Towards Industry 4.0: Machine malfunction prediction based on IIoT streaming data
Date Issued
2023-09-17
Author(s)
Nikolova, Dragana
Pires, Ivan Miguel
Abstract
The manufacturing industry relies on continuous
optimization to meet quality and safety standards, which is part
of the Industry 4.0 concept. Predicting when a specific part of a
product will fail to meet these standards is of utmost importance
and requires vast amounts of data, which often are collected
from variety of sensors, often reffered to as Industrial Internet
of Things (IIoT). Using a published dataset from Bosch, that
describes the process at every step of production, we aim to train
a machine learning model that can accurately predict faults in
the manufacturing process. The dataset provides two years of
production data across four production lines and 52 stations.
Considering that the data generated from each production part
includes more than four thousand features, we investigate various
feature selection and data preprocessing methods. The obtained
results exhibit Area Under the Receiver Operating Characteristic
Curve (AUC ROC) of up to 0.997, which is remarkable and
promising even for real-life production use.
optimization to meet quality and safety standards, which is part
of the Industry 4.0 concept. Predicting when a specific part of a
product will fail to meet these standards is of utmost importance
and requires vast amounts of data, which often are collected
from variety of sensors, often reffered to as Industrial Internet
of Things (IIoT). Using a published dataset from Bosch, that
describes the process at every step of production, we aim to train
a machine learning model that can accurately predict faults in
the manufacturing process. The dataset provides two years of
production data across four production lines and 52 stations.
Considering that the data generated from each production part
includes more than four thousand features, we investigate various
feature selection and data preprocessing methods. The obtained
results exhibit Area Under the Receiver Operating Characteristic
Curve (AUC ROC) of up to 0.997, which is remarkable and
promising even for real-life production use.
Subjects
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