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.

Multimodal sensor fusion is increasingly used to improve observability for fault diagnosis and prognostics, enabling Remaining Useful Life estimation in complex mechatronic and robotic systems. Yet, real-world deployments remain vulnerable to sensor faults and data integrity issues—including bias and drift, miscalibration, dropouts, saturation, cross-talk, time desynchronization, and domain shift—which can propagate through fusion pipelines and lead to optimistic validation and poor generalization. These challenges are particularly consequential in safety- and health-adjacent applications such as collaborative robots, wearable/rehabilitation devices, and human-centric mechatronic systems where decisions based on faulty sensing may affect both reliability and user safety. This review synthesizes the state of the art on (i) sensor fault taxonomies and fault models relevant to multimodal fusion, (ii) fault-aware fusion strategies spanning data-, feature-, and decision-level integration, and (iii) how sensor faults and uncertainty impact diagnosis and remaining-life estimators. We will conduct a systematic scoping review of peer-reviewed literature, extracting sensor modalities, fault characterization or injection protocols, fusion architectures, validation settings (simulation, hardware-in-the-loop, bench, and in-field/on-body studies), and reporting completeness. Beyond summarizing methods, we provide practical reporting standards for sensor-fusion-based diagnosis and prognostics, including a minimum disclosure set covering synchronization, fault ground truth, missingness handling, leakage controls, uncertainty calibration, and task-relevant metrics. Reusable checklists and evidence tables are included to support more comparable, reproducible, and deployment-ready research.

In recent decades, the field of physical rehabilitation, with the help of robotic systems that aid the population of any age with locomotor difficulties, has been evolving rapidly. Several robotic exoskeleton systems of the lower limbs have been proposed in the patent literature and some are even commercially available. Given the above, we are asking ourselves at the end of the COVID-19 pandemic: how much has this pandemic affected both the publication of patents and the application of new ones? How has new patents’ publication volume or application in robotic exoskeleton systems changed? We hypothesize that this pandemic has caused a reduction in the volume of new applications and possibly publications. We compare pandemic analysis and the last decade’s analysis to answer these questions. In this study, we used a set of statistical tests to see if there were any statistically significant changes. Our results show that the pandemic had at least one effect on applying for new patents based on the information analyzed from the three databases examined.

This review synthesises fault detection and diagnosis (FDD) methods for robotic actuation in healthcare, where precise, compliant, and safe physical human–robot interaction (pHRI) is essential. Actuator families—harmonic-drive electric transmissions, series-elastic designs, Cable/Bowden mechanisms, permanent-magnet synchronous motors (PMSM), and force–torque-sensed architectures—are mapped to characteristic fault classes and to sensing, residual-generation, and decision pipelines. Four methodological families are examined: model-based observers/parity relations, parameter-estimation strategies, signal-processing with change detection, and data-driven pipelines. Suitability for pHRI is assessed by attention to latency, robustness to movement artefacts, user comfort, and fail-safe behaviour. Aligned with ISO 14971 and the IEC 60601/80601 series, a validation framework is introduced, with reportable metrics—time-to-detect (TTD), minimal detectable fault amplitude (MDFA), and false-alarm rate (FAR)—at clinically relevant thresholds, accompanied by a concise reporting checklist. Across 127 studies (2016–2025), a pronounced technology-dependent structure emerges in the actuator-by-fault relationship; accuracy (ACC/F1) is commonly reported, whereas MDFA, TTD, and FAR are rarely documented. These findings support actuation-aware observers and decision rules and motivate standardised reporting beyond classifier accuracy to enable clinically meaningful, reproducible evaluation in contact-rich pHRI.

This paper presents a new conception and analyzes a hyperredundant continuous robot (continuous style manipulator), drive system, and control strategy. The robot includes ten flexible segments and can be extended to several components as needed. The chosen hyper-redundant robot has a continuous infinite hybrid structure (HHRIC), based on hydraulic control with a rheological element. This system combines the advantage of a joint-level drive with a lightweight construction similar to the base-driven robots. It is suitable for tasks such as wiring in hard-toreach areas (caves, subaccounts, steep areas), transportation of fluids or food to areas affected by natural disasters (people buried under ruins), exploration in difficult areas (speleological research). Generally, the control algorithms for hyper-redundant robots are specific to the robots’ constructive particularities to which they have applied and the environment in which they operate. Experimental results validate the proposal robot design and control strategies in virtual reality. As a result, it is concluded that hyper-redundant robots and immersive technologies should play an essential role soon in automated and teleoperation applications.

Soft wearable actuators offer compliant, lightweight assistance for rehabilitation and motor support, but their translation beyond controlled laboratories depends on reliable sensing, fault management, and adaptive control. This systematic review maps the evidence across three linked domains: fault detection and diagnosis (FDD), sensing, and intelligent control. Owing to marked heterogeneity in actuator types, validation settings, and reported outcomes, the evidence was synthesised narratively and described using publication counts and proportions rather than pooled performance estimates. The study-level supplementary appraisal covers all 106 publications included in the review. Four normative standards were retained within this total as normative context and were marked as not applicable for empirical methodological appraisal; they were not subtracted from the corpus count. Across the 106 included records, 40 were classified as direct, 57 as adjacent, five as indirect, and four as normative context. The FDD subset was especially limited: nine records provided adjacent evidence and five were indirect under the strict soft-wearable rehabilitation criterion. The review therefore identifies soft-actuator-specific FDD benchmarks, multimodal health-state estimation, and fault-aware control as actionable priorities, while distinguishing these structural gaps from constraints expected in a technologically young field. The completed review was retrospectively registered in the OSF Registries on 21 July 2026 using the Open-Ended Registration template.