Scalable AI Models for Predictive Failure Analysis in Cloud-Based Asset Management Systems

@article{1706542,
      author = {Rajesh Ojha, Dr. Lalit Kumar},
      title = {Scalable AI Models for Predictive Failure Analysis in Cloud-Based Asset Management Systems},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {8},
      number = {5},
      pages = {1040-1056},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1706542.pdf},
      abstract = {The increasing complexity of cloud-based asset management systems demands advanced solutions for ensuring operational reliability and minimizing downtime. This paper explores the development and implementation of scalable artificial intelligence (AI) models for predictive failure analysis within these systems. Leveraging machine learning and deep learning algorithms, the proposed models analyze real-time data streams from asset operations to predict potential failures before they occur. By integrating these models with cloud platforms, the system can continuously adapt to new data and operational conditions, offering robust insights into asset health and performance. We discuss the architectural design, scalability challenges, and the benefits of using AI for proactive maintenance, resource optimization, and minimizing disruptions in critical asset-dependent operations. The paper also highlights the application of explainable AI techniques for increased transparency in model predictions, ensuring the interpretability of decisions in high-stakes environments.},
      keywords = {AI models, predictive failure analysis, cloud-based systems, asset management, machine learning, deep learning, scalability, proactive maintenance, explainable AI.},
      month = {November},
  }