...-of-the-art classifiers such as artificial neural networks, support vector machines, and random forests are implemented and compared for handling multi-fault diagnosis using programmable logic controller signal data. For unsupervised learning, classifiers based on principal component analysis utilizing major...

... (CAE) for unsupervised feature extraction. A multiclass extension for semi-supervised anomaly diagnosis is proposed that utilizes principal component analysis (PCA) as the basis for anomaly scoring, and the proposed approach intersects the results of targeted one-against-all phases on partially labeled...

... formation is kept as low as is required for meeting customer requirements. Principal component analysis is used to reduce the dimensionality of the dataset while retaining the majority of the variability in the process variables. It was found that a multinomial logit model containing these components...

...-by-station test in a forging process. Afterwards, the principal component analysis is conducted on the segmented tonnage signals to generate the principal component (PC) features to be selected for designing the classifier. Finally, the optimal selection of PC features is integrated with the design...

... 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...

... equipment maintenance engineering condition monitoring principal component analysis regression analysis fast Fourier transforms A shaft transmission system is one of the most fundamental and important parts of rotating machinery. The ability to estimate and predict shaft alignment...

...Y. G. Liu; S. J. Hu A new approach to fixture fault diagnosis, designated component analysis (DCA), is proposed for automotive body assembly systems using multivariate statistical analysis. Instead of estimating the fault patterns solely from the process data as in principal component analysis (PCA...

... deformation and springback. This paper discusses the effect of geometric covariance in the calculation of assembly variation of compliant parts. A new method is proposed for predicting compliant assembly variation using the component geometric covariance. It combines the use of principal component analysis...

... , S. J. , and Wu , S. M. , 1992 , “ Identifying Sources of Variation in Automobile Body Assembly Using Principal Component Analysis ,” Transactions of NAMRI/SME , XX , pp. 311 – 316 . Ceglarek , D. , and Shi , J. , 1996 , “ Fixture Failure Diagnosis for Autobody Assembly Using...

... control fault diagnosis state-space methods principal component analysis Dimensional quality, represented by product dimension variability, is one of the most critical challenges in industries which use multistage manufacturing processes such as assembly and machining for automotive, aerospace...

... principal component analysis The dimensional integrity of an automotive body has tremendous impact on the quality of the final vehicle. A typical body-in-white (BIW), which is the automotive body without closure panels such as the doors, hood, and deck lid, and without paint applied, consists...

... procedure combines principal component analysis (PCA) of measurement data and fault pattern recognition using statistical hypothesis tests. Verification of the proposed method is presented through simulations and one case study. [S1087-1357(00)02502-8] Compliant assemblies are widely used in automotive...

... variable interactions by using a fractional factorial design of experiments (DOE). In this methodology, features are extracted by using principal component analysis (PCA) to represent variation patterns of tonnage signals. Regression analyses are performed to model the relationship between features...