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.

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.

Multiple Fault Diagnosis Method in Multistation Assembly Processes Using Orthogonal Diagonalization Analysis

2008 ◽  
Vol 130 (1) ◽  
Author(s):  
Zhenyu Kong ◽  
Dariusz Ceglarek ◽  
Wenzhen Huang
Keyword(s):  
Fault Diagnosis ◽  
Product Information ◽  
Statistical Significance ◽  
Measurement Data ◽  
Space Representation ◽  
Assembly Process ◽  
Dimensional Control ◽  
Multiple Fault ◽  
Multiple Fault Diagnosis ◽  
Multistation Assembly

Dimensional control has a significant impact on overall product quality and performance of large and complex multistation assembly systems. To date, the identification of process-related faults that cause large variations of key product characteristics (KPCs) remains one of the most critical research topics in dimensional control. This paper proposes a new approach for multiple fault diagnosis in a multistation assembly process by integrating multivariate statistical analysis with engineering models. The proposed method is based on the following steps: (i) modeling of fault patterns obtained using state space representation of process and product information that explicitly represents the relationship between process-related error sources denoted by key control characteristics (KCCs) and KPCs, and (ii) orthogonal diagonalization of measurement data using principal component analysis (PCA) to project measurement data onto the axes of an affine space formed by the predetermined fault patterns. Orthogonal diagonalization allows estimating the statistical significance of the root cause of the identified fault. A case study of fault diagnosis for a multistation assembly process illustrates and validates the proposed methodology.

Sign in / Sign up

Export Citation Format

Share Document