This book discusses both reliability in reactive maintenance and an integrated, intelligent management strategy through the application of AI, machine learning, and soft computing. System reliability is becoming an increasingly critical concern due to the growing size and complexity of modern system designs. These systems encompass autonomous transportation networks and integrated power infrastructures. To address this paradigm shift, the disciplines of this book have united to integrate three fundamental domains: systematic System Engineering to ensure structural integrity, advanced reliability modeling to deliver precise prognoses, and cutting-edge computational intelligence (CI) to facilitate predictive decision-making.

The book's topical sections encompass subjects such as digital siblings, critical infrastructures (including IoT, peripheral devices, smart city environments, and related areas), and fundamental data-driven frameworks. Researchers, graduate students, and professionals engaged in the development and maintenance of resilient technology for the future are encouraged to read this book.

Provides a manual for improving system dependability by use of computational intelligence Delves into the core principles of reliability engineering, including metrics, and common reliability assessment methods Explores real-world case studies from various industries

Inhalt

Digital Twins for System Monitoring and Optimization.- Data Driven Reliability Modelling Utilizing AI Techniques for System Performance Optimization.- Modelling Software Vulnerability Management using the Epidemiological SIR Framework.- A Picture Fuzzy Numbers based COCOSO Approach to deal with different Software Code smells.- Availability and Reliability Analysis of An Interchangeable 3 Unit Series Parallel System.- Comparison of OSS reliability assessment methods based on deep learning considering the multi objective optimization.