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.

Fixture Fault Diagnosis in Multistation Assembly of Sheet Metal

2014 ◽  
Vol 635-637 ◽  
pp. 1841-1846
Author(s):  
Ling Hua Zhou ◽  
Xiang Hong Xu ◽  
De Zhong Yu
Keyword(s):  
Fault Diagnosis ◽  
State Space ◽  
Sheet Metal ◽  
Matrix Equation ◽  
Determination Method ◽  
Measurement Equation ◽  
Multistation Assembly ◽  
Space Equation ◽  
Source Diagnosis

This paper presents a methodology for diagnostics of fixture failures in multistation assembly processes. Diagnosis matrix equation is established by state space equation and measurement equation, which study the conditions of deviation source diagnosis. The determination method of deviation source diagnosis is obtained. 3-D scanner is used to measure the key data. A case study illustrates the proposed method.

scholarly journals Fault diagnosis based on Petri Nets: the case study of a hydropower plant

2016 ◽  
Vol 49 (31) ◽  
pp. 1-6 ◽  
Author(s):  
Arthur H.A. Melani ◽  
Javier Martinez Silva ◽  
Gilberto F.M. de Souza ◽  
José Reinaldo Silva
Keyword(s):  
Fault Diagnosis ◽  
Petri Nets ◽  
Hydropower Plant

Process Fault Diagnosis Based on Process Model Knowledge: Part II—Case Study Experiments

1991 ◽  
Vol 113 (4) ◽  
pp. 627-633 ◽  
Author(s):  
R. Isermann ◽  
B. Freyermuth
Keyword(s):  
Fault Diagnosis ◽  
Knowledge Acquisition ◽  
Knowledge Base ◽  
Process Models ◽  
Normal Operation ◽  
Problem Solution ◽  
Inference Mechanism ◽  
Process Knowledge ◽  
Fault Trees

A computer assisted fault diagnosis system (CAFD) is considered which allows the early detection and localization of process faults during normal operation or on request. It is based on an on-line engineering expert system and consists of an analytical problem solution, a process knowledge base, a knowledge acquisition component and an inference mechanism. The analytic problem solution uses a process parameter estimation, and the detection of process coefficient changes, which are symptoms of process faults. The process knowledge base is comprised of analytical knowledge in the form of process models and heuristic knowledge in the form of fault trees and fault statistics. In the phase of knowledge acquisition the process specific knowledge like theoretical process models, the normal behavior and fault trees is compiled. The inference mechanism performs the fault diagnosis, based on the observed symptoms, the fault trees, fault probabilities and the process history. This is described in Part I. In Part II, case study experiments with a d.c. motor, centrifugal pump, a heat exchanger and an industrial robot show practical results of the model based fault diagnosis.

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