Off-Board Diagnostics is a term used for the diagnostics of planes, vehicles and other machines. It was created by Nassim Khaled in 2018 and first published in Digital Twin Development and Deployment on the Cloud Due to be published later this year.

Motivated by Max 737 issues and the subsequent crashes of airplanes [2], Nassim and his co-authors wanted to make the process of predicting and detecting failures of vehicles, machines and processes more robust.

Unlike on-board diagnostics [3] where the diagnostic decision takes place in the controller of the machine, Nassim proposes having the decision made in the cloud [1]. The main advantage of Off-BD compared to On-Board Diagnostics (OBD) is the computational power in the cloud. Nassim proposes using physics-based models that can be used as virtual copies of the physical machine. These virtual copies, or digital twins, can then be used as a reference to check if the machine is operating properly.

Figure 1 demonstrates the concept of a turbine asset and its virtual replica (i.e. the model) and how their outputs can be compared to diagnose a deterioration in the performance of the physical asset. Nassim and his co-authors propose a process to streamline Off-BD. Figure 2 shows the general steps involved in designing Off-BD [1].

About the book

Digital Twin Development and Deployment in the Cloud promotes a physics-based approach to the field of digital twins. Through usage of multiphysics models running on the cloud, significant improvement to the diagnostics and prognostic of systems can be attained. The book draws a clear definition of digital twins. It helps business leaders clearly identify what it is and what value it brings Digital Twin Development and Deployment in the Cloud refines the term digital twins. The book outlines the key elements needed to deploy digital twins. This includes the hardware and software tools needed. Special attention is paid to the process of developing and deploying the multi-physics models of the digital twins.

Key Features:

• Provides a high-level overview of digital twins and its underutilization in the field of asset management and maintenance
• Proposes a streamline process to create digital twins for a wide variety of applications using Matlab® Simscape™
• Deploys developed digital twins on Amazon Web Services
• Includes Matlab and Simulink codes available for free download on Matlab central
• Popular prototyping hardwares such as Arduino and Raspberry Pi are used for demonstration of the concepts proposed in the book

References:

[1] https://www.elsevier.com/books/digital-twin-development-and-deployment-on-the-cloud/khaled/978-0-12-821631-6

[2] https://en.wikipedia.org/wiki/Boeing_737_MAX_groundings

[3] https://en.wikipedia.org/wiki/On-board_diagnostics