Fault diagnosis in discrete time hybrid systems – A case study

2007 ◽  
Vol 177 (5) ◽  
pp. 1290-1308 ◽  
Author(s):  
P BHOWAL ◽  
D SARKAR ◽  
S MUKHOPADHYAY ◽  
A BASU
Keyword(s):  
Fault Diagnosis ◽  
Hybrid Systems ◽  
Discrete Time

INFERENTIAL PROBLEMS FOR A CLASS OF DISCRETE-TIME HYBRID SYSTEMS PART III: FAULT DIAGNOSIS

2002 ◽  
Vol 35 (1) ◽  
pp. 283-288
Author(s):  
D. Sarkar ◽  
P. Bhowal ◽  
A. Basu ◽  
S. Mukhopadhyay
Keyword(s):  
Fault Diagnosis ◽  
Hybrid Systems ◽  
Discrete Time

Case Study on Fault Diagnosis of the Actual Operating Transformer by FRA

2011 ◽  
Vol 131 (1) ◽  
pp. 78-85 ◽  
Author(s):  
Takahiro Sano ◽  
Yoshiharu Ogawa ◽  
Takaaki Shimonosono ◽  
Tadayuki Wada
Keyword(s):  
Fault Diagnosis ◽  
Actual Operating

scholarly journals Sparse Autoencoder-based Multi-head Deep Neural Networks for Machinery Fault Diagnostics with Detection of Novelties

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Zhe Yang ◽  
Dejan Gjorgjevikj ◽  
Jianyu Long ◽  
Yanyang Zi ◽  
Shaohui Zhang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Supervised Classification ◽  
Deep Neural Networks ◽  
Activation Function ◽  
Reconstruction Error ◽  
Fine Tuning ◽  
Fault Diagnostics ◽  
Sparse Autoencoder

AbstractSupervised fault diagnosis typically assumes that all the types of machinery failures are known. However, in practice unknown types of defect, i.e., novelties, may occur, whose detection is a challenging task. In this paper, a novel fault diagnostic method is developed for both diagnostics and detection of novelties. To this end, a sparse autoencoder-based multi-head Deep Neural Network (DNN) is presented to jointly learn a shared encoding representation for both unsupervised reconstruction and supervised classification of the monitoring data. The detection of novelties is based on the reconstruction error. Moreover, the computational burden is reduced by directly training the multi-head DNN with rectified linear unit activation function, instead of performing the pre-training and fine-tuning phases required for classical DNNs. The addressed method is applied to a benchmark bearing case study and to experimental data acquired from a delta 3D printer. The results show that its performance is satisfactory both in detection of novelties and fault diagnosis, outperforming other state-of-the-art methods. This research proposes a novel fault diagnostics method which can not only diagnose the known type of defect, but also detect unknown types of defects.

Reliability evaluation method based on dynamic fault diagnosis results: A case study of a seabed mud lifting system

2021 ◽  
pp. 107763
Author(s):  
Chuan Wang ◽  
Yupeng Liu ◽  
Dongbo Wang ◽  
Guorong Wang ◽  
Dingya Wang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Evaluation Method ◽  
Reliability Evaluation ◽  
Lifting System ◽  
Dynamic Fault Diagnosis

scholarly journals Set-based state estimation and fault diagnosis of linear discrete-time descriptor systems using constrained zonotopes

2020 ◽  
Vol 53 (2) ◽  
pp. 4291-4296
Author(s):  
Brenner S. Rego ◽  
Davide M. Raimondo ◽  
Guilherme V. Raffo
Keyword(s):  
Fault Diagnosis ◽  
State Estimation ◽  
Discrete Time ◽  
Descriptor Systems

Development of a robust model-based fault diagnosis technique for Reusable Launch Vehicles. A case study

2008 ◽  
Vol 41 (2) ◽  
pp. 12036-12041 ◽  
Author(s):  
Alexandre Falcoz ◽  
David Henry ◽  
Ali Zolghadri
Keyword(s):  
Fault Diagnosis ◽  
Launch Vehicles ◽  
Reusable Launch Vehicles ◽  
Model Based ◽  
Robust Model ◽  
Diagnosis Technique

scholarly journals Mixed approach for fault diagnosis and fault location of hybrid systems

2016 ◽  
Vol 49 (12) ◽  
pp. 1002-1007 ◽  
Author(s):  
B. Maaref ◽  
Z. Simeu Abazi ◽  
H. Dhouibi ◽  
H. Messaoud ◽  
E. Gascard
Keyword(s):  
Fault Diagnosis ◽  
Hybrid Systems ◽  
Fault Location ◽  
Mixed Approach
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