ProfessorsProfessors of Institut Teknologi Sepuluh Nopembermoch. solichin

moch. solichin

Institut Teknologi Sepuluh Nopember

H-index: 6

Asia-Indonesia

About moch. solichin

moch. solichin, With an exceptional h-index of 6 and a recent h-index of 5 (since 2020), a distinguished researcher at Institut Teknologi Sepuluh Nopember, specializes in the field of machinery diagnostics and prognostics, vibrations, optimization, big data analysis, machine learning.

His recent articles reflect a diverse array of research interests and contributions to the field:

Enhancing Realistic Remaining Useful Life Prediction using Multi-Fidelity Physic-Informed Neural Network Approach

Bearing Fault Degradation Modeling Based on Multi-time Windows Fusion Unsupervised Health Indicator

Fault Deterioration Modelling of Bearings Combined with Spectograms and PCA

Initial Fault Time Determination Based on Adaptive Thresholds for Predicting Bearing Remaining Useful Life

Combining Correlation Technique with Exhaustive Search Feature Selection Method for Rotating Machinery Fault Diagnosis

Multi-Filter Clustering Fusion for Feature Selection in Rotating Machinery Fault Classification

Adaptive Feature Selection Method using Multi-Filter Clustering for Bearing Fault Diagnosis

Integration of Extra-Trees Classifier with Exhaustive Technique as a Feature Selection Method for Rotating Machinery Fault Diagnosis

moch. solichin Information

University	Institut Teknologi Sepuluh Nopember
Position	Department of Mechanical Engineering
Citations(all)	108
Citations(since 2020)	94
Cited By	53
hIndex(all)	6
hIndex(since 2020)	5
i10Index(all)	4
i10Index(since 2020)	3
Email	Access Email
University Profile Page	Institut Teknologi Sepuluh Nopember
Google Scholar	View Google Scholar Profile

moch. solichin Skills & Research Interests

machinery diagnostics and prognostics

vibrations

optimization

big data analysis

machine learning

Top articles of moch. solichin

Title	Journal	Author(s)	Publication Date
Enhancing Realistic Remaining Useful Life Prediction using Multi-Fidelity Physic-Informed Neural Network Approach	Annual Conference of the PHM Society	Yoojeong Noh Solichin Mochammad Nam Ho Kim	2023/10/26
Bearing Fault Degradation Modeling Based on Multi-time Windows Fusion Unsupervised Health Indicator	IEEE Sensors Journal	Solichin Mochammad Yoojeong Noh Young-Jin Kang	2023/7/14
Fault Deterioration Modelling of Bearings Combined with Spectograms and PCA	대한기계학회 춘추학술대회	Solichin Mochammad Yoojeong Noh Young-Jing Kang	2022/3
Initial Fault Time Determination Based on Adaptive Thresholds for Predicting Bearing Remaining Useful Life		Solichin Mochammad1 Yoojeong Noh Nam Ho Kim	2022/12/9
Combining Correlation Technique with Exhaustive Search Feature Selection Method for Rotating Machinery Fault Diagnosis		Mochammad Solichin Yoojeong Noh Young-Jin Kang	2022/8/19
Multi-Filter Clustering Fusion for Feature Selection in Rotating Machinery Fault Classification	Sensors	Solichin Mochammad Yoojeong Noh Young-Jin Kang Sunhwa Park Jangwoo Lee ...	2022/3/11
Adaptive Feature Selection Method using Multi-Filter Clustering for Bearing Fault Diagnosis	대한기계학회 춘추학술대회	Solichin Mochammad Yoojeong Noh Young-Jin Kang	2021/11
Integration of Extra-Trees Classifier with Exhaustive Technique as a Feature Selection Method for Rotating Machinery Fault Diagnosis	대한기계학회 춘추학술대회	Solichin Mochammad Yoojeong Noh Young-Jin Kang	2021/8
Stable hybrid feature selection method for compressor fault diagnosis	IEEE Access	Solichin Mochammad Young-Jin Kang Yoojeong Noh Sunhwa Park Byeongha Ahn	2021/6/28
Exhaustive search feature selection method for fault detection of a compressor	대한기계학회 춘추학술대회	Mochammad Solichin Noh Yoojeong	2020/12