A prototype fire detection implemented using the Internet of Things and fuzzy logic

Dangerous fires often occur because slow fire spots have the potential to become big fires that are difficult to extinguish. An example of this danger are peatlands in Riau, Indonesia. These dangerous conditions can be ameliorated by first detecting them. A device was developed that can detect fire hotspots by using the Internet of Things (IoT) and fuzzy logic. This early prototype fire detection tool could identify hotspots in the peatlands by using fire sensors, temperature sensors, servo motors, buzzers and surveillance cameras controlled by a WEMOS ESP8266 microcontroller and by applying the fuzzy logic method to analyse the intensity of the detected flames. Based on an experiment using the prototype, fire detection devices with an IoT connection can speed up the monitoring of fire hotspots, and the use of fuzzy logic can minimise false warnings from fire detection devices. The prototype could be used as a medium of learning for high school students majoring in computer engineering and networking

Digital twins for condition and fleet monitoring of aircraft: towards more-intelligent electrified aviation systems

The convergence of Information Technology (IT), Operational Technology (OT), and Educational Technology (ET) has led to the emergence of the fourth industrial revolution. As a result, a new concept has emerged known as Digital Twins (DT), which is defined as "a virtual representation of various objects or systems that receive data from physical objects/systems to make changes and corrections”. In the aviation industry, numerous attempts have been made to utilize DT in the design, manufacturing, and condition monitoring of aircraft fleets. Among these research efforts, real-time, accurate, fast, and predictive condition monitoring methods play a crucial role in ensuring the safe and efficient performance of aircraft. Using DT for condition and fleet monitoring not only enhances the reliability and safety of aircraft but also reduces operational and maintenance costs. In this paper, the conducted studies on the applications of DT systems for condition monitoring of aircraft units and the aerospace sector are discussed and reviewed. The aim of this review paper is to analyse the current developments of DT systems in the aviation industry as well as explain the remaining challenges of DT systems. Then Finally, future trends of DT systems along with aircraft are presented. Among reviewed papers, most of them have used computational fluid dynamics, finite element methods, and artificial intelligence techniques for developing DT models for aircraft. At the same time, most of these analyses are dedicated to the failure and crack detection body of aircraft as well as engine fault detection. Life prediction is another popular application for using DT in aircraft units that could help the engineers predict the maintenance required for different parts of the aircraft. Finally, the application of DT in marine, power systems, and space programs has been also reviewed and the lessons learned from them have been translated to the aviation sector

An industrial analytics methodology and fog computing cyber-physical system for Industry 4.0 embedded machine learning applications

Industrial cyber-physical systems are the primary enabling technology for Industry 4.0, which combine legacy industrial and control engineering, with emerging technology paradigms (e.g. big data, internet-of-things, artificial intelligence, and machine learning), to derive self-aware and self-configuring factories capable of delivering major production innovations. However, the technologies and architectures needed to connect and extend physical factory operations to the cyber world have not been fully resolved. Although cloud computing and service-oriented architectures demonstrate strong adoption, such implementations are commonly produced using information technology perspectives, which can overlook engineering, control and Industry 4.0 design concerns relating to real-time performance, reliability or resilience. Hence, this research compares the latency and reliability performance of cyber-physical interfaces implemented using traditional cloud computing (i.e. centralised), and emerging fog computing (i.e. decentralised) paradigms, to deliver real-time embedded machine learning engineering applications for Industry 4.0. The findings highlight that despite the cloud’s highly scalable processing capacity, the fog’s decentralised, localised and autonomous topology may provide greater consistency, reliability, privacy and security for Industry 4.0 engineering applications, with the difference in observed maximum latency ranging from 67.7% to 99.4%. In addition, communication failures rates highlighted differences in both consistency and reliability, with the fog interface successfully responding to 900,000 communication requests (i.e. 0% failure rate), and the cloud interface recording failure rates of 0.11%, 1.42%, and 6.6% under varying levels of stress