Review of Fault Detection and Diagnosis Methods in Power Plants: Algorithms, Architectures, and Trends

• Published in: Applied sciences Vol. 15; no. 11; p. 6334
• Main Authors: Maican, Camelia Adela, Pană, Cristina Floriana, Pătrașcu-Pană, Daniela Maria, Rădulescu, Virginia Maria
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2025
• Subjects: Algorithms; Artificial intelligence; Decision making; Deep learning; Electric power-plants; fault detection and diagnosis; hybrid models; Internet/Web search services; Liu, Timothy; Machine learning; Nuclear energy; Nuclear power plants; Power plants; sensor technologies; Sensors
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app15116334

Fault detection and diagnosis (FDD) in power plant systems is a rapidly evolving field driven by the increasing complexity of industrial infrastructure and the demand for reliability, safety, and predictive maintenance. This review presents a structured and data-driven synthesis of 185 peer-reviewed articles, sourced from journals indexed in MDPI and Elsevier, as well as through the Google Scholar search engine, published between 2019 and 2025. The study systematically classifies these articles by plant type, sensor technology, algorithm category, and diagnostic pipeline (detection, localization, resolution). The analysis reveals a significant transition from traditional statistical methods to machine learning (ML) and deep learning (DL) models, with over 70% of recent studies employing AI-driven approaches. However, only 30.3% of the articles addressed the full diagnostic pipeline and merely 17.3% targeted system-level faults. Most research remains component-focused and lacks real-world validation or interpretability. A novel taxonomy of diagnostic configurations, mapping system types, sensor use, algorithmic strategy, and functional depth is proposed. In addition, a methodological checklist is introduced to evaluate the completeness and operational readiness of FDD studies. Key findings are summarized in a comparative matrix, highlighting trends, gaps, and inconsistencies across publication sources. This review identifies critical research gaps—including the underuse of hybrid models, lack of benchmark datasets, and limited integration between detection and control layers—and offers concrete recommendations for future research. Combining a thematic and quantitative approach, this article aims to support researchers, engineers, and decision-makers in developing more robust, scalable, and transparent diagnostic systems for power generation infrastructure.