Title: Trends and developments in machine learning-based signature analysis: a bibliometric and thematic content review

Authors: Indrajeet Mahto; Vaibhav S. Narwane; Seema Nagrani

Addresses: Mechanical Engineering Department, K.J. Somaiya School of Engineering (Formerly K.J. Somaiya College of Engineering), Somaiya Vidyavihar University, Mumbai – 400077, India; Department of Mechanical Engineering, Thakur Shyamnarayan Engineering College, Kandivali East, Mumbai, Maharashtra, 400101, India ' Mechanical Engineering Department, K.J. Somaiya School of Engineering (Formerly K.J. Somaiya College of Engineering), Somaiya Vidyavihar University, Mumbai – 400077, India ' Mechanical Engineering Department, K.J. Somaiya School of Engineering (Formerly K.J. Somaiya College of Engineering), Somaiya Vidyavihar University, Mumbai – 400077, India; Department of Mechanical Engineering, Thakur College of Engineering and Technology, Mumbai – 400101, India

Abstract: Machine learning plays a crucial role in vibration analysis by enhancing the ability to detect, diagnose, and predict faults in machinery and structures. The primary purpose of this study is to provide brief information about machine learning (ML) for signature analysis (SA). It has conducted a bibliometric literature review on machine learning for signature analysis, in which 498 research articles have been analysed. These articles are highly investigated using the title-abstract-keyword (TAK) principle, and it was observed that 170 articles are suitable for performing the content analysis. This study shows how industries adopt machine learning for signature analysis and how much it is spread in developed and developing countries. With the help of content analysis, researchers know the types of studies, applications, and ML algorithms related to machine learning for signature analysis. It helps the practitioner and decision-maker to implement machine learning for signature analysis. This study is limited to research articles published from 2018 to 2024. Only English-language articles are included here. This is one of the few studies that present a bibliometric literature review and the content analysis of machine learning for signature analysis. This study highlights the implications based on the researchers and industrial professionals.

Keywords: signature analysis; vibration analysis; machine learning.

DOI: 10.1504/IJBBM.2025.153056

International Journal of Bibliometrics in Business and Management, 2025 Vol.4 No.4, pp.368 - 409

Received: 29 Jan 2025
Accepted: 19 Jun 2025
Published online: 20 Apr 2026 *

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