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The purpose of this paper is to build a monitoring scheme in order to detect and subsequently eliminate abnormal behavior of the concerned casting process so as to produce worm wheels with good quality characteristics.

In this a study, a process monitoring strategy has been devised for a centrifugal casting process using data-based multivariate statistical technique, namely, partial least squares regression (PLSR).

Based on a case study, the PLSR model constructed for this study seems to mimic the actual process quite well which is evident from the various performance criteria (predicted and analysis of variance results).

The practical implication of the study involves development of a software application with a back-end database which would be interfaced with a computer program based on PLSR algorithm for estimation of model parameters and the control limit for the monitoring chart. It would help in easy and real-time detection of faults.

This study concerns the application of a PLSR-based monitoring strategy to a centrifugal casting process engaged in the production of worm wheel.

The purpose of this paper is to overcome the application limitations of other multi-variable regression based on polynomials due to the huge computation room and time cost.

In this paper, based on the idea of feature selection and cascaded regression, two strategies including Laguerre polynomials and manifolds optimization are proposed to enhance the accuracy of multi-variable regression. Laguerre polynomials were combined with the genetic algorithm to enhance the capacity of polynomials approximation and the manifolds optimization method was introduced to solve the co-related optimization problem.

Two multi-variable Laguerre polynomials regression methods are designed. Firstly, Laguerre polynomials are combined with feature selection method. Secondly, manifolds component analysis is adopted in cascaded Laguerre polynomials regression method. Two methods are brought to enhance the accuracy of multi-variable regression method.

With the increasing number of variables in regression problem, the stable accuracy performance might not be kept by using manifold-based optimization method. Moreover, the methods mentioned in this paper are not suitable for the classification problem.

Experiments are conducted on three types of datasets to evaluate the performance of the proposed regression methods. The best accuracy was achieved by the combination of cascade, manifold optimization and Chebyshev polynomials, which implies that the manifolds optimization has stronger contribution than the genetic algorithm and Laguerre polynomials.

Monitoring of a process leading to the detection of faults and determination of the root causes are essential for the production of consistent good quality end products with improved yield. The history of process monitoring fault detection (PMFD) strategies can be traced back to 1930s. Thereafter various tools, techniques and approaches were developed along with their application in diversified fields. The purpose of this paper is to make a review to categorize, describe and compare the various PMFD strategies.

Taxonomy was developed to categorize PMFD strategies. The basis for the categorization was the type of techniques being employed for devising the PMFD strategies. Further, PMFD strategies were discussed in detail along with emphasis on the areas of applications. Comparative evaluations of the PMFD strategies based on some commonly identified issues were also carried out. A general framework common to all the PMFD has been presented. And lastly a discussion into future scope of research was carried out.

The techniques employed for PMFD are primarily of three types, namely data driven techniques such as statistical model based and artificial intelligent based techniques, priori knowledge based techniques, and hybrid models, with a huge dominance of the first type. The factors that should be considered in developing a PMFD strategy are ease in development, diagnostic ability, fault detection speed, robustness to noise, generalization capability, and handling of nonlinearity. The review reveals that there is no single strategy that can address all aspects related to process monitoring and fault detection efficiently and there is a need to mesh the different techniques from various PMFD strategies to devise a more efficient PMFD strategy.

The review documents the existing strategies for PMFD with an emphasis on finding out the nature of the strategies, data requirements, model building steps, applicability and scope for amalgamation. The review helps future researchers and practitioners to choose appropriate techniques for PMFD studies for a given situation. Further, future researchers will get a comprehensive but precise report on PMFD strategies available in the literature to date.

The review starts with identifying key indicators of PMFD for review and taxonomy was proposed. An analysis was conducted to identify the pattern of published articles on PMFD followed by evolution of PMFD strategies. Finally, a general framework is given for PMFD strategies for future researchers and practitioners.

The deformation of a large fuselage panel is unavoidable due to its weak-stiffness and low-rigidity. Sometimes, the assembly accuracy of the panel is out of tolerance. The purpose of this paper is to propose a method to predict and correct the assembly deformation of a large fuselage panel during digital assembly by using a finite element (FE) analysis and partial least squares regression (PLSR) method.

A FE model is proposed to optimize the layout of load-transmitting devices to reduce panel deformation after the process of hoisting and supporting. Furthermore, another FE model is established to investigate the deformation behavior of the panel. By orthogonal simulations, the position error data of measurement points representing the precision of the panel are obtained. Then, a mathematical model of the relationship between the position errors of measurement points on the panel and the displacements of numerical control positioners is developed based on the PLSR method.

The case study shows that the model has a high level of computing accuracy and that the proposed method is an efficient way to correct the panel deformation in digital assembly.

The results of this study will enhance the understanding of the deformation behavior of a panel in aircraft digital assembly and help to improve the assembly precision systematically and efficiently.

In aircraft wing–fuselage assembly, the distributed multi-point support layout of positioners causes fuselage to deform under gravity load, leading to assembly difficulty and assembly stress. This paper aims to propose a hybrid force position control method to balance aerodynamic shape accuracy and deformation of assembly area, thereby correcting assembly deformation and reducing assembly stress.

Force and position control axes of positioners are selected based on screw theory and ellipsoid method. The position-control axes follow the posture trajectory to align the fuselage posture. To exert force on the fuselage and correct the deformations, the force-control axes follow the contact force derived by using orthogonal experiments and partial least squares regression (PLSR). Finite element simulation and one-dimension deformation correction experiment are conducted to verify the validity of this method.

Simulation results indicate that hybrid force position control method can correct assembly deformation and improve the wing–fuselage assembly quality significantly. Experiment on specimen verifies the effect of this method indirectly.

The proposed method gives a solution to solve the deformation problem during aircraft wing-fuselage assembly, thereby reducing assembly stress and improving assembly quality.

The purpose of this paper is to examine descriptive sensory characteristics and consumer acceptability of eight commercial ready-to-eat cooked rice samples by 8 trained panelists and 50 consumers.

A total of 24 descriptive attributes for appearance, odor/aroma, taste/flavor, and texture were developed. Also Consumer Acceptability (CA) was performed for overall liking, appearance, flavor, and texture liking. All statistical analyses were using analysis of variance, principal component analysis (PCA), hierarchical cluster analysis (HCA), and partial least square regression (PLSR).

The overall liking score for the cooked white rice from C brand was the highest (6.43) among the eight samples. Three groups of eight commercial ready-to-eat cooked rice samples were obtained from PCA and HCA. The samples of cooked white rice from C, N, and O brand characterized by intactness, starch odor, translucency, whiteness, and glossiness were located on to the positive PLS 1, whereas the samples of cooked white rice from D and E brand characterized by scorched odor, cohesiveness, stickiness, and moistness were located on the negative side of PLS 2 in the PLSR analysis.

Further studies on the improvement of sensory quality for brown rice are necessary to increase CA in terms of health functionality of brown rice.

Through an analytical and comparative study, examines the development and current status of the USA’s and China’s national library statistical systems, their natural functions and key system characteristics, usability of performance measures, and each system’s strengths and shortcomings. A distinction is made between government‐centred and profession‐centred systems. Identifies four factors which influence the systems’ characteristics. By finding commonalities and differences between the two statistical systems, provides library professionals, governmental administrators, and educators with the opportunity to learn from each other’s practices and experiences, to conduct further investigations into the purpose and characteristics of the statistical systems, and ultimately, to improve nationwide library planning and evaluation in their own country.

The current challenge for forecasting smart meters electricity consumption lies in the uncertainty and volatility of load profiles. Moreover, forecasting the electricity consumption for all the meters requires an enormous amount of time. Most papers tend to avoid such complexity by forecasting the electricity consumption at an aggregated level. This paper aims to forecast the electricity consumption for all smart meters at an individual level. This paper, for the first time, takes into account the computational time for training and forecasting the electricity consumption of all the meters.

A novel hybrid autoregressive-statistical equations idea model with the help of clustering and whale optimization algorithm (ARSEI-WOA) is proposed in this paper to forecast the electricity consumption of all the meters with best performance in terms of computational time and prediction accuracy.

The proposed model was tested using realistic Irish smart meters energy data and its performance was compared with nine regression methods including: autoregressive integrated moving average, partial least squares regression, conditional inference tree, M5 rule-based model, k-nearest neighbor, multilayer perceptron, RandomForest, RPART and support vector regression. Results have proved that ARSEI-WOA is an efficient model that is able to achieve an accurate prediction with low computational time.

This paper presents a new hybrid ARSEI model to perform smart meters load forecasting at an individual level instead of an aggregated one. With the help of clustering technique, similar meters are grouped into a few clusters from which reduce the computational time of the training and forecasting process. In addition, WOA improves the prediction accuracy of each meter by finding an optimal factor between the average electricity consumption values of each cluster and the electricity consumption values for each one of its meters.