Advanced Sensor Signal Processing for Resolving Overlapping Temperature Events in Industrial Applications

Abstract

This article presents an advanced sensor data processing framework leveraging a hybrid deep learning network (DLN) composed of multilayer perceptron (MLP) and convolutional neural network (CNN) models to accurately detect, classify, and reconstruct overlapping temperature events in distributed temperature sensing (DTS) systems. DTS systems frequently face challenges related to limited spatial resolution and overlapping thermal profiles, significantly impairing accurate event detection and localization in different applications. To overcome these limitations, we propose a novel sensor data fusion and pattern recognition approach employing simulated and experimental DTS datasets. Our hybrid DLN extracts intricate features from sensor data, effectively reconstructing temperature profiles with minimal gaps of 0.1 m between events, achieving a mean absolute error (MAE) of 0.104 m. The proposed method demonstrates robust generalization capabilities and high accuracy in real-world industry application scenarios, significantly enhancing the sensor's data processing capability without necessitating modifications to existing DTS infrastructure. This research provides substantial advancements in soft computing methodologies for sensor data processing, particularly in high-density thermal event detection and classification.