Results 151 to 160 of about 35,178 (215)

A smartphone-based vibration dataset for induction motor fault diagnosis under different speed and load conditions. [PDF]

Data Brief
Ertarğın M, Orhan A, Yıldırım Ö, Chauhan S, Vashishtha G.   +4 more
europepmc   +1 more source

Sliding sampling and successive variational mode decomposition CNN-BiLSTM-attention based fault detection and early warning method for DC microgrid. [PDF]

Sci Rep
Dai Y, Wang M, Zhang L, Wang S, Jiang Z.
europepmc   +1 more source

CNN Bearing Fault Diagnosis Based on Symmetric Point Pattern Feature Fusion with Multi-Source Resonance Sparse Components. [PDF]

Sensors (Basel)
Liu Y, Li Y, Sun Q, Yang L, Jia Q, Zhu X, Yang Y, Yang P.   +7 more
europepmc   +1 more source

Fault diagnosis of aero-engines using transfer dispersion entropy and dispersion patterns. [PDF]

PLoS One
Zhang H, Zhang Y, Liu J, Dong K.
europepmc   +1 more source

A machine learning-based classification method for SynRM faults. [PDF]

Sci Rep
Rajini V, Nagarajan VS, Gulbarga MI, Aljarbouh A, Jassem M, Meer A, Saleel CA.   +6 more
europepmc   +1 more source

A fault diagnosis method for complex systems based on hierarchical belief rule base with One-vs-Rest strategy. [PDF]

Sci Rep
Wu J, Li N, Li Y, Lin L, Liu B.
europepmc   +1 more source

Diffusion-augmented YOLO26-Swin cascaded framework with hybrid SHAP-CAM for autonomous power grid inspection. [PDF]

Auton Intell Syst
Stefenon SF, Matos-Carvalho JP, Mariani VC, Dos Santos Coelho L, Yow KC.   +4 more
europepmc   +1 more source

From Fault Classification to Fault Tolerance for Multi-Agent Systems [PDF]

, 2013
Faults are a concern for Multi-Agent Systems (MAS) designers, especially if the MAS are built for industrial or military use because there must be some guarantee of dependability. Some fault classification exists for classical systems, and is used to define faults. When dependability is at stake, such fault classification may be used from the beginning
Katia Potiron, Amal El Fallah Seghrouchni, Patrick Taillibert   +2 more
openaire   +3 more sources

Some of the next articles are maybe not open access.

Related searches:

Hierarchical Classification for Unknown Faults

2020 IEEE International Conference on Prognostics and Health Management (ICPHM), 2020
Data-driven prognostics and health management (PHM) models are generally trained on a set of data collected from the system under study. A standard assumption of this paradigm is that the training data contains all the normal operating conditions and fault conditions that are possible.
Stephen C. Adams, Tyler Cody, Peter A. Beling, Sherwood Polter, Kevin Farinholt   +4 more
openaire   +1 more source