Fault Detection
53 papers with code • 0 benchmarks • 5 datasets
Benchmarks
These leaderboards are used to track progress in Fault Detection
Datasets
PRONOSTIA Bearing Dataset
Unbalance Classification Using Vibration Data
IMS Bearing Dataset
Sound-based drone fault classification using multitask learning
PRONTO
Latest papers
Zero-Shot Motor Health Monitoring by Blind Domain Transition
ozercandevecioglu/zero-shot-bearing-fault-detection-by-blind-domain-transition
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To address this need, in this pilot study, we propose a zero-shot bearing fault detection method that can detect any fault on a new (target) machine regardless of the working conditions, sensor parameters, or fault characteristics.
DeepFT: Fault-Tolerant Edge Computing using a Self-Supervised Deep Surrogate Model
The emergence of latency-critical AI applications has been supported by the evolution of the edge computing paradigm.
Self-Supervised Masked Convolutional Transformer Block for Anomaly Detection
ristea/ssmctb
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In this work, we extend our previous self-supervised predictive convolutional attentive block (SSPCAB) with a 3D masked convolutional layer, a transformer for channel-wise attention, as well as a novel self-supervised objective based on Huber loss.
Automatic detection of faults in race walking from a smartphone camera: a comparison of an Olympic medalist and university athletes
We also revealed that the machine learning model detects faults according to the rules of race walking.
SensorSCAN: Self-Supervised Learning and Deep Clustering for Fault Diagnosis in Chemical Processes
However, manual annotation of large amounts of data can be difficult in industrial settings.
CIPCaD-Bench: Continuous Industrial Process datasets for benchmarking Causal Discovery methods
This work introduces two novel public datasets for CD in continuous manufacturing processes.
Explainable AI Algorithms for Vibration Data-based Fault Detection: Use Case-adadpted Methods and Critical Evaluation
This allows to assess the saliency given to features which depend on the rotation speed and those with constant frequency.
Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency
mims-harvard/tfc-pretraining
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Experiments against eight state-of-the-art methods show that TF-C outperforms baselines by 15. 4% (F1 score) on average in one-to-one settings (e. g., fine-tuning an EEG-pretrained model on EMG data) and by 8. 4% (precision) in challenging one-to-many settings (e. g., fine-tuning an EEG-pretrained model for either hand-gesture recognition or mechanical fault prediction), reflecting the breadth of scenarios that arise in real-world applications.
Monitoring of Perception Systems: Deterministic, Probabilistic, and Learning-based Fault Detection and Identification
mit-spark/perceptionmonitoring
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This paper investigates runtime monitoring of perception systems.
Black-Box Testing of Deep Neural Networks Through Test Case Diversity
In this paper, we investigate black-box input diversity metrics as an alternative to white-box coverage criteria.
