Functional Neural Network Control Chart

In many Industry 4.0 data analytics applications, quality characteristic data acquired from manufacturing processes are better modeled as functions, often referred to as profiles. In practice, there are situations where a scalar quality characteristic, referred to also as the response, is influenced by one or more variables in the form of functional data, referred to as functional covariates. To adjust the monitoring of the scalar response by the effect of this additional information, a new profile monitoring strategy is proposed on the residuals obtained from the functional neural network, which is able to learn a possibly nonlinear relationship between the scalar response and the functional covariates. An extensive Monte Carlo simulation study is performed to assess the performance of the proposed method with respect to other control charts that appeared in the literature before. Finally, a case study in the railway industry is presented with the aim of monitoring the heating, ventilation and air conditioning systems installed onboard passenger trains

A comparison study of distribution-free multivariate SPC methods for multimode data

The data-rich environments of industrial applications lead to large amounts of correlated quality characteristics that are monitored using Multivariate Statistical Process Control (MSPC) tools. These variables usually represent heterogeneous quantities that originate from one or multiple sensors and are acquired with different sampling parameters. In this framework, any assumptions relative to the underlying statistical distribution may not be appropriate, and conventional MSPC methods may deliver unacceptable performances. In addition, in many practical applications, the process switches from one operating mode to a different one, leading to a stream of multimode data. Various nonparametric approaches have been proposed for the design of multivariate control charts, but the monitoring of multimode processes remains a challenge for most of them. In this study, we investigate the use of distribution-free MSPC methods based on statistical learning tools. In this work, we compared the kernel distance-based control chart (K-chart) based on a one-class-classification variant of support vector machines and a fuzzy neural network method based on the adaptive resonance theory. The performances of the two methods were evaluated using both Monte Carlo simulations and real industrial data. The simulated scenarios include different types of out-of-control conditions to highlight the advantages and disadvantages of the two methods. Real data acquired during a roll grinding process provide a framework for the assessment of the practical applicability of these methods in multimode industrial applications

Monitoring roundness profiles based on an unsupervised neural network algorithm

In modern manufacturing, approaches for profile monitoring can be adopted to detect unnatural behaviors of production processes, i.e. to signal when the relationship used to represent the profiles changes with time. Most of the literature concerned with profile monitoring deals with the problem of model identification and multivariate charting of parameters vector. In this paper, a different approach, which is based on an unsupervised neural network, is presented for profile monitoring. The neural network allows a computer to automatically learn from data the relationship to represent in-control profiles. Then, the algorithm may produce a signal when an input profile does not fit to the prototype learned from the in-control ones. The neural network does not require an analytical model for the statistical description of profiles faced (model-free approach). A comparison study is provided in this paper, in which the Phase II performance of the neural network is compared to that of approaches representative of the industrial practice. Performance is assessed by computer simulation, with reference to a case study related to profiles measured on machined items subject to geometrical specification (roundness). The results indicate that the neural network may outperform usual control charts in signaling out-of-control conditions, due to spindle-motion errors in several production scenarios. The proposed approach can be considered a valuable option for profile monitoring in industrial applications

Monitoring roundness profiles based on an unsupervised neural network algorithm

SCOPUS eid=2-s2.0-79952627449 - In modern manufacturing, approaches for profile monitoring can be adopted to detect unnatural behaviors of production processes, i.e. to signal when the relationship used to represent the profiles changes with time. Most of the literature concerned with profile monitoring deals with the problem of model identification and multivariate charting of parameters vector. In this paper, a different approach, which is based on an unsupervised neural network, is presented for profile monitoring. The neural network allows a computer to automatically learn from data the relationship co represent in-control profiles. Then, the algorithm may produce a signal when an input profile does not fit to the prototype learned from the in-control ones. The neural network does not require an analytical model for the statistical description of profiles faced (model-free approach). A comparison study is provided in this paper, in which the Phase II performance of the neural network is compared to that of approaches representative of the industrial practice. Performance is assessed by computer simulation, with reference to a case study related to profiles measured on machined items subject to geometrical specification (roundness). The results indicate that the neural network may outperform usual control charts in signaling out-of-control conditions, due to spindle-motion errors in several production scenarios. The proposed approach can be considered a valuable option for profile monitoring in industrial applications. (C) 2011 Elsevier Ltd. All rights reserved