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Equipment Failure

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A novel bearing intelligent fault diagnosis framework under time-varying working conditions using recurrent neural network.

ISA transactions
Normal operation of bearing is the key to ensure the reliability and security of rotary machinery, so that bearing fault diagnosis is quite significant. However, the large amount of data collected by modern data acquisition system and time-varying wo...

Optimized artificial neural network to improve the accuracy of estimated fault impedances and distances for underground distribution system.

PloS one
This paper proposes an approach to accurately estimate the impedance value of a high impedance fault (HIF) and the distance from its fault location for a distribution system. Based on the three-phase voltage and current waveforms which are monitored ...

IoT Based Predictive Maintenance Management of Medical Equipment.

Journal of medical systems
Technological advancements are the main drivers of the healthcare industry as it has a high impact on delivering the best patient care. Recent years witnessed unprecedented growth in the number of medical equipment manufactured to aid high-quality pa...

An integrated machine learning model for aircraft components rare failure prognostics with log-based dataset.

ISA transactions
Predictive maintenance is increasingly advancing into the aerospace industry, and it comes with diverse prognostic health management solutions. This type of maintenance can unlock several benefits for aerospace organizations. Such as preventing unexp...

Development of an Alarm Algorithm, With Nanotechnology Multimodal Sensor, to Predict Impending Infusion Failure and Improve Safety of Peripheral Intravenous Catheters in Neonates.

Advances in neonatal care : official journal of the National Association of Neonatal Nurses
BACKGROUND: Peripheral intravenous catheters connected to an infusion pump are necessary for the delivery of fluids, nutrition, and medications to hospitalized neonates but are not without complications. These adverse events contribute to hospital-ac...

A Neural Network-Based Joint Prognostic Model for Data Fusion and Remaining Useful Life Prediction.

IEEE transactions on neural networks and learning systems
With the rapid development of sensor and information technology, now multisensor data relating to the system degradation process are readily available for condition monitoring and remaining useful life (RUL) prediction. The traditional data fusion an...

Preclinical evaluation of a markerless, real-time, augmented reality guidance system for robot-assisted radical prostatectomy.

International journal of computer assisted radiology and surgery
PURPOSE: Intra-operative augmented reality (AR) during surgery can mitigate incomplete cancer removal by overlaying the anatomical boundaries extracted from medical imaging data onto the camera image. In this paper, we present the first such complete...

Malfunction Events in the US FDA MAUDE Database: How Does Robotic Gynecologic Surgery Compare with Other Specialties?

Journal of minimally invasive gynecology
STUDY OBJECTIVE: To review malfunction events (MEs) related to the use of the da Vinci robot reported to the United States Food and Drug Administration Manufacturer and User Facility Device Experience in the last 10 years and compare gynecologic surg...

Predicting early failure of quantum cascade lasers during accelerated burn-in testing using machine learning.

Scientific reports
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...

End-to-End Continuous/Discontinuous Feature Fusion Method with Attention for Rolling Bearing Fault Diagnosis.

Sensors (Basel, Switzerland)
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...