Systematic Review of Non-Destructive Testing Methods for Predictive Failure Analysis in Mechanical Systems

• Author(s): Enoch Oluwadunmininu Ogunnowo ; Musa Adekunle Adewoyin ; Joyce Efekpogua Fiemotongha ; Thompson Odion Igunma ; Adeniyi K. Adeleke
• Paper ID: 1708638
• Page: 207-222
• Published Date: 31-10-2020
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 4 Issue 4 October-2020

Abstract

Predictive failure analysis in mechanical systems is a crucial aspect of modern engineering maintenance and reliability management. Non-Destructive Testing (NDT) methods have emerged as essential tools for detecting material defects, structural anomalies, and potential failures without compromising the integrity of components. This systematic review evaluates the efficacy, limitations, and application domains of various NDT techniques—including ultrasonic testing (UT), magnetic particle testing (MT), radiographic testing (RT), eddy current testing (ECT), acoustic emission (AE), thermographic testing (TT), and visual inspection (VI)—in predictive failure analysis. The study follows the PRISMA methodology, employing a comprehensive literature search from databases such as Scopus, Web of Science, and IEEE Xplore from 2013 to 2020. A total of 79 peer-reviewed articles were selected based on relevance, citation quality, and methodological rigor. The findings highlight that ultrasonic testing and acoustic emission methods are highly effective in early crack detection and fatigue monitoring, especially in aerospace and pressure vessel applications. Radiographic and thermographic techniques excel in detecting internal voids and delamination in composite materials. Magnetic particle and eddy current testing are particularly suitable for surface and near-surface flaws in ferromagnetic and conductive materials, respectively. Visual inspection remains widely used due to its simplicity, though it often lacks the precision of other methods. Integrated approaches that combine multiple NDT techniques with artificial intelligence (AI) and machine learning (ML) algorithms are increasingly being adopted to enhance diagnostic accuracy and predictive capabilities. Despite their effectiveness, challenges such as operator dependency, high equipment costs, and limitations in defect quantification remain prevalent. The review recommends a hybrid framework that leverages sensor fusion, digital twins, and real-time data analytics for robust predictive maintenance. Furthermore, the review calls for the standardization of data interpretation protocols and the adoption of automated systems to minimize human error. This systematic review contributes to the growing body of knowledge aimed at improving mechanical system reliability, minimizing downtime, and optimizing lifecycle costs. It provides a foundation for further research into adaptive, intelligent NDT systems for Industry 4.0 environments, where continuous monitoring and predictive analytics are essential.

Keywords

Non-Destructive Testing, Predictive Failure Analysis, Mechanical Systems, PRISMA Review, Ultrasonic Testing, Acoustic Emission, Thermographic Testing, AI-Driven NDT

Citations

IRE Journals:
Enoch Oluwadunmininu Ogunnowo , Musa Adekunle Adewoyin , Joyce Efekpogua Fiemotongha , Thompson Odion Igunma , Adeniyi K. Adeleke "Systematic Review of Non-Destructive Testing Methods for Predictive Failure Analysis in Mechanical Systems" Iconic Research And Engineering Journals Volume 4 Issue 4 2020 Page 207-222

IEEE:
Enoch Oluwadunmininu Ogunnowo , Musa Adekunle Adewoyin , Joyce Efekpogua Fiemotongha , Thompson Odion Igunma , Adeniyi K. Adeleke "Systematic Review of Non-Destructive Testing Methods for Predictive Failure Analysis in Mechanical Systems" Iconic Research And Engineering Journals, 4(4)