SET: A Shared-Encoder Transformer Scheme for Multi-Sensor, Multi-Class Fault Classification in Industrial IoT

The Industrial Internet of Things (IIoT) has revolutionized the industrial sector by integrating sensors to monitor equipment health and optimize production processes. These sensors collect real-time data and are prone to a variety of different faults, such as bias, drift, noise, gain, spike, and constant faults. Such faults can lead to significant operational problems, including false results, incorrect predictions, and misleading maintenance decisions. Therefore, classifying sensor data appropriately is essential for ensuring the reliability and efficiency of IIoT systems. In this paper, we propose the Shared-Encoder Transformer (SET) scheme for multi-sensor, multi-class fault classification in IIoT systems. Leveraging the transformer architecture, the SET uses a shared encoder with positional encoding and multi-head self-attention mechanisms to capture complex temporal patterns in sensor data. Consequently, it can accurately detect the health status of sensor data, and if the sensor data is faulty, it can specifically identify the fault type. Additionally, we introduce a comprehensive fault injection strategy to address the problem of fault data scarcity, enabling the validation of the robust performance of SET even with limited fault samples in both ideal and realistic scenarios. In our research, we conducted extensive experiments using the Commercial Modular Aeropropulsion System Simulation (C-MAPSS) and Skoltech Anomaly Benchmark (SKAB) datasets to study the performance of the SET. Our experimental results indicate that SET consistently outperforms baseline methods, including Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN)-LSTM, and Multilayer Perceptron (MLP), as well as the proposed comparative variant of SET, Multi-Encoder Transformer (MET), in terms of accuracy, precision, recall, and F1-score across different fault intensities. The shared-kmencoder architecture improves fault detection accuracy and ensures parameter efficiency/robustness, making it suitable for deployment in memory-constrained industrial environments.