Enhancing Diagnostic Accuracy in Plasma Applications: Overcoming Data Imbalance and Uncertainty with Advanced Machine Learning Techniques

In plasma applications critical to industries like aerospace, automotive, material processing, advance manufacturing, and nuclear fusion, temperature sensor accuracy and reliability are essential for ensuring operational safety, efficiency, and equipment longevity. These sensors are vital in managing processes at extreme temperatures but face challenges due to data imbalance and uncertainty. Typically, extreme temperature events, anomalies are infrequent, leading to machine learning models that are undertrained for critical conditions, potentially missing failure signs. Additionally, sensor data uncertainty, from degradation, harsh environments, and response variability, can compromise data integrity for anomaly detection through diagnostic. This situation underscores the need for advanced methods to address challenges. Traditional diagnostic techniques fall short in complex plasma environments, often achieving suboptimal diagnostic accuracy. The initiation of Generative Adversarial Networks (GAN) for plasma temperature sensor signals with combination of Artificial Neural Networks (ANN) offers a breakthrough, with these models learning from complex data patterns and generating synthetic data to represent rare events better. This approach has significantly improved diagnostic accuracy in plasma applications, reaching up to 96.12%, a marked improvement over traditional methods. The integration of proposed diagnostic frameworks sets new standards for safety, efficiency, and reliability in plasma application engineering. It highlights the importance of developing machine learning algorithms that can manage data imbalance and incorporate uncertainty measures, ensuring models are accurate, reliable, and capable of identifying critical events. This advancement not only enhances plasma application reliability but also contributes to high-temperature operational management and sensor technology development, paving the way for more resilient and intelligent sensing systems in critical engineering applications.