Pin Lim

1.4k total citations · 1 hit paper
8 papers, 1.1k citations indexed

About

Pin Lim is a scholar working on Control and Systems Engineering, Safety, Risk, Reliability and Quality and Artificial Intelligence. According to data from OpenAlex, Pin Lim has authored 8 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Control and Systems Engineering, 4 papers in Safety, Risk, Reliability and Quality and 2 papers in Artificial Intelligence. Recurrent topics in Pin Lim's work include Machine Fault Diagnosis Techniques (6 papers), Fault Detection and Control Systems (5 papers) and Reliability and Maintenance Optimization (4 papers). Pin Lim is often cited by papers focused on Machine Fault Diagnosis Techniques (6 papers), Fault Detection and Control Systems (5 papers) and Reliability and Maintenance Optimization (4 papers). Pin Lim collaborates with scholars based in Singapore, Australia and China. Pin Lim's co-authors include Kay Chen Tan, A. K. Qin, Chong Zhang, Chi-Keong Goh, Mohamed Habibullah, Feng Yang, Zhao Xu, Sivakumar Nadarajan, P.S. Dutta and Yi Zhao and has published in prestigious journals such as IEEE Transactions on Industrial Electronics, IEEE Transactions on Cybernetics and IEEE Transactions on Neural Networks and Learning Systems.

In The Last Decade

Pin Lim

8 papers receiving 1.1k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Multiobjective Deep Belief Networks Ensemble for Remaining Useful Life Estimation in Prognostics

2016 669 citations Chong Zhang, Pin Lim et al. IEEE Transactions on Neural Networks and Learning Systems profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Pin Lim Singapore	7	784	335	271	255	162	8	1.1k
Mei Yuan China	11	666 0.8×	213 0.6×	261 1.0×	152 0.6×	185 1.1×	40	1.1k
Edward Balaban United States	15	882 1.1×	220 0.7×	237 0.9×	203 0.8×	109 0.7×	34	1.2k
Zheng Zhou China	17	888 1.1×	186 0.6×	437 1.6×	204 0.8×	291 1.8×	45	1.3k
Yuanhong Chang China	14	659 0.8×	152 0.5×	323 1.2×	177 0.7×	146 0.9×	21	940
Gang Niu China	16	456 0.6×	152 0.5×	180 0.7×	123 0.5×	132 0.8×	46	841
Ahmed Farahat United States	15	520 0.7×	201 0.6×	149 0.5×	341 1.3×	91 0.6×	34	1.0k
Shuai Zheng China	12	480 0.6×	174 0.5×	195 0.7×	158 0.6×	94 0.6×	25	960
Pierre Dersin France	12	486 0.6×	170 0.5×	200 0.7×	126 0.5×	93 0.6×	48	832
Huawei Wang China	17	653 0.8×	156 0.5×	354 1.3×	132 0.5×	224 1.4×	67	1.1k

Countries citing papers authored by Pin Lim

Since Specialization

Citations

This map shows the geographic impact of Pin Lim's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Pin Lim with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pin Lim more than expected).

Fields of papers citing papers by Pin Lim

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Pin Lim. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Pin Lim. The network helps show where Pin Lim may publish in the future.

Co-authorship network of co-authors of Pin Lim

This figure shows the co-authorship network connecting the top 25 collaborators of Pin Lim. A scholar is included among the top collaborators of Pin Lim based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Pin Lim. Pin Lim is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

8 of 8 papers shown

1.

Lim, Pin, Chi-Keong Goh, & Kay Chen Tan. (2018). A Novel Time Series-Histogram of Features (TS-HoF) Method for Prognostic Applications. IEEE Transactions on Emerging Topics in Computational Intelligence. 2(3). 204–213. 13 indexed citations

2.

Lim, Pin, Chi-Keong Goh, & Kay Chen Tan. (2016). A time window neural network based framework for Remaining Useful Life estimation. 1746–1753. 51 indexed citations

3.

Zhang, Chong, Pin Lim, A. K. Qin, & Kay Chen Tan. (2016). Multiobjective Deep Belief Networks Ensemble for Remaining Useful Life Estimation in Prognostics. IEEE Transactions on Neural Networks and Learning Systems. 28(10). 2306–2318. 669 indexed citations breakdown →

4.

Lim, Pin, Chi-Keong Goh, & Kay Chen Tan. (2016). Evolutionary Cluster-Based Synthetic Oversampling Ensemble (ECO-Ensemble) for Imbalance Learning. IEEE Transactions on Cybernetics. 47(9). 2850–2861. 119 indexed citations

5.

Yang, Feng, et al.. (2016). Health Index-Based Prognostics for Remaining Useful Life Predictions in Electrical Machines. IEEE Transactions on Industrial Electronics. 63(4). 2633–2644. 183 indexed citations

6.

Lim, Pin, Chi-Keong Goh, Kay Chen Tan, & P.S. Dutta. (2015). Multimodal Degradation Prognostics Based on Switching Kalman Filter Ensemble. IEEE Transactions on Neural Networks and Learning Systems. 28(1). 136–148. 60 indexed citations

7.

Lim, Pin, Chi-Keong Goh, Kay Chen Tan, & P.S. Dutta. (2014). Estimation of Remaining Useful Life Based on Switching Kalman Filter Neural Network Ensemble. Annual Conference of the PHM Society. 6(1). 30 indexed citations

8.

Luo, Ming, et al.. (2008). Categorical data analysis for equipment failure prediction. 1473–1478. 2 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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