Collection Overview
Scientific Reports has launched a Guest-Edited Collection on Data-driven predictive maintenance of electronics.
Data-driven predictive maintenance of electronics is an emerging research field that leverages data analytics, machine learning, and sensor technologies to anticipate failures in electronic systems before they occur. Unlike traditional scheduled maintenance or reactive repair strategies, predictive maintenance uses real-time and historical performance data—such as voltage fluctuations, temperature changes, current irregularities, and usage patterns—to model degradation pathways and identify early indicators of component failure. The objective is to enhance reliability, minimize downtime, and extend the operational lifespan of electronic systems, particularly in critical sectors such as aerospace, automotive, telecommunications, and industrial automation. Recent advances in the field have been driven by the integration of high-resolution sensors, edge computing, and sophisticated algorithms capable of handling the nonlinear, multivariate, and often sparse datasets characteristic of electronic device behavior. Simultaneously, self-diagnosing circuits and system-on-chip solutions are being developed to enable localized fault detection and autonomous decision-making.
This will be a Collection of original research papers and will be open for submissions from all authors – on the condition that the manuscripts fall within the scope of the Collection and of Scientific Reports more generally. We are welcoming submissions until 17th August 2026.
Why is this Collection important?
"Data-driven predictive maintenance of electronics integrates reliability modelling, condition monitoring, and artificial intelligence to anticipate failures in power electronic converters, renewable energy interfaces, smart grid components, and industrial systems. With increasing electrification and renewable integration, electronic reliability has become mission-critical. This collection is timely as advanced machine learning, signal processing, and data analytics now enable accurate fault diagnosis and remaining useful life estimation. I am excited because it bridges theory and real-world deployment, accelerating intelligent, self-aware systems. The impact will be a more resilient, cost-effective, and sustainable infrastructure. Researchers should submit to contribute cutting-edge methodologies and gain visibility within a focused, high-impact platform dedicated to next-generation electronic reliability."
- Dr. Kumari Sarita, Guest Editor
Why submit to a collection?
Collections like this one help promote high-quality science. They are led by Guest Editors, who are experts in their fields, and In-House Editors and are supported by a dedicated team of Commissioning Editors and Managing Editors at Springer Nature. Collection manuscripts typically see higher citations, downloads, and Altmetric scores and provide a one-stop-shop on a cutting-edge topic of interest.
Who is involved?
Guest Editors:
- Anoop Balakrishnan Kadan, PhD, Nehru College of Engineering and Research Centre, India
- Kumari Sarita, PhD, Government Engineering College Aurangabad, India
- Qichun Zhang, PhD, Buckinghamshire New University, United Kingdom
Internal Team:
- In-House Editor: Chenyu Wang, Scientific Reports, USA
- Commissioning Editor: Quintina Dawson, Fully OA Brands, Springer Nature, UK
- Managing Editor: Eleanor Smith, Fully OA Brands, Springer Nature, UK
How can I submit my paper?
Visit the Collection page for more information on the Collection, and how to submit your article.