This paper develops a dynamic Knowledge Graph (KG)-augmented Large Language Model (LLM) framework integrated with a Bidirectional Encoder Representations from Transformers-Cascade Relation Extraction (BERT-CasRel) architecture to address key challenges in aviation equipment fault diagnosis, including unstructured maintenance text processing, ambiguous domain semantics, static knowledge constraints, and limited explainable reasoning capabilities. The study first constructs a domain-specific aviation ontology and adopts a context-enhanced BERT-CasRel model to extract high-quality entity–relation triples from maintenance logs and technical documentation. These structured triples populate a dynamic aviation fault KG that supports hierarchical causal inference, subgraph refinement, and in-context learning for adaptive knowledge updating. Structured domain prompting enables bidirectional interaction between LLMs and the KG, facilitating traceable fault chain analysis and accurate root-cause diagnosis. Evaluated on CFM56-5 aero-engine turbine blade fault cases, the BERT-CasRel model achieves a triple extraction F1-score of 0.968, while the integrated LLM–KG framework attains fault diagnosis accuracy exceeding 95%. Benchmarking against conventional and state-of-the-art methods confirms the framework's superiority in extraction accuracy, diagnostic precision, interpretability, and scalability. It delivers strong cross-domain generalization and computational efficiency, mitigates LLM hallucinations, complies with aviation regulations, and provides an interpretable, scalable diagnostic solution while acknowledging limitations in large-scale knowledge iteration and full industrial deployment.

Spectral entropy (SE) is a promising nonlinear measure for detecting dynamic variations in vibration signals acquired from rolling element bearings (REB). However, in real world scenarios, characteristic spectral features relating to REB fault gets concealed by unwanted frequency components due to the association of heavy environmental noise. Consequently, original SE not only fails to detect incipient REB fault but also fails to monitor the progression of the fault along with predicting the remaining useful life of the faulty REB. Aiming to address aforementioned problems, in this paper, firstly, characteristic spectral features of REB fault is revealed by calculating the spectrum of the adaptively demodulated weighted squared envelope of the corresponding vibration signal. Subsequently, instead of using classical Shannon entropy theory corresponding to original SE, comprehensive prognosis of the analyzed REB health is achieved through the information quantification of the calculated spectrum by incorporating dispersion entropy (DE) theory. In this context, the proposed measure is named as adaptively demodulated dispersive spectral entropy (ADDSE). Two different run to failure REB data have been utilized to verify the effectiveness of the proposed ADDSE. Results show that the proposed ADDSE not only can overcome the limitations of the original SE in comprehensive dynamic prognosis of REB health but also demonstrate superior performance in compare to other conventional measures such as original DE and root mean square (RMS); advanced version of spectral entropy namely cumulative spectrum distribution entropy (CSDE) and three dimensional holo hilbert spectral entropy (MHHSE3D); alternative sparsity based measure namely Gini index (GI).