2023/11/18 by Murat Külahçı, Kulahci, Murat, Antonio Lepore +5
Decision Sciences · Engineering · #Advanced Statistical Process Monitoring #Applications (stat.AP) #FOS: Computer and information sciences #Fault Detection and Control Systems #Industrial Vision Systems and Defect Detection #Methodology (stat.ME)
paper · pdf · doi:10.48550/arxiv.2311.11050
openalex publication_date 2023/11/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.