info

Product

Resources

About