IA para Diagnóstico de Falhas em Máquinas Rotativas: Um estudo de Caso

Name: IA para Diagnóstico de Falhas em Máquinas Rotativas: Um estudo de Caso
Brand: Denys Pestana
SKU: 9786501292274
Price: 2090.0 INR
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ISBN13: 9786501292274
Binding: Paperback
Subject: N/A
Publisher: Denys Pestana
Publisher Imprint: Denys Pestana
Publication Date: February 7, 2025
Pages: 178
Original Price: USD 20.0
Language: Portuguese
Edition: N/A
Item Weight: 245 grams
BISAC Subject(s): Electrical

About The Book

Este livro apresenta uma abordagem preditiva para o diagnóstico de falhas em motores Diesel, combinando inteligência artificial e gêmeos digitais. Utilizando algoritmos baseados em IA, a obra oferece soluções práticas para estimar falhas com precisão, garantindo eficiência e confiabilidade operacional. Destinada a engenheiros e profissionais da indústria, esta é uma referência essencial para otimizar processos e reduzir custos na manutenção preditiva.

A crescente demanda por eficiência, durabilidade e confiabilidade em máquinas rotativas, como motores Diesel, destaca a importância de tecnologias avançadas para monitoramento e diagnóstico de falhas. Nos últimos anos, o uso de inteligência artificial (IA) tem se consolidado como uma ferramenta poderosa para enfrentar os desafios de manutenção preditiva e gestão operacional, permitindo identificar padrões complexos e prever falhas de maneira mais eficiente.

Este trabalho representa uma contribuição significativa ao unir técnicas de aprendizado de máquina com simulações baseadas em gêmeos digitais. Ele aborda de forma inovadora o diagnóstico e a caracterização de falhas em motores Diesel, uma área de extrema relevância para diversos setores industriais. Ao integrar algoritmos como Perceptron Multicamadas, Random Forest, Máquinas de Vetores de Suporte e Modelos de Regressão de Processo Gaussiano, este estudo propõe soluções práticas e robustas para a detecção precoce de falhas, assegurando maior disponibilidade operacional e reduzindo custos de manutenção.

Além disso, a criação de uma base de dados pública a partir de simulações realistas destaca o compromisso com a transparência e a colaboração científica, ampliando as possibilidades de avanço na área. Este recurso não apenas oferece um ambiente controlado para o treinamento de algoritmos, mas também permite que a comunidade acadêmica e industrial explore novos métodos e aplicações.

O autor apresenta, de forma clara e sistemática, desde a fundamentação teórica até os resultados práticos, construindo uma ponte sólida entre teoria e aplicação. A abordagem interdisciplinar e o uso de tecnologias de ponta reforçam a relevância deste trabalho, que certamente servirá de referência para pesquisadores, engenheiros e profissionais interessados em manutenção preditiva e diagnóstico de falhas.

Que este estudo inspire novos avanços e contribua para a construção de um futuro mais eficiente e sustentável no campo da automação e manutenção industrial.

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