High efficiency and safety are critical factors in ensuring the optimal performance and reliability of systems and equipment across various industries. Fault monitoring (FM) techniques play a pivotal role in this regard by continuously monitoring system performance and identifying the presence of faults or abnormalities. However, traditional FM methods face limitations in fully capturing the complex interactions within a system and providing real-time monitoring capabilities. To overcome these challenges, Digital Twin (DT) technology has emerged as a promising solution to enhance existing FM practices. By creating a virtual replica or digital copy of a physical equipment or system, DT offers the potential to revolutionize fault monitoring approaches. This paper aims to explore and discuss the diverse range of predictive methods utilized in DT and their implementations in FM across industries. Furthermore, it will showcase successful implementations of DT in FM across a wide array of industries, including manufacturing, energy, transportation, and healthcare. The utilization of DT in FM enables a comprehensive understanding of system behavior and performance by leveraging real-time data, advanced analytics, and machine learning algorithms. By integrating physical and virtual components, DT facilitates the monitoring and prediction of faults, providing valuable insights into the system’s health and enabling proactive maintenance and decision making.

With the rapid development of portable electronics, wearable devices have become widely used to monitor body signals for long-term health care and home care applications. They detect vital signals through physiological sensors and then transmit them to a cloud database for evaluation and monitoring purposes through wireless communication systems. In this paper, a smart analog-to-digital converter (ADC) was realized by a mixed-signal application-specific integrated circuit (ASIC) based on adaptive resolution and lossless compression techniques for electrocardiogram (ECG) signal monitoring. The sampling clock for the ADC can be adaptively selected according to the characteristic of the signals. The lossless encoder consists of trend forecasting and entropy coding modules. The transmission data rate was decreased efficiently by adaptive resolution and lossless compression techniques. The chip aims to meet the low power consumption for the design, because it reduced the signal transmission rate and maintained high-quality ECG signal detection. The proposed mixed-signal ASIC design was realized using a 0.18-μm CMOS process with a total power consumption of 78.8 μW when operating at 1 kHz and a total chip area of 850 × 850 μm ² .

In this paper, it uses digital image processing and support vector machine (SVM) to detect aedes aegypti mosquito. Aedes aegypti mosquito is a carrier of dengue virus which causes the problem that is called dengue hemorrhagic fever. As of today there is still no particular medicine for the treatment of such. As a result the study proposed a way on how to detect if a mosquito is an aedes aegypti or not using SVM in Matlab. First, it was trained by feeding images of aedes aegypti and not aedes aegypti such as aedes albopictus and culex that were taken at different views, highlighting their unique features. In the image processing, the features were extracted and then classified to aedes aegypti or not aedes aegypti. Confusion matrix was used to show the accuracy of the system in detecting the Aedes Aegypti mosquito.

Additive manufacturing (AM) has emerged as a transformative technology for various industries, enabling the production of complex and customized parts. However, ensuring the quality and reliability of AM parts remains a critical challenge. Thus, image-based fault monitoring has gained significant attention as an efficient approach for detecting and classifying faults in AM processes. This paper presents a comprehensive survey of image-based fault monitoring in AM, focusing on recent developments and future directions. Specifically, the proponents garnered relevant papers from 2019 to 2023, gathering a total of 53 papers. This paper discusses the essential techniques, methodologies, and algorithms employed in image-based fault monitoring. Furthermore, recent developments are explored such as the use of novel image acquisition techniques, algorithms, and methods. In this paper, insights into future directions are provided, such as the need for more robust image processing algorithms, efficient data acquisition and analysis methods, standardized benchmarks and datasets, and more research in fault monitoring. By addressing these challenges and pursuing future directions, image-based fault monitoring in AM can be enhanced, improving quality control, process optimization, and overall manufacturing reliability.

Image processing is the method that will be used in this system. A surveillance camera will be mounted at an optimal position so that it could detect the vehicles efficiently. The Foreground Detector and Blob Analysis make up the techniques that will be used to identify an object as a vehicle in a video sequence. The Mixture Model, specifically Gaussian must have an initialized Foreground Detector that has sampled a video frame before it can be used. After initializing Foreground Detector, Blob Analysis would be used to detect vehicles and place bounding box. Image processing used in detecting vehicles is used in determining the weather condition. The only difference is that histogram would also be used. Since it will operate in real-time, it is capable of detecting the density of vehicles on the lane even in various conditions like raining or changes in illumination.

This is an automated system in determining shelf-life of a cavendish banana fruit using its ethylene gas content through fuzzy logic approach. One finger of banana is placed inside a chamber for experimentation. An MQ3 sensor is used to measure the ethylene gas emitted by the fruit and a DHT22 temperature sensor is also used to monitor the ambient temperature inside the chamber. Linear regression analysis statistics is being applied to show that adjusted R Square result is 0.94. Three temperature range were considered to differentiate the emission speed of ethylene gas for a certain temperature. The higher the temperature, the faster the ripening process. Moist also affects ethylene gas emission inside the chamber and is therefore, proper ventilation is essential during the ripening process. The system uses MATLAB to determine banana's shelf-life using fuzzy logic toolbox and displayed in a graphical user interface. Ethylene gas content were also monitored for each classifications of banana, namely: unripe, ripe and overripe. With this, conventional way in determining ripeness will be minimized and shelf-life will also be easily identified. Consumers and farmers are the great beneficiaries of the system, as it is their way to monitor fruit ripening and its shelf-life.