MTF-industrial-process

Data and R code to reproduce similar results to those described in the paper "An Adaptive Learning Approach to Multivariate Time Forecasting in Industrial Processes" (Miguelez et al., 2024). Notice that we use a smaller data set simply to reduce computational time.

Data

This folder contains the dataset oee_data_4weeks.Rsd, a subset of the complete dataset comprising data from week 21 to week 24 of the year 2020, with the following variables:

• lsp: observation period id
• day: starting date
• hour: starting hour
• sh.id: work shift id
• new.sh: 1=first observation of the work shift, 0=otherwise
• wday: weekday
• tday: shift (Morning, Afternoon, Night)
• week.id: week id
• inistop: 1=the machine is inactive at the beginning of the period, 0=the machine is active
• of.id: production order id
• new.of: 1=first observation of the production order, 0=otherwise
• ref: reference
• ics: ideal cycle speed, units per minute
• TU: total units
• DU: defective units
• TgU: target units, OpT$\times$ics
• OT: period length, minutes
• SBT: stand-by time, minutes
• LT: loading time = OT-SBT
• rcs: real cycle speed = TU/LT
• lo: loading rate = LT/OT
• DT: downtime, minutes
• OpT: operating time = LT-DT
• av: availability rate = OpT/LT
• PLT: performance losses time, minutes
• NOpT: net operating time = OpT-PLT
• pf: performance rate = NOpT/OpT
• QLT: quality losses time, minutes
• VT: valuable time = NOpT-QLT
• qu: quality rate = VT/NOpT
• oee: av $\times$ pf $\times$ qu
• nstops: number of stops
• hum: % of humidity
• temp: temperature in ºC
• wPT: work shift time
• av.level: availability level ([80-100%]: very good, [60-80%): acceptable, [40-60%): improvable, [0-40%): very poor)
• pf.level: performance level
• qu.level: quality level
• oee.level: oee level

MVTF

This folder contains all the code required to run the multivariate and univariate models and reproduce figures similar to Figures 5.1 and 5.2 of the paper.

• functions: R script with some auxiliary functions that will be used to run the model, including functions for the clustering step (section 4.3 in the paper).
• update_model: R code for parameter estimation and response prediction, including Algorithm 1 and Algorithm 2 of the paper.
• theme_mtf: customized theme for figures.
• run_mv_model: R code to run the multivariate version of the model using a subset of the whole dataset comprising 4 consecutive weeks of data. Responses $\mathbf y_n$, covariates $\mathbf x_n$ and classification variables $\mathbf t_n$ are chosen as stated in Section 5. Different model configurations can be tested by the user changing either of them. The performance of the prediction method is measured using a 4-fold cross-validation technique alternatively using one week as the test set, whereas the remaining three weeks are used to train the model.
• run_uv_model: R code to run the univariate version of the multivariate code above.
• mvtf_figures: R code to reproduce figures similar to Figures 5.1 and 5.2 in the paper.

To ensure the proper working of the code please run the scripts in the following order: run_mv_model - run_uv_model - mvtf_figures.

Computations were run using R-4.2.1.

References

Miguelez, F., Doncel, J. and Ugarte, M.D. (2024). An Adaptive Learning Approach to Multivariate Time Forecasting in Industrial Processes. Submitted. (ArXiv: https://arxiv.org/abs/2403.07554)