This paper examines the effectiveness of neural network algorithms for hydraulic system fault detection and a novel neural network architecture is suggested. The proposed gated convolutional autoencoder was trained on a simulated training set augmented with just 0.2% data from the real test bench, dramatically reducing the time needed to spend with the actual hardware to build a high-quality fault detection model. Our fault detection model was validated on a test bench and showed accuracy of more than 99% of correctly recognized hydraulic system states with a 10-s sampling window. This model can be also leveraged to examine the decision boundaries of the classifier in the two-dimensional embedding space.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8272240 | PMC |
http://dx.doi.org/10.3390/s21134410 | DOI Listing |
Sensors (Basel)
December 2024
CARISSMA Institute of Electric, Connected, and Secure Mobility (C-ECOS), Technische Hochschule Ingolstadt, Esplanade 10, 85049 Ingolstadt, Germany.
The perception of the vehicle's environment is crucial for automated vehicles. Therefore, environmental sensors' reliability and correct functioning are becoming increasingly important. Current vehicle inspections and self-diagnostics must be adapted to ensure the correct functioning of environmental sensors throughout the vehicle's lifetime.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong SAR, China.
This paper introduces a novel approach for enhancing fault diagnosis in industrial equipment systems through the application of sensor network-driven knowledge graph-based in-context learning (KG-ICL). By focusing on the critical role of sensor data in detecting and isolating faults, we construct a domain-specific knowledge graph (DSKG) that encapsulates expert knowledge relevant to industrial equipment. Utilizing a long-length entity similarity (LES) measure, we retrieve relevant information from the DSKG.
View Article and Find Full Text PDFSensors (Basel)
December 2024
School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230002, China.
LLC resonant converters have emerged as essential components in DC charging station modules, thanks to their outstanding performance attributes such as high power density, efficiency, and compact size. The stability of these converters is crucial for vehicle endurance and passenger experience, making reliability a top priority. However, malfunctions in the switching transistor or current sensor can hinder the converter's ability to maintain a resonant state and stable output voltage, leading to a notable reduction in system efficiency and output capability.
View Article and Find Full Text PDFSensors (Basel)
December 2024
State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, China.
Applying deep learning to unsupervised bearing fault diagnosis in complex industrial environments is challenging. Traditional fault detection methods rely on labeled data, which is costly and labor-intensive to obtain. This paper proposes a novel unsupervised approach, WDCAE-LKA, combining a wide kernel convolutional autoencoder (WDCAE) with a large kernel attention (LKA) mechanism to improve fault detection under unlabeled conditions, and the adaptive threshold module based on a multi-layer perceptron (MLP) dynamically adjusts thresholds, boosting model robustness in imbalanced scenarios.
View Article and Find Full Text PDFSensors (Basel)
December 2024
LASSENA-Laboratory of Space Technologies, Embedded Systems, Navigation and Avionics, École de Technologie Supérieure (ETS), Montreal, QC H3C-1K3, Canada.
The hindering of Global Navigation Satellite Systems (GNSS) signal reception by jamming and spoofing attacks degrades the signal quality. Careful attention needs to be paid when post-processing the signal under these circumstances before feeding the signal into the GNSS receiver's post-processing stage. The identification of the time domain statistical attributes and the spectral domain characteristics play a vital role in analyzing the behaviour of the signal characteristics under various kinds of jamming attacks, spoofing attacks, and multipath scenarios.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!