A Bayesian Optimized Discriminant Analysis Model for Condition Monitoring of Face Milling Cutter Using Vibration Datasets

Bajaj, Naman S.; Patange, Abhishek D.; Jegadeeshwaran, R.; Kulkarni, Kaushal A.; Ghatpande, Rohan S.; Kapadnis, Atharva M.

doi:10.1115/1.4051696

Cited by 26 publications

(18 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Bajaj et al realized early fault warning of gearbox by constructing the self-coding network model and combining the threshold analysis method based on an extremum theory. As the model fusion output is multiple monitoring quantities, the abovementioned method can better comprehensively represent the operating state of the gearbox [4]. However, the SCADA data of wind turbine gearbox in normal operation not only have stable data structure characteristics but also satisfy some data distribution rules.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Wind Turbine Gearbox Based on Multisensor Data Fusion

Zhang

Zhou

2022

Journal of Control Science and Engineering

View full text Add to dashboard Cite

In order to improve the operation efficiency of wind turbine gearbox and reduce the operation and maintenance cost of wind farm, a fault diagnosis system for wind turbine gearbox based on multisensor data fusion was proposed. First, the different time-domain statistical characteristic parameters of the original vibration signal were calculated, and the information fusion of the feature level and the data level was carried out by means of parallel superposition to obtain the fused data set. Second, a fault classification and recognition model based on GMO-KELM was established by using the fusion data set. Finally, the proposed method was used to monitor the status of the measured data of the gearbox on the vibration test bed of rotating machinery. The experimental results showed that the average training accuracy and the average test accuracy of GMO-KELM method were 100% and 95.58%, respectively, which were much higher than those of other methods. Through experiments and analyses, it was shown that the proposed method was effective and feasible. Compared with other similar methods, the proposed method had the best classification performance.

show abstract

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Wind Turbine Gearbox Based on Multisensor Data Fusion

Zhang

Zhou

2022

Journal of Control Science and Engineering

View full text Add to dashboard Cite

show abstract

“…The statistical approach has been very popular for examining the difference in a vibration signals of each of the tool category [33][34][35][36][37][38][39][40] and thus used herein. The time-domain response of vibration was studied using a statistical approach.…”

Section: Resultsmentioning

confidence: 99%

Application of Machine Learning for Tool Condition Monitoring in Turning

Patange¹,

R.²,

Bajaj³

et al. 2022

Sound&Vibration

Self Cite

View full text Add to dashboard Cite

The machining process is primarily used to remove material using cutting tools. Any variation in tool state affects the quality of a finished job and causes disturbances. So, a tool monitoring scheme (TMS) for categorization and supervision of failures has become the utmost priority. To respond, traditional TMS followed by the machine learning (ML) analysis is advocated in this paper. Classification in ML is supervised based learning method wherein the ML algorithm learn from the training data input fed to it and then employ this model to categorize the new datasets for precise prediction of a class and observation. In the current study, investigation on the single point cutting tool is carried out while turning a stainless steel (SS) workpeice on the manual lathe trainer. The vibrations developed during this activity are examined for failure-free and various failure states of a tool. The statistical modeling is then incorporated to trace vital signs from vibration signals. The multiple-binary-rule-based model for categorization is designed using the decision tree. Lastly, various tree-based algorithms are used for the categorization of tool conditions. The Random Forest offered the highest classification accuracy, i.e., 92.6%.

show abstract

“…In the table, the nomenclature for categories and sub-categories of phases, components, and topics reflect the previously introduced legend. Bajaj_2022 [276] x p3 x Xu_2022 [277] x p4 x x Ye_2021a [244] x p3 x Ahmed_2021 [172] x x p2 x x Mufazzal_2021 [278] x p4 x Yang_2021 [110] x p2 x x x x x Moghadam_2021 [193] x p2 x x Saucedo-Dorantes_2021 [173] x p2 x x x x Espinoza-Sepulveda_2021 [279] x p4 x x Kalista_2021 [73] x p1 x x x Zhang_2021a [280] x p4 x x Zhang_2021b [267] x p3 x x Meng_2021 [59] x p1 x x Tiwari_2021 [95] x p1 x x Tatsis_2021 [281] x p4 x x Leaman_2021 [282] x p5 x x Ou_2021 [283] x p5 x x Espinoza_2021 [226] x x p3 x x Wang_2021 [233] x p3 x x x Goyal_2021 [60] x p1 x x Bai_2021a [203] x p2 x x x Yu_2021 [75] x p1 x x Papathanasopoulos_2021 [66] x p1 x x Sharma_2021 [25] x p1 x x x Rauber_2021 [219] x p3 x x Laval_2021 [76] x p1 x x x x Shao_2021 [158] x x p2 x x Rafiq_2021 [166] x p2 x x x x Zhao_2021 [77] x p1 x x Gómez_2021 [284] x p1 x x Jablon_2021 [197] x p2 x x Barusu_2021 [62] x p1 x x x x Hadroug_2021 [250] x p3 x Hou_2021 [35] x x p1 x x Yuan_2021 [285] x p5 x Ye_2021b [245] x p3 x x x Tingarikar_2021 [286] x p4 x Ribeiro_2021 [287] x p2 x Peng_2021 [288] x x p2 x x x Gu_2021 …”

Section: Appendix Amentioning

confidence: 99%

A Review on Vibration-Based Condition Monitoring of Rotating Machinery

et al. 2022

View full text Add to dashboard Cite

Monitoring vibrations in rotating machinery allows effective diagnostics, as abnormal functioning states are related to specific patterns that can be extracted from vibration signals. Extensively studied issues concern the different methodologies used for carrying out the main phases (signal measurements, pre-processing and processing, feature selection, and fault diagnosis) of a malfunction automatic diagnosis. In addition, vibration-based condition monitoring has been applied to a number of different mechanical systems or components. In this review, a systematic study of the works related to the topic was carried out. A preliminary phase involved the analysis of the publication distribution, to understand what was the interest in studying the application of the method to the various rotating machineries, to identify the interest in the investigation of the main phases of the diagnostic process, and to identify the techniques mainly used for each single phase of the process. Subsequently, the different techniques of signal processing, feature selection, and diagnosis are analyzed in detail, highlighting their effectiveness as a function of the investigated aspects and of the results obtained in the various studies. The most significant research trends, as well as the main innovations related to the various phases of vibration-based condition monitoring, emerge from the review, and the conclusions provide hints for future ideas.

show abstract

A Bayesian Optimized Discriminant Analysis Model for Condition Monitoring of Face Milling Cutter Using Vibration Datasets

Cited by 26 publications

References 27 publications

Fault Diagnosis of Wind Turbine Gearbox Based on Multisensor Data Fusion

Fault Diagnosis of Wind Turbine Gearbox Based on Multisensor Data Fusion

Application of Machine Learning for Tool Condition Monitoring in Turning

A Review on Vibration-Based Condition Monitoring of Rotating Machinery

Contact Info

Product

Resources

About