AIMC Topic: Equipment Failure Analysis

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Intelligent Fault Diagnosis and Forecast of Time-Varying Bearing Based on Deep Learning VMD-DenseNet.

Sensors (Basel, Switzerland)
Rolling bearings are important in rotating machinery and equipment. This research proposes variational mode decomposition (VMD)-DenseNet to diagnose faults in bearings. The research feature involves analyzing the Hilbert spectrum through VMD whereby ...

Application Combining VMD and ResNet101 in Intelligent Diagnosis of Motor Faults.

Sensors (Basel, Switzerland)
Motor failure is one of the biggest problems in the safe and reliable operation of large mechanical equipment such as wind power equipment, electric vehicles, and computer numerical control machines. Fault diagnosis is a method to ensure the safe ope...

Crack Size Identification for Bearings Using an Adaptive Digital Twin.

Sensors (Basel, Switzerland)
In this research, the aim is to investigate an adaptive digital twin algorithm for fault diagnosis and crack size identification in bearings. The main contribution of this research is to design an adaptive digital twin (ADT). The design of the ADT te...

Pre-Processing Method to Improve Cross-Domain Fault Diagnosis for Bearing.

Sensors (Basel, Switzerland)
Models trained with one system fail to identify other systems accurately because of domain shifts. To perform domain adaptation, numerous studies have been conducted in many fields and have successfully aligned different domains into one domain. The ...

A fault diagnosis method based on Auxiliary Classifier Generative Adversarial Network for rolling bearing.

PloS one
Rolling bearing fault diagnosis is one of the challenging tasks and hot research topics in the condition monitoring and fault diagnosis of rotating machinery. However, in practical engineering applications, the working conditions of rotating machiner...

Machine learning for pattern detection in cochlear implant FDA adverse event reports.

Cochlear implants international
Medical device performance and safety databases can be analyzed for patterns and novel opportunities for improving patient safety and/or device design. The objective of this analysis was to use supervised machine learning to explore patterns in rep...

Intelligent Fault-Prediction Assisted Self-Healing for Embryonic Hardware.

IEEE transactions on biomedical circuits and systems
This paper proposes novel methods for making embryonic bio-inspired hardware efficient against faults through self-healing, fault prediction, and fault-prediction assisted self-healing. The proposed self-healing recovers a faulty embryonic cell throu...

Advanced Data Analytics for Clinical Research Part II: Application to Cardiothoracic Surgery.

Innovations (Philadelphia, Pa.)
In the first part of this series, we introduced the tools of Big Data, including Not Only Standard Query Language data warehouse, natural language processing (NLP), optical character recognition (OCR), and Internet of Things (IoT). There are nuances ...

Advantages of aquatic animals as models for bio-inspired drones over present AUV technology.

Bioinspiration & biomimetics
Robotic systems are becoming more ubiquitous, whether on land, in the air, or in water. In the aquatic realm, aquatic drones including ROVs (remotely operated vehicles) and AUVs (autonomous underwater vehicles) have opened new opportunities to invest...

A hybrid fault diagnosis methodology with support vector machine and improved particle swarm optimization for nuclear power plants.

ISA transactions
The safety and public health during nuclear power plant operation can be enhanced by accurately recognizing and diagnosing potential problems when a malfunction occurs. However, there are still obvious technological gaps in fault diagnosis applicatio...