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.

Multiple Fault Diagnosis Method in Multi-Station Assembly Processes Using State Space Model and Orthogonal Diagonalization Analysis

2005 ◽  
Author(s):  
Zhenyu Kong ◽  
Ramesh Kumar ◽  
Suren Gogineni ◽  
Yingqing Zhou ◽  
Jijun Lin ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
State Space ◽  
State Space Model ◽  
Measurement Data ◽  
Assembly Process ◽  
Dimensional Control ◽  
Multiple Fault ◽  
Space Model ◽  
Multiple Fault Diagnosis ◽  
Affine System

Dimensional control has a significant impact on the overall product quality and performance in large and complex multi-station assembly systems. From measurement data, the way to identify root causes for large variation of Key Product Characteristics (KPCs) is one of the most critical research topics in dimensional control. This paper proposes a new approach for multiple fault diagnosis in a multi-station assembly process by integrating multivariate statistical analysis with engineering model. Based on product/process information, by using the state space model, a set of fault patterns for multi-station assembly process are developed, which explicitly represent the relationship between the error sources and KPCs. The vectors of these patterns form an affine system. Afterwards, the Principal Component Analysis (PCA) is applied to conduct orthogonal diagonalization of the measurement data. Thus, the measurement data can be easily projected to the axes of the affine system. Whereby, the significance of each fault pattern shall be estimated accurately. Finally, a few case studies are also provided to validate the proposed methodology.

Multiple Fault Diagnosis for Sheet Metal Fixtures Using Designated Component Analysis

2004 ◽  
Vol 126 (1) ◽  
pp. 91-97 ◽  
Author(s):  
Jaime A. Camelio ◽  
S. Jack Hu
Keyword(s):  
Fault Diagnosis ◽  
Sheet Metal ◽  
Measurement Data ◽  
Component Analysis ◽  
New Approach ◽  
Multiple Fault ◽  
Sheet Metal Parts ◽  
Multiple Fault Diagnosis ◽  
Line Measurement ◽  
On Line

This paper presents a new approach to multiple fault diagnosis for sheet metal fixtures using designated component analysis (DCA). DCA first defines a set of patterns based on product/process information, then finds the significance of these patterns from the measurement data and maps them to a particular set of faults. Existing diagnostics methods has been mainly developed for rigid-body-based 3-2-1 locating scheme. Here an N-2-1 locating scheme is considered since sheet metal parts are compliant. The proposed methodology integrates on-line measurement data, part geometry, fixture layout and sensor layout in detecting simultaneous multiple fixture faults. A diagnosability discussion for the different type of faults is presented. Finally, an application of the proposed method is presented through a computer simulation.

Multiple fault diagnosis in digital microfluidic biochips

2006 ◽  
Vol 2 (4) ◽  
pp. 262-276 ◽  
Author(s):  
Daniel Davids ◽  
Siddhartha Datta ◽  
Arindam Mukherjee ◽  
Bharat Joshi ◽  
Arun Ravindran
Keyword(s):  
Fault Diagnosis ◽  
Multiple Fault ◽  
Multiple Fault Diagnosis ◽  
Microfluidic Biochips ◽  
Digital Microfluidic

scholarly journals Health Condition Estimation of Bearings with Multiple Faults by a Composite Learning-Based Approach

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4424
Author(s):  
Udeme Inyang ◽  
Ivan Petrunin ◽  
Ian Jennions
Keyword(s):  
Fault Diagnosis ◽  
Health Management ◽  
Network Models ◽  
Health Condition ◽  
Limited Attention ◽  
Neural Network Models ◽  
Multiple Fault ◽  
Multiple Faults ◽  
Multiple Fault Diagnosis ◽  
Non Gaussian

Bearings are critical components found in most rotating machinery; their health condition is of immense importance to many industries. The varied conditions and environments in which bearings operate make them prone to single and multiple faults. Widespread interest in the improvements of single fault diagnosis meant limited attention was spent on multiple fault diagnosis. However, multiple fault diagnosis poses extra challenges due to the submergence of the weak fault by the strong fault, presence of non-Gaussian noise, coupling of the frequency components, etc. A number of existing convolutional neural network models operate on a distinct feature that is not enough to assure reliable results in the presence of these challenges. In this paper, extended feature sets in three homogenous deep learning models are used for multiple fault diagnosis. This ensures a measure of diversity is introduced to the health management dataset to obtain complementary solutions from the models. The outputs of the models are fused through blending ensemble learning. Experiments using vibration datasets based on bearing multiple faults show an accuracy of 98.54%, with an improvement of 2.74% in the overall effectiveness over the single models. Compared with other technologies, the results show that this approach provides an improved generalized diagnostic capability.

scholarly journals A New Approach to Multiple Fault Diagnosis: A Combination of Diagnostic Matrices, Graphs, Algebraic and Rule-Based Models. The Case of Two-Layer Models

2008 ◽  
Vol 18 (4) ◽  
pp. 465-476 ◽  
Author(s):  
Antoni Ligęza ◽  
Jan Kościelny
Keyword(s):  
Fault Diagnosis ◽  
Simultaneous Occurrence ◽  
Diagnostic Model ◽  
Rule Based ◽  
New Approach ◽  
Multiple Fault ◽  
Multiple Faults ◽  
Model Based ◽  
Multiple Fault Diagnosis ◽  
System Variables

A New Approach to Multiple Fault Diagnosis: A Combination of Diagnostic Matrices, Graphs, Algebraic and Rule-Based Models. The Case of Two-Layer ModelsThe diagnosis of multiple faults is significantly more difficult than singular fault diagnosis. However, in realistic industrial systems the possibility of simultaneous occurrence of multiple faults must be taken into account. This paper investigates some of the limitations of the diagnostic model based on the simple binary diagnostic matrix in the case of multiple faults. Several possible interpretations of the diagnostic matrix with rule-based systems are provided and analyzed. A proposal of an extension of the basic, single-level model based on diagnostic matrices to a two-level one, founded on causal analysis and incorporating an OR and an AND matrix is put forward. An approach to the diagnosis of multiple faults based on inconsistency analysis is outlined, and a refinement procedure using a qualitative model of dependencies among system variables is sketched out.

Sign in / Sign up

Export Citation Format

Share Document