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This paper reviews monitoring and error prediction of
PLC-program using Neural Network. In the PLC-device
controlled manufacturing line, PLC-program holds place
of underlying component. It becomes controlling
mechanism. The level of automation in the production linerelies on control mechanism practiced. In the modern
manufacturing, PLC devices can handle whole productionline given that structured and smart PLC-program is
executed. In other words, PLC-program can managewhole process structure consisting set of procedures. We
present a method to monitor PLC-program and PLC
error prediction it using neural network. The neural
network method being predictive in nature, it rigorouslycan monitor process signals from sensors, sensed during
operation of PLC devices or execution of PLC-program.
Subsequently, a neural network algorithm practiced for
the analysis of signals. In this way, thorough monitoring of PLC-program can find possible errors from temporal
parameters (e.g. Voltage, bias etc). In addition, possible
alterations in program and irregularities can beminimized. That can result, easily to use in fault detection,maintenance, and decision support in manufacturing
organization. Similarly, it can lessen down-time of
Akesson [6] have practiced discrete event systems. They
have used EFA (Extended Finite Automata) as modeling
tool and finding faults, particularly this work can be seen
more focused on extension of finite automata and
modeling. Similarly, in the work of PLC diagnosis, Z. D.
Zhou, Y. P. Chen, J. Y. H. Fuh, & A. Y. C. Nee have
approached distinct methodology, which combines bothhardware and software. They have presented work
structurally using hybrid strategy with multiple sensors
and multi-associated parameters in the system [7].
However, their work can be seen as inclined to hardwareimplementation to avoid faults. Some notably advance
works has been carried out in PLC monitoring by Hao
Zhang, Jianfeng Lu, Yunjun Mu, Shuogong Zhang,
Liangwei Jiang [8]. In their paper, online monitoring of PLC has been illustrated. They have developed BPMS
(Bao-steel PLC Monitoring System) application for the
monitoring which runs on PC. Although, their work
stresses on development of PLC monitoring system, detaildescription of mechanism is not explained. Recently, the
use of neural network in the diagnosis of PLC can be seen
in the paper of Magdy M. Abdelhmeed, Houshang
Darabi [9]. Particularly, they have applied RNN(Recurrent Neural Network), a type of ANN for diagnosis
and debugging of PLC-program. In their work, they have
proposed an algorithm for the conversion of LLD (a type
of PLC-program). The algorithm with time-delay in
hidden layers outputs has been applied to convert LLD
into a RNN; subsequently, they carry out fault detection
process on transformed data. Although their work on
monitoring is in-depth, however in real scenario diagnosis
work can be carried out without transforming PLC-
program in to ANN. Hence, their work can be considered
redundant. In addition, the application of RNN becomescomplex and takes high computing time relative to other
ANNs.
Most of works on diagnosis and fault detection of PLC-
program seem to be focused on particular side. Most of
the methodologies applied are concerned with discrete
event system [6], where in real system PLC involves
continuous or analog values. Some others apply new
methods however computing time and efficiencies are
ignored [9]. To overcome, these two major limitations,
fully connected feed-forward neural network can beapplied for the fault diagnosis and monitoring of PLC-
controlled manufacturing line. First of all, diagnosis
process takes place in data-value in which PLC-programrelies on. In other hand, feed-forward with widely used back-propagation learning algorithm is used in this work,
explained in section 4.
3. Background
When we talk about fault-detection in PLC-program,
we particularly focus on to locate alterations in the valid
PLC-program sequence. These faults in PLC-program can be found continuous observations of PLC-program
variables. In the controlling of manufacturing line, PLCs
are deployed which are programmable. The valid PLC-
program is working program in real PLC device whichallows machines to behave normally, as per instructions
given. Because of different process parameters such as
sensor inputs there is always chance of being modification
in original valid PLC-program sequence. In other way, the
objective of monitoring becomes finding errors or
alterations in program sequence. When there are
alterations in program sequence i.e. it doesn’t match with
original valid program sequence, refers that there exists
fault. In our work, we adopt neural network for
monitoring purpose as suitable method, with appropriate
learning algorithm, back-propagation. Determining the
network architecture is one of the most important and
difficult tasks in the development of ANN models. Ingreat extent, the efficiency of ANN depends upon
architecture modeler, since there are some judgmental
factors which have to be decided on design time of network. It requires the selection of the number of hidden
layers and the number of nodes in each of these. It has
been shown that a network with two layers, where the
hidden layer is sigmoid and the output layer is linear, can be trained to approximate any function provided that