{
  "id": "2205.06256",
  "version": "v1",
  "published": "2022-05-12T17:56:29.000Z",
  "updated": "2022-05-12T17:56:29.000Z",
  "title": "Real-time Monitoring of Functional Data",
  "authors": [
    "Fabio Centofanti",
    "Antonio Lepore",
    "Murat Kulahci",
    "Max Peter Spooner"
  ],
  "categories": [
    "stat.AP"
  ],
  "abstract": "With the rise of Industry 4.0, huge amounts of data are now generated that are apt to be modelled as functional data. In this setting, standard profile monitoring methods aim to assess the stability over time of a completely observed functional quality characteristic. However, in some practical situations, evaluating the process state in real-time, i.e., as the process is running, could be of great interest to significantly improve the effectiveness of monitoring. To this aim, we propose a new method, referred to as functional real-time monitoring (FRTM), that is able to account for both phase and amplitude variation through the following steps: (i) registration; (ii) dimensionality reduction; (iii) monitoring of a partially observed functional quality characteristic. An extensive Monte Carlo simulation study is performed to quantify the performance of FRTM with respect to two competing methods. Finally, an example is presented where the proposed method is used to monitor batches from a penicillin production process in real-time.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2022-05-12T17:56:29.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "functional data",
      "real-time monitoring",
      "functional quality characteristic",
      "profile monitoring methods aim",
      "extensive monte carlo simulation study"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}