This paper proposes a novel and reliable leak-detection method for pipeline systems based on acoustic emission (AE) signals. The proposed method analyzes signals from two AE sensors installed on the pipeline to detect leaks located between these two sensors. Firstly, the raw AE signals are preprocessed using empirical mode decomposition. The time difference of arrival (TDOA) is then extracted as a statistical feature of the two AE signals. The state of the pipeline (leakage/normal) is determined through comparing the statistical distribution of the TDOA of the current state with the prior normal state. Specifically, the two-sample Kolmogorov–Smirnov (K–S) test is applied to compare the statistical distribution of the TDOA feature for leak and non-leak scenarios. The K–S test statistic value in this context functions as a leakage indicator. A new criterion called leak sensitivity is introduced to evaluate and compare the performance of leak detection methods. Extensive experiments were conducted using an industrial pipeline system, and the results demonstrate the excellence of the proposed method in leak detection. Compared to traditional feature-based indicators, our approach achieves a significantly higher performance in leak detection.

In this study, we present a comprehensive approach for the morphological analysis of palladium on carbon (Pd/C) nanoparticles utilizing scanning electron microscopy (SEM) imaging and advanced deep learning techniques.

In this work, we propose a deep-learning method to diagnose bearing faults of electric motors based on vibration and bearing housing temperature. Our methods can accurately diagnose faults related to bearing cracking and lubricant shortages. The proposed method is effective in terms of computational complexity and model capacity thanks to the advantages of neural architecture search (NAS) and parameter quantization in the model establishment. The experimental results found that the information on bearing temperature improved the diagnostic accuracy for the bearing fault diagnosis task. The proposed method has explored the most optimal model in terms of computational resources and model capacity with a pre-defined accuracy target. The searched model has a relatively high diagnostic accuracy of 98.7% and a size of about 27.3 kB. After quantization, the obtained model maintained 96.9% accuracy and reduced 4 times in size. All experiments are executed elaborately on our custom bearing fault dataset.

In this work, a method of fabricating a NO ₂ nano-sensor working at room temperature with a low detectable concentration limit is proposed.