Deep learning-based proactive fault detection method for enhanced quadrotor safety

Abstract

The early detection of faults in advanced technological systems is imperative for ensuring operational reliability and safety. While there is a growing interest in using artificial intelligence for fault detection, current methodologies often exhibit limitations in utilizing comprehensive system information and sensor data. Hidden faults within collected data further highlight the need for advanced analysis techniques. This study introduces a novel deep learning-based framework designed to predict faults and extract insights from complex system datasets. The model, consisting of LSTM-autoencoder and BiLSTM classification components, effectively reduces feature dimensions, thereby enhancing fault detection accuracy. The autoencoder’s latent layer identifies prominent features across various dimensions, while BiLSTM classification conducts bidirectional analysis using these features from both healthy and faulty states, facilitating early fault detection. Experimental results demonstrate the model’s efficacy, achieving an accuracy of 79.48% in predicting incipient faults 30 seconds before a serious malfunction occurs. This underscores the significant potential of the proposed framework in enhancing operational safety and reliability in complex systems. Moreover, the study emphasizes the importance of leveraging comprehensive data and advanced analysis techniques for early fault detection.

Keywords

• quadrotor fault prognostics
• early fault detection
• deep learning
• autoencoders
• LSTM