Review of Fault Detection and Diagnosis Methods in Power Plants: Algorithms, Architectures, and Trends
Abstract
:1. Introduction
- Temperature sensors (RTDs, thermocouples)—essential for steam cycle regulation, heat exchanger monitoring, and anomaly detection in turbines or superheaters;
- Pressure transducers—used for monitoring boiler conditions, valve operations, and fluid flow dynamics;
- Robust interfaces with SCADA or DCS platforms;
- Response time guarantees;
- Explainability and auditability for operator trust;
- The type of power plant (e.g., thermal, nuclear, PV, hybrid);
- The sensor technology employed;
- The algorithmic paradigm (traditional, ML, DL, hybrid);
- The degree of diagnostic pipeline (detection, localization, resolution).
- A taxonomy of diagnostic system configurations;
- A methodological checklist for evaluating the robustness and completeness of FDD studies;
- A summary of key findings, supported by tables and figures.
2. Materials and Methods
2.1. Inclusion Criteria
- The paper explicitly addresses a fault, defect, or anomaly in a power generation system;
- It provides either a practical method or algorithmic framework for detection, diagnosis, or resolution;
- The study demonstrates applicability to thermal, gas, or hybrid power plants, or contains transferable principles;
- Both model-based and data-driven approaches (e.g., AI, fuzzy logic, Kalman filtering) were accepted if linked to defect-related performance;
- Articles include sufficient methodological or experimental detail to allow categorization along key analytical axes.
2.2. Exclusion Criteria
- Focused solely on general system optimization or design, without addressing specific faults;
- Provided no concrete methodology or algorithm (e.g., purely conceptual, editorial, or vision papers);
- Addressed systems unrelated to power generation (e.g., transportation, robotics, electronics) without translatable fault models;
- Lacked technical completeness (e.g., abstract-only entries, inaccessible full texts, or duplicates).
2.3. Labeling and Coding Process
- Fault type: mechanical, thermal, electrical, sensor-based, structural;
- System component: turbine, boiler, control unit, steam line, sensors;
- Diagnostic pipeline: detection only, detection + localization, full pipeline (detection–localization–solution);
- Algorithm category: no algorithm, shallow learning, deep learning, hybrid;
- Solution provided: yes/no;
- Source (MDPI, Elsevier, or Google Scholar), year, and article type (review/original research).
2.4. Analysis Methodology
- Algorithm type by fault category;
- Diagnostic pipeline by article source and year;
- Presence of solutions across fault types;
- Emergence of AI-based models over time.
3. Results
3.1. Power Plant Types and Sensor Technologies Covered
- MDPI showed strong representation in thermal and hybrid plant studies, with occasional references to waste-to-energy plants and distributed heating systems.
- Elsevier included the broadest variety of plant types, notably covering nuclear and combined cycle power plants in greater detail. Articles sourced from this database also discussed integration with renewable components, such as PV panels in hybrid systems.
- Google Scholar, while also dominated by thermal systems, included more exploratory studies involving wind turbines and microgrid configurations, possibly due to its indexing of academic theses and preprints.
- Thermal imaging and infrared sensors were especially notable in studies focusing on PV systems or power electronics (e.g., inverters and converters), particularly in MDPI and Google Scholar articles.
- Acoustic sensors and current signature analysis were featured in condition monitoring of rotating machinery (e.g., pumps and generators), with application to hydroelectric and nuclear systems.
- Advanced techniques such as multisensor fusion, drone-assisted monitoring, and unsupervised anomaly detection from signal data were more frequently seen in recent Elsevier papers.
3.2. Source and Year Distribution
3.3. Use of Diagnostic Algorithms
- Elsevier featured the highest concentration of studies with algorithmic content, where over 90% of the articles involved either classical statistical models or modern AI techniques.
- MDPI showed similar commitment to algorithm-driven research, especially in recent years, where supervised machine learning and hybrid frameworks became increasingly prominent.
- Google Scholar also included a wide range of studies with algorithms, although some works were less methodologically structured, possibly reflecting the inclusion of conference proceedings or academic theses.
3.4. Types of Algorithms Employed
3.5. Proposed Solutions for Fault Management
- Predictive Maintenance and Early Warning Models: These rely heavily on supervised and unsupervised learning to forecast equipment degradation and issue alerts before critical thresholds are exceeded. Such models often incorporate time-series analysis and recurrent neural networks (e.g., LSTM, GRU).
- Control-Oriented Interventions: Many studies proposed enhancements to existing PID controllers through adaptive techniques, such as Model Predictive Control (MPC), Robust MPC (RMPC), or fuzzy-PID hybrids. These strategies were particularly common in thermal systems where superheated steam regulation or turbine control is involved.
- Simulation-Based Evaluation and Validation: Several articles implemented simulation environments (e.g., MATLAB/Simulink, ANSYS, or custom-built platforms) to test the effectiveness of proposed solutions under faulted conditions. These environments allow researchers to tune parameters and assess system performance under realistic, yet safe conditions.
- Intelligent Recovery and Reconfiguration Logic: A smaller group of papers dealt with self-healing systems capable of rerouting functions or reconfiguring subsystems dynamically in response to detected faults. These were most often found in publications related to smart grids or distributed generation environments.
3.6. Process Scope: Partial vs. Complete
- Data Availability and System Access: Researchers often lack access to detailed process data or physical systems where controlled fault injection and response validation can be performed;
- Complexity of Localization and Control Design: While detection can be achieved with relatively general-purpose classifiers or statistical models, localization requires domain-specific models and often a deep understanding of plant topology and interdependencies;
- Disciplinary Separation: In many cases, the development of detection tools and control logic is split between different research communities (e.g., data science vs. process control), which can lead to fragmented solutions.
3.7. System-Level vs. Component-Level Focus
- Steam turbines and drum-type boilers, often associated with temperature or pressure instability;
- Electrical converters and control units, where cyber-physical vulnerabilities and signal interference were focal points;
- Service water pumps and hydraulic subsystems, especially in safety-critical environments such as nuclear power stations;
- PV modules and heat exchangers, particularly in renewable or hybrid energy systems.
3.8. Publication Type
- One hundred fifty (81.1%) were classified as original research articles,
- Thirty-three (17.8%) were categorized as review papers,
- Two (1.1%) were patents or patent-related literature.
3.9. Evolution of Algorithm Types over Time
3.10. Full Diagnostic Pipeline by Source
- Elsevier had the highest share of complete-process articles, with many studies incorporating both detection and intelligent control solutions.
- MDPI displayed a more balanced distribution, with several review articles and original studies addressing detection and partial localization.
- Google Scholar had the lowest percentage of full-process articles, often containing conceptual frameworks or limited empirical validation.
3.11. Co-Occurrence Between Solution and Full Diagnostic Pipeline
3.12. Distribution of Algorithm Types by Source
- Elsevier exhibits the highest concentration of deep learning techniques, including CNNs, LSTMs, and advanced neural network architectures. This reflects Elsevier’s focus on high-impact engineering and control journals, which often prioritize performance-driven, simulation-validated studies with application to industrial environments.
- MDPI demonstrates a more balanced distribution of algorithm types. Many articles from MDPI integrated traditional statistical approaches (e.g., PCA, FDI, fuzzy logic) with emerging machine learning techniques. Additionally, this source featured a notable presence of hybrid models, which leverage both physical system knowledge and data-driven prediction.
- Google Scholar, as an aggregator of diverse publication types—including theses, conference papers, and preprints—had a higher proportion of unspecified or traditional methods. Several of these works focused on exploratory modeling or conceptual frameworks without rigorous algorithmic definition, often due to resource limitations or the preliminary nature of the research.
3.13. Proposed Taxonomy of Diagnostic Systems
- Thermal power plants remain the dominant application context, often monitored through vibration or temperature sensors;
- PV systems tend to utilize thermal imaging and deep learning techniques, reflecting advances in remote, image-based fault detection;
- Nuclear and hydroelectric facilities employ pressure or acoustic sensors, typically analyzed via traditional or ML-based approaches;
- Hybrid and gas systems are associated with more complex data sources and tend to incorporate hybrid algorithms with higher process integration.
3.14. Illustrative Case Comparison of Diagnostic Frameworks
4. Discussion
4.1. Research Gaps Identified in the Literature
4.2. Methodological Trends and Technological Shifts
4.3. Practical Implications for Power Plant Monitoring
- Realistic noise levels and sensor failure scenarios;
- Variability in operating conditions;
- Integration with existing control systems;
- User-friendly interfaces and alerts.
- Adapted to specific plant architectures and sensor networks;
- Scalable across components and subsystems;
- Transparent and interpretable;
- Integrated with control and maintenance platforms;
- Validated under realistic operating conditions.
4.4. Recommendations for Future Research
- Detection of anomalies or abnormal patterns;
- Localization and identification of root causes;
- Proposal and simulation of corrective actions.
- Hybrid architectures that embed physical constraints into data-driven learning;
- Physics-informed neural networks that balance model flexibility with interpretability;
- Cross-validation with real industrial data to enhance robustness and generalization.
- Multidomain coupling (e.g., thermal-electrical interactions);
- Temporal propagation and delay effects;
- Networked diagnosis, integrating data from multiple subsystems.
- Post hoc interpretability tools (e.g., SHAP, LIME);
- Ante-hoc interpretable models (e.g., decision trees, symbolic regression);
- Operator-in-the-loop systems that combine human expertise with automated diagnosis.
- Public repositories for annotated sensor data;
- Benchmark challenges similar to those in computer vision or NLP;
- Agreement on key performance metrics (e.g., accuracy, latency, explainability, computational load).
- Field validation of diagnostic frameworks;
- Collaborative trials with utilities and plant operators;
- Iterative feedback from users to refine system usability and alerts.
4.5. Methodological Checklist for Robust Diagnostic Studies
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
Abbreviations
FDD | Fault Detection and Diagnosis |
ML | Machine Learning |
DL | Deep Learning |
CNN | Convolutional Neural Network |
RNN | Recurrent Neural Network |
LSTM | Long Short-Term Memory |
PCA | Principal Component Analysis |
ICA | Independent Component Analysis |
FDI | Fault Detection and Isolation |
HVAC | Heating, Ventilation, and Air Conditioning |
SVM | Support Vector Machine |
RF | Random Forest |
DC | Direct Current |
AC | Alternating Current |
PV | Photovoltaic |
SCADA | Supervisory Control and Data Acquisition |
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Power Plant Type | References |
---|---|
Thermal | [68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93] |
Nuclear | [94] |
Wind energy plant | [95,96,97] |
Other | [41,68,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118] |
Power Plant Type | References |
---|---|
Thermal | [1,2,3,4,5,7,12,13,14,17,18,20,25,26,28,29,32,33,34,35,36,37,38,42,48,49,50,51,52,54,55,56,58,59,61,62,63,65,66,119,120,121] |
Nuclear | [23,31,64] |
Wind energy plant | [46] |
Other | [6,8,9,10,11,15,16,19,21,22,24,27,30,39,40,41,43,44,45,47,53,57,60,67] |
Power Plant Type | References |
---|---|
Thermal | [122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170] |
Nuclear | [171,172] |
Wind energy plant | [173] |
Other | [143,174,175,176,177,178,179,180,181,182,183] |
Power Plant Type | Sensor Type | Algorithm | Diagnostic Pipeline | No. of Articles |
---|---|---|---|---|
Thermal | Vibration | Traditional | Detection Only | 18 |
Temperature | Machine Learning | Detection + Localization | 15 | |
Current | Deep Learning | Full Pipeline | 7 | |
PV | Thermal Imaging | Deep Learning | Full Pipeline | 12 |
Detection + Localization | 6 | |||
Nuclear | Pressure | Traditional | Detection Only | 10 |
Hybrid | Multisensor | Hybrid | Detection + Localization | 9 |
Hydroelectric | Acoustic | Machine Learning | Detection Only | 8 |
Gas | Infrared | Hybrid | Full Pipeline | 5 |
Method Type | Advantages | Limitations |
---|---|---|
Traditional (PCA, FDI) | Interpretable, efficient, low data needs | Inflexible, poor generalization |
Machine Learning (SVM, RF) | Scalable, works on structured data | Requires labeled datasets, sensitive to noise |
Deep Learning (CNN, LSTM) | High accuracy, handles unstructured/time-series data | Data-hungry, low explainability |
Hybrid (Physics + ML) | Combines domain knowledge and learning | Complex integration, underused in practice |
Criterion | Importance Level | Coverage in Literature (%) | Typical Gaps |
---|---|---|---|
1. Clearly defines fault types and system context | Critical | High (>85%) | Vague fault categorization or plant setup |
2. Applies a validated diagnostic algorithm | Critical | High (~90%) | No empirical or simulated validation |
3. Includes fault localization capability | Moderate | Medium (~45%) | Detection only, without spatial analysis |
4. Proposes a corrective or decision action | Critical | Medium (~69%) | No link to control or maintenance strategy |
5. Validated on real or realistic data | Essential | Low (~25–30%) | Synthetic/simulated data only |
6. Addresses sensor uncertainty and noise | Important | Low | Assumes ideal data conditions |
7. Ensures model interpretability or transparency | Important | Low | Uses black-box models without explanation |
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Maican, C.A.; Pană, C.F.; Pătrașcu-Pană, D.M.; Rădulescu, V.M. Review of Fault Detection and Diagnosis Methods in Power Plants: Algorithms, Architectures, and Trends. Appl. Sci. 2025, 15, 6334. https://doi.org/10.3390/app15116334
Maican CA, Pană CF, Pătrașcu-Pană DM, Rădulescu VM. Review of Fault Detection and Diagnosis Methods in Power Plants: Algorithms, Architectures, and Trends. Applied Sciences. 2025; 15(11):6334. https://doi.org/10.3390/app15116334
Chicago/Turabian StyleMaican, Camelia Adela, Cristina Floriana Pană, Daniela Maria Pătrașcu-Pană, and Virginia Maria Rădulescu. 2025. "Review of Fault Detection and Diagnosis Methods in Power Plants: Algorithms, Architectures, and Trends" Applied Sciences 15, no. 11: 6334. https://doi.org/10.3390/app15116334
APA StyleMaican, C. A., Pană, C. F., Pătrașcu-Pană, D. M., & Rădulescu, V. M. (2025). Review of Fault Detection and Diagnosis Methods in Power Plants: Algorithms, Architectures, and Trends. Applied Sciences, 15(11), 6334. https://doi.org/10.3390/app15116334