Diagnosis of Rotor Component Shedding in Rotating Machinery: A Data-Driven Approach.

The potential for rotor component shedding in rotating machinery poses significant risks, necessitating the development of an early and precise fault diagnosis technique to prevent catastrophic failures and reduce maintenance costs. This study introduces a data-driven approach to detect rotor component shedding at its inception, thereby enhancing operational safety and minimizing downtime. Utilizing frequency analysis, this research identifies harmonic amplitudes within rotor vibration data as key indicators of impending faults. The methodology employs principal component analysis (PCA) to orthogonalize and reduce the dimensionality of vibration data from rotor sensors, followed by k-fold cross-validation to select a subset of significant features, ensuring the detection algorithm's robustness and generalizability. These features are then integrated into a linear discriminant analysis (LDA) model, which serves as the diagnostic engine to predict the probability of rotor component shedding. The efficacy of the approach is demonstrated through its application to 16 industrial compressors and turbines, proving its value in providing timely fault warnings and enhancing operational reliability.

Evaluating apoenzyme-coenzyme-substrate interactions of methane monooxygenase with an engineered active site for electron harvesting: a computational study.

Low-temperature methane oxidation is one of the greatest challenges in energy research. Although methane monooxygenase (MMO) does this catalysis naturally, how to use this biocatalyst in a fuel cell environment where the electrons generated during the oxidation process is harvested and used for energy generation has not yet been investigated. A key requirement to use this enzyme in a fuel cell is wiring of the active site of the enzyme directly to the supporting electrode. In soluble MMO (sMMO), two cofactors, i.e., nicotinamide adenine di-nucleotide (NAD+) and flavin adenine dinucleotide (FAD) provide opportunities for direct attachment of the enzyme system to a supporting electrode. However, once modified to be compatible with a supporting metal electrode via FeS functionalization, how the two cofactors respond to complex binding phenomena is not yet understood. Using docking and molecular dynamic simulations, modified cofactors interactions with sMMO-reductase (sMMOR) were studied. Studies revealed that FAD modification with FeS did not interfere with binding phenomena. In fact, FeS introduction significantly improved the binding affinity of FAD and NAD+ on sMMOR. The simulations revealed a clear thermodynamically more favorable electron transport path for the enzyme system. This system can be used as a fuel cell and we can use FeS-modified-FAD as the anchoring molecule as opposed to using NAD+. The overall analysis suggests the strong possibility of building a fuel cell that could catalyze methane oxidation using sMMO as the anode biocatalyst.