The article presents the topic of propeller damage detection on unmanned multirotor drones. Propeller damage is dangerous as it can negatively affect the flight of a drone or lead to hazardous situations. The article proposes a non-invasive method for detecting damage within the drone’s hardware, which utilizes existing sensors in the Internal Measuring Unit (IMU) to classify propeller damage. The classification is performed by using the Bayesian Gaussian Mixture Model (BGMM). In the field of drone propeller damage detection, there is a significant issue of data scarcity due to traditional methods often involving invasive and destructive testing, which can lead to the loss of valuable equipment and high costs. Bayesian methods, such as BGMM, are particularly well-suited to address this issue by effectively handling limited data through incorporating prior knowledge and probabilistic reasoning. Moreover, using the IMU for damage detection is highly advantageous as it eliminates the need for additional sensors, reducing overall costs and preventing added weight that could compromise the drone’s performance. IMUs do not require specific environmental conditions to function properly, making them more versatile and practical for real-world applications.

This paper presents a robust framework for brushless DC (BLDC) motor fault diagnosis by comparing two data fusion strategies: decision-level stacking and data-level fusion. The study uses features extracted from current and rotational speed signals to classify four conditions: healthy, mechanical damage, electrical damage, and combined faults. Decision-level fusion employs a meta-learner to integrate predictions from specialized base models, while data-level fusion utilizes a single Random Forest classifier trained on all combined features. This comparative analysis aims to provide insights into the optimal fusion strategy for developing highly accurate BLDC motor diagnostic systems, with particular relevance for predictive maintenance applications, where early and reliable fault identification is essential.

Industrial diagnostic systems increasingly combine machine-learning classifiers, temporal models, uncertainty surrogates, and explanation methods. Their operational use requires a separate mechanism that determines whether the available evidence is valid, mutually consistent, sufficient for a recommendation, or should instead lead to abstention or human escalation. This paper introduces the Diagnostic Evidence Governance Agent (DEGA), a deterministic agent that separates evidence generation from workflow governance. DEGA operates on immutable evidence objects, follows an explicit finite-state workflow, applies a higher-priority SafetyGuard, and records a hash-linked audit trace supporting deterministic replay. The architecture is demonstrated in an offline case study using eight DUDU-BLDC acquisitions, 200 non-overlapping windows, 28 current- and speed-based features, three classifiers, and four spline representations. Across 16 dependent acquisition-disjoint assignment views, mean acquisition-level macro-F1 was 0.875 for Logistic Regression and 0.667 for both tree-based classifiers. The pipeline produced 768 complete EvidenceBundles with reproducible scientific hashes. Explanation rankings were assignment-sensitive, with a mean top-10 Jaccard similarity of 0.318. Twelve representative DEGA executions ended in three escalations and nine no-decision outcomes, with complete audit chains and deterministic replay. The study demonstrates auditable, fail-closed governance of AI-supported diagnostic evidence without delegating workflow control to an AI agent.

Industrial diagnostic systems require a governance mechanism that determines whether heterogeneous evidence is admissible, mutually consistent, and sufficient for an automatic action. This paper presents the Diagnostic Evidence Governance Agent (DEGA), a deterministic governance layer built from explicit finite-state machine states, replaceable routing policies, an authoritative SafetyGuard, and hash-linked audit with deterministic replay. The bounded DUDU-BLDC case study retains diagnostic evidence from eight acquisitions and extends the evaluation to governance-profile sensitivity and state coverage. Across 15,360 retained real-evidence case-policy routes, no automatic recommendation was issued; 9216 routes reached Decision Check; and the mandatory-explanation rule triggered 7904 times. Under predeclared contract-satisfied simulated profiles, 2924 recommendations and 12,436 escalations were produced, with no recommended route violating the internal governance-consistency proxy. The results show that explanation availability is a binding governance condition and that recommendation-path reachability can be evaluated without weakening the remaining SafetyGuard gates; the simulation is restricted to explanation availability and admissibility metadata and does not validate feature attribution, recommendation correctness, or industrial deployment.

Induction motors constitute a major share of industrial drives, making reliable fault detection essential for maintaining operational continuity. This work develops a Bayesian classifier for identifying rotor-bar damage using start-up current measurements represented in the frequency domain. The spectra are modelled as smooth functional curves using a hierarchical B-spline formulation, and posterior sampling provides a generative mechanism for augmenting scarce labelled data. Classification is performed using a Bayesian Gaussian mixture model, where each prediction is obtained by averaging over thousands of posterior samples, yielding stable and interpretable probability estimates. In experimental evaluation, the proposed approach achieves consistent separation between healthy and faulty motors across repeated training runs, correctly identifying all test cases in the binary classification setting and exhibiting more stable probability estimates than logistic and soft-max regression under limited labelled data. The model additionally signals atypical responses for unmodelled faults, indicating potential for anomaly detection. These findings highlight the suitability of Bayesian functional modelling as a reliable tool for induction motor condition monitoring.