Citation: Jung, S.; Kim, M.; Kim, B.;
Kim, J.; Kim, E.; Kim, J.; Lee, H.; Kim,
S. Fault Detection for CNC Machine
Tools Using Auto-Associative Kernel
Regression Based on Empirical Mode
Decomposition. Processes 2022, 10,
2529. https://doi.org/10.3390/
pr10122529
Academic Editor: Jie Zhang
Received: 31 October 2022
Accepted: 24 November 2022
Published: 28 November 2022
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Article
Fault Detection for CNC Machine Tools Using Auto-Associative
Kernel Regression Based on Empirical Mode Decomposition
Seunghwan Jung
1
, Minseok Kim
1
, Baekcheon Kim
1
, Jinyong Kim
1
, Eunkyeong Kim
1
,
Jonggeun Kim
2
, Hyeonuk Lee
2
and Sungshin Kim
3,
*
1
Department of Electrical and Electronics Engineering, Pusan National University,
Busan 46241, Republic of Korea
2
Artificial Intelligence Research Center, Korea Electrotechnology Research Institute,
Changwon 51543, Republic of Korea
3
Department of Electrical Engineering, Pusan National University, Busan 46241, Republic of Korea
* Correspondence: sskim@pusan.ac.kr; Tel.: +82-51-510-2374
Abstract:
In manufacturing processes using computerized numerical control (CNC) machines, ma-
chine tools are operated repeatedly for a long period for machining hard and difficult-to-machine
materials, such as stainless steel. These operating conditions frequently result in tool breakage. The
failure of machine tools significantly degrades the product quality and efficiency of the target process.
To solve these problems, various studies have been conducted for detecting faults in machine tools.
However, the most related studies used only the univariate signal obtained from CNC machines.
The fault-detection methods using univariate signals have a limitation in that multivariate models
cannot be applied. This can restrict in performance improvement of the fault detection. To address
this problem, we employed empirical mode decomposition to construct a multivariate dataset from
the univariate signal. Subsequently, auto-associative kernel regression was used to detect faults in
the machine tool. To verify the proposed method, we obtained a univariate current signal measured
from the machining center in an actual industrial plant. The experimental results demonstrate that
the proposed method successfully detects faults in the actual machine tools.
Keywords:
machine tool; fault detection; empirical mode decomposition; auto-associative kernel
regression
1. Introduction
Computerized numerical control (CNC) refers to a method of automating the control
of machine tools by inputting programmed milling information into a microcomputer
without a manual operator. The programmed milling specification is stored in the memory
of the computer to process large amounts of work efficiently. Furthermore, by flexibly
controlling various milling conditions (rotating speed, cutting force, etc.), high-quality
material products can be produced at low costs. However, when the milling process is
running, machine tools frequently suffer from faults, because these are operated in extreme
environments to cut materials [1].
A fault is defined as an unpermitted deviation of at least one characteristic property
of a variable from an acceptable behavior [
2
,
3
]. Although CNC machines have been well
developed, they lack a function that diagnoses the condition of the tools and replaces faulty
tools with new ones. The failure of machine tools requires maintenance time (also known
as downtime) to replace the machine tool by stopping the machine. This may reduce the
quality of the products and efficiency of the target process and increases the maintenance
cost. In addition, if the damaged tool is used continuously to process the material, it may
cause severe physical damage owing to the failure of the CNC machine. In fact, 79.6% of
the maintenance time for CNC machines in modern industry is observed to be caused by
damage to machine tools [
4
]. To solve these problems in the industrial field, when the life
Processes 2022, 10, 2529. https://doi.org/10.3390/pr10122529 https://www.mdpi.com/journal/processes
