Abstract
In the realm of modern data-driven systems, accurate and reliable sensor measurements are imperative for informed decision-making and system integrity. The objective of this project is to develop a robust sensor fault detection methodology leveraging the power of deep learning techniques. The ubiquity of sensors such as temperature and humidity sensors has led to a critical need for discerning between accurate readings and erroneous data, thereby enhancing the reliability of these measurements. This study addresses the prevailing challenge of sensor reliability by introducing a data-driven approach that harnesses deep learning algorithms to detect sensor faults promptly and accurately. A comprehensive dataset comprising sensor readings under diverse conditions forms the foundation of this investigation. Multiple cutting-edge deep learning architectures and techniques are systematically explored and evaluated against this dataset to identify the most efficient and precise sensor fault detection method. The outcomes of this research contribute significantly to advancing the state-of-the-art in sensor fault detection, with implications for a wide array of applications reliant on sensor measurements. By harnessing the capabilities of deep learning, this project presents a tangible solution to the challenge of accurately identifying faulty sensor data in real-time, thereby bolstering the dependability and efficacy of sensor-driven systems. Ultimately, this work underscores the potential of integrating advanced machine learning techniques in ensuring the reliability and precision of sensor data, heralding a new era of robust and trustworthy sensor-enabled environments.
Keywords: Convolutional Neural Networks (CNN), Data Integrity, Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNN), Sensor Fault Detection, Variational Auto Encoders (VAE)