AIMC Topic: Equipment Failure

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Deep learning algorithms for detecting fractured instruments in root canals.

BMC oral health Feb 23, 2025
BACKGROUND: Identifying fractured endodontic instruments (FEIs) in periapical radiographs (PAs) is a critical yet challenging aspect of root canal treatment (RCT) due to anatomical complexities and overlapping structures. Deep learning (DL) models of...
Algorithms Deep Learning Humans Root Canal Therapy Dental Instruments Dental Pulp Cavity Equipment Failure
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Leveraging feature extraction and risk-based clustering for advanced fault diagnosis in equipment.

PloS one Dec 30, 2024
In the contemporary manufacturing landscape, the advent of artificial intelligence and big data analytics has been a game-changer in enhancing product quality. Despite these advancements, their application in diagnosing failure probability and risk r...
Cluster Analysis Equipment Failure Artificial Intelligence Algorithms Humans
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Advancement of post-market surveillance of medical devices leveraging artificial intelligence: Patient monitors case study.

Technology and health care : official journal of the European Society for Engineering and Medicine Nov 25, 2024
BackgroundHealthcare institutions throughout the world rely on medical devices to provide their services reliably and effectively. However, medical devices can, and do sometimes fail. These failures pose significant risk to patients.ObjectiveOne way ...
Humans Product Surveillance, Postmarketing Support Vector Machine Equipment Failure Decision Trees Artificial Intelligence Equipment and Supplies Machine Learning Algorithms
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Cross-domain zero-shot learning for enhanced fault diagnosis in high-voltage circuit breakers.

Neural networks : the official journal of the International Neural Network Society Aug 31, 2024
Ensuring the stability of high-voltage circuit breakers (HVCBs) is crucial for maintaining an uninterrupted supply of electricity. Existing fault diagnosis methods typically rely on extensive labeled datasets, which are challenging to obtain due to t...
Vibration Algorithms Equipment Failure Semantics Electricity Neural Networks, Computer Machine Learning
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Novel glassbox based explainable boosting machine for fault detection in electrical power transmission system.

PloS one Aug 28, 2024
The reliable operation of electrical power transmission systems is crucial for ensuring consumer's stable and uninterrupted electricity supply. Faults in electrical power transmission systems can lead to significant disruptions, economic losses, and ...
Electric Power Supplies Machine Learning Algorithms Equipment Failure Electricity Neural Networks, Computer
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Feasibility of Artificial Intelligence Powered Adverse Event Analysis: Using a Large Language Model to Analyze Microwave Ablation Malfunction Data.

Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes Aug 21, 2024
Determine if a large language model (LLM, GPT-4) can label and consolidate and analyze interventional radiology (IR) microwave ablation device safety event data into meaningful summaries similar to humans. Microwave ablation safety data from Januar...
Artificial Intelligence Equipment Failure Humans Ablation Techniques Feasibility Studies Microwaves
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A Deep Learning Model with Signal Decomposition and Informer Network for Equipment Vibration Trend Prediction.

Sensors (Basel, Switzerland) Jun 22, 2023
Accurate equipment operation trend prediction plays an important role in ensuring the safe operation of equipment and reducing maintenance costs. Therefore, monitoring the equipment vibration and predicting the time series of the vibration trend is o...
Algorithms Deep Learning Vibration Equipment Failure Entropy
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Fault Early Warning Model for High-Speed Railway Train Based on Feature Contribution and Causal Inference.

Sensors (Basel, Switzerland) Nov 25, 2022
The demands for model accuracy and computing efficiency in fault warning scenarios are increasing as high-speed railway train technology continues to advance. The black box model is difficult to interpret, making it impossible for this technology to ...
Industry Records Equipment Failure Bayes Theorem Machine Learning
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End-to-End Continuous/Discontinuous Feature Fusion Method with Attention for Rolling Bearing Fault Diagnosis.

Sensors (Basel, Switzerland) Aug 29, 2022
Mechanical equipment failure may cause massive economic and even life loss. Therefore, the diagnosis of the failures of machine parts in time is crucial. The rolling bearings are one of the most valuable parts, which have attracted the focus of fault...
Neural Networks, Computer Signal Processing, Computer-Assisted Machine Learning Vibration Equipment Failure
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Predicting early failure of quantum cascade lasers during accelerated burn-in testing using machine learning.

Scientific reports Jun 2, 2022
Device life time is a significant consideration in the cost of ownership of quantum cascade lasers (QCLs). The life time of QCLs beyond an initial burn-in period has been studied previously; however, little attention has been given to predicting prem...
Lasers, Semiconductor Algorithms Equipment Failure Support Vector Machine
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