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Abstrak :
IFToMM conferences have a history of success due to the various advances achieved in the field of rotor dynamics over the past three decades. These meetings have since become a leading global event, bringing together specialists from industry and academia to promote the exchange of knowledge, ideas, and information on the latest developments in the dynamics of rotating machinery. The scope of the conference is broad, including e.g. active components and vibration control, balancing, bearings, condition monitoring, dynamic analysis and stability, wind turbines and generators, electromechanical interactions in rotor dynamics and turbochargers. The proceedings are divided into four volumes. This second volume covers the following main topics: condition monitoring, fault diagnostics and prognostics; modal testing and identification; parametric and self-excitation in rotor dynamics; uncertainties, reliability and life predictions of rotating machinery; and torsional vibrations and geared systems dynamics.

Abstrak :
Rotating machinery dalam industri minyak dan gas merupakan aset kritis yang beroperasi dalam medan kerja yang berat, sehingga beberapa bagian umum rentan mengalami fault. Fault merupakan anomali yang menunjukkan penyimpangan dari kondisi operasi normal pada suatu sistem, sehingga perlu dideteksi lebih dini, secara akurat, dan terotomasi. Salah satu metode yang dapat digunakan adalah dengan machine learning. Data yang digunakan dalam penelitian ini adalah sensor condition monitoring aset rotating machinery yang diperoleh dari sebuah perusahaan minyak dan gas di Indonesia. Data sensor yang diperoleh mencakup 3 operation parameters yakni kecepatan, suhu, dan vibrasi. Algoritme klasifikasi pada penelitian ini menggunakan supervised learning yakni Support Vector Machine (SVM), Random Forest (RF), dan K-Nearest Neighbors (KNN). Kinerja model machine learning dievaluasi menggunakan metrik accuracy, precision, F1 score, dan matthews correlation coefficient (MCC). Hasil model klasifikasi random forest menunjukkan hasil yang sangat baik dengan akurasi 98,5%, presisi 98,6%, f1-score 98,5%, dan MCC sebesar 97,2%. Analisis SHAP Explainer secara global mampu menjelaskan feature importance dan secara lokal yang memperlihatkan kontribusi variabel-variabel operating parameter yang berkontribusi paling besar pada kelas normal, alert, dan fault. ......Rotating machinery in the oil and gas industry is a critical asset that operates in a tough work environment, where some of the common parts are prone to faults. Fault is an anomaly that indicates a deviation from the normal operating conditions of a system, so it needs to be detected early, accurately, and automated. The data used in this study is obtained from a condition monitoring sensor of rotating machinery in an oil and gas company in Indonesia. The acquired sensor data includes 3 operating parameters: speed, temperature, and vibration. The classification algorithms used in this research are supervised learning methods, namely Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN). The performance of the machine learning models is evaluated using metrics such as accuracy, precision, F1 score, and Matthews correlation coefficient (MCC). The results of the random forest classification model show very good results with an accuracy of 98.5%, a precision of 98.6%, an f1-score of 98.5%, and an MCC of 97.2%. SHAP Explainer in global explanation is able to explain the feature importance and also locally which shows the contribution of operating parameter variables that contribute the most to the normal, alert, and fault classes.

Abstrak :
Rotating Machinery, Optical Methods & Scanning LDV Methods, Volume 6: Proceedings of the 37th IMAC, A Conference and Exposition on Structural Dynamics, 2019, the sixth volume of eight from the Conference brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Structural Health Monitoring, including papers on: - Novel Techniques - Optical Methods, - Scanning LDV Methods - Photogrammetry & DIC - Rotating Machinery

Abstrak :
IFToMM conferences have a history of success due to the various advances achieved in the field of rotor dynamics over the past three decades. These meetings have since become a leading global event, bringing together specialists from industry and academia to promote the exchange of knowledge, ideas, and information on the latest developments in the dynamics of rotating machinery. The scope of the conference is broad, including e.g. active components and vibration control, balancing, bearings, condition monitoring, dynamic analysis and stability, wind turbines and generators, electromechanical interactions in rotor dynamics and turbochargers. The proceedings are divided into four volumes. This fourth volume covers the following main topics: aero-engines; turbochargers; eolian (wind) generators; automotive rotating systems; and hydro power plants.

Abstrak :
IFToMM conferences have a history of success due to the various advances achieved in the field of rotor dynamics over the past three decades. These meetings have since become a leading global event, bringing together specialists from industry and academia to promote the exchange of knowledge, ideas, and information on the latest developments in the dynamics of rotating machinery. The scope of the conference is broad, including e.g. active components and vibration control, balancing, bearings, condition monitoring, dynamic analysis and stability, wind turbines and generators, electromechanical interactions in rotor dynamics and turbochargers. The proceedings are divided into four volumes. This third volume covers the following main topics: dynamic analysis and stability; electromechanical interactions in rotordynamics; nonlinear phenomena in rotordynamics; rotordynamics of micro, nano and cryogenic machines; and fluid structure interactions in rotordynamics.

Abstrak :
IFToMM conferences have a history of success due to the various advances achieved in the field of rotor dynamics over the past three decades. These meetings have since become a leading global event, bringing together specialists from industry and academia to promote the exchange of knowledge, ideas, and information on the latest developments in the dynamics of rotating machinery. The scope of the conference is broad, including e.g. active components and vibration control, balancing, bearings, condition monitoring, dynamic analysis and stability, wind turbines and generators, electromechanical interactions in rotor dynamics and turbochargers. The proceedings are divided into four volumes. This first volume covers the following main topics: Active Components and Vibration Control; Balancing; Bearings: Fluid Film Bearings, Magnetic Bearings, Rolling Bearings and Seals; and Blades, Bladed Systems and Impellers.

Abstrak :
This book provides readers with a timely snapshot of the potential offered by and challenges posed by signal processing methods in the field of machine diagnostics and condition monitoring. It gathers contributions to the first Workshop on Signal Processing Applied to Rotating Machinery Diagnostics, held in Setif, Algeria, on April 9-10, 2017, and organized by the Applied Precision Mechanics Laboratory (LMPA) at the Institute of Precision Mechanics, University of Setif, Algeria and the Laboratory of Mechanics, Modeling and Manufacturing (LA2MP) at the National School of Engineers of Sfax. The respective chapters highlight research conducted by the two laboratories on the following main topics: noise and vibration in machines; condition monitoring in non-stationary operations; vibro-acoustic diagnosis of machinery; signal processing and pattern recognition methods; monitoring and diagnostic systems; and dynamic modeling and fault detection.