We survey the state-of-the-art in offboard diagnostics for vehicles, their occupants, and environments, with particular focus on vibroacoustic approaches. We identify promising application areas including data-driven management for shared mobility and automated fleets, usage-based insurance, and vehicle, occupant, and environmental state and condition monitoring. We close by exploring the particular application of vibroacoustic monitoring to vehicle diagnostics and prognostics and propose the introduction of automated vehicle- and context-specific model selection as a means of improving algorithm performance, e.g. to enable smartphone-resident diagnostics. Towards this vision, four strong-performing, interdependent classifiers are presented as a proof-of-concept for identifying vehicle configuration from acoustic signatures. The described approach may serve as the first step in developing "universal diagnostics," with applicability extending beyond the automotive domain.

Surveying Off-Board and Extra-Vehicular Monitoring and Progress Towards Pervasive Diagnostics

Internet of things (IoT) is developing much fast than any other technology. IoT framework can be called as a gathering of smart gadgets that connect on a shared premise to satisfy a shared objective. IoT systems may use different processing and communication architectures, technologies, and design methodologies. This selection is based on the target it must be achieved. This review work focussed on the step into the different communication protocols used in IoT systems.

Review on Online Monitoring and Control in Industrial Automation–An IoT Perspective

Gamification and the Internet of Things will impact education in a manner previously impossible. Gamified tools built upon game engines will offer opportunities in the development of innovative virtual labs and other interactive experiences for students. The Internet of Things can provide real-world data visualization in near real-time. Combining these fields, it becomes possible to develop realistic and safe simulation environments with real life data or triggering systems taking the student learning experience in whole new levels. 

Gamification and the Internet of Things in Education

Digitalization, analysis and optimization of discrete manufacturing processes represent a research challenge because the data generated by the sensors that monitor the manufacturing processes are characterized by noise, missing values, many features due to the fact that the assembly processes involve many different assembly steps and often the resources that are used in the manufacturing processes are applied inefficiently. Digitalization of assembly processes using the latest technologies, analysis of data generated by the monitoring sensors using big data technologies and optimization of the manufacturing processes by identifying the steps that have the highest impact on the final output represent serious research challenges, and in this chapter, we approach these challenges by: (1) creating a platform in which the users can visualize the assembly steps of the discrete manufacturing processes, (2) identifying the products that have manufacturing faults using machine learning and deep learning models and (3) optimizing the parameters of the machine learning and deep learning models using bio-inspired heuristics.

Apache Spark for Digitalization, Analysis and Optimization of Discrete Manufacturing Processes

The Internet of Things (IoT) can reduce manufacturing cost, improve uptime, and help operators gain process insights. In this paper, we develop a system architecture for an IoT-enabled manufacturing system and demonstrate it on a bench-top milling machine. We explore on how we can build such a laboratory-scale system with commonly available, low-cost electronic sensors. Factors for designing such a system have been explored. The designed manufacturing system can take manufacturing inputs as G-Codes from the internet via a website as well as stream the data from several sensors to the internet. Engineering implementation of the system as well as its underlying ideology have been discussed. Micro ElectroMechanical System (MEMS) sensors have low cost, smaller form factor as well as tend to work with a variety of IoT enabled systems. This work evaluates the feasibility of using them for condition monitoring in manufacturing. These systems embody a sort of primary unit for enabling the ‘Smart Factory’ revolution.

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N. Arunachalam

Papers

Development of an Internet of Things enabled manufacturing system for tool wear characterization