PLC Control Logic Error Mo

7/27/2019 PLC Control Logic Error Mo

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PLC control logic error monitoring and prediction using Neural Network

In-Sung Jung, BM Mulman, Devinder Thapa, lock-jo koo, Jae-Ho Bae, Sang-hyun Hong, Sungjoo

Yeo ,C.M. Park, S.C. Park, Gi-Nam Wang

Department of Industrial Engineering Ajou University 442-749, Korea

[email protected],{mulmanz, debu, lockjo9, jhbae, hsh3298, oriheap, cmpark, scpark, gnwang}@ajou.ac.kr

Abstract

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

machines and prevent possible risks.

Keywords: PLC, Artificial Neural Network (ANN), Fault-detection, Error prediction, Monitoring

1. Introduction

In the modern manufacturing, the PLC is well-adopted to

a range of automation tasks. These are typically industrial

processes where changes to the system would be expected

during its operational life and the production systems thatfeature cost of maintaining is relatively higher than cost

of automation [1]. PLC is special-purpose computer,

which is designed for multiple input and output

arrangements, extended temperature ranges, immunity toelectrical noise, and resistance to vibration and impact.

The reason behind increasing popularity of PLC

(Programmable Logic Controller) is flexibility in control;

the possible changes in manufacturing controlling are performed through PLC-program. The PLC-program

determines automation level of a manufacturing industry.

In other words, the whole process structure of production

line can be modeled and controlled by providing set of

instructions to PLC. In this way, PLC-program becomes

underlying component of modern manufacturing.

However, because of PLC’s nonflexible programming

system relative to high level languages, their ability in

fault detection and diagnosis is limited. The continuous

monitoring of PLC-program is vital to decrease machinedown-times, safety-critical reasons and prevent potential

risks. The diagnosis of PLC-program becomes difficult

because of data characteristic involved in process: analog

and discrete [2]. PLC devices execute programs scanning

continuously and operate involved machines sending

instructions as I/O in the discrete or digital format.

However, pressure, temperature, flow, and weights are

often represented with integer values. Hence, input and

output signals are represented in either binary or integer values; there are always chances of alterations in the

values in the real time running production line. That is,

the originally sound PLC-program may behave

abnormally due to the changes in input and output values.Using neural network for fault diagnosis is not common

as for vision or speech processing, however many

successful applications have been reported notably [3].

ANNs are a form of artificial intelligence, which, bymeans of their architecture, attempt to simulate the

biological structure of the human brain and nervous

system. Although the concept of artificial neurons was

first introduced in 1943, research into applications of ANNs has blossomed since the introduction of the back-

propagation training algorithm for feed-forward ANNs in

1986 [4]. ANNs may thus be considered a relatively new

tool in the field of prediction and forecasting. When feed-forward ANNs are used for prediction and forecasting, the

modeling philosophy employed is similar to that used inthe development of more conventional statistical models.

In both cases, the purpose of the model is to capture therelationship between a historical set of model inputs and

corresponding outputs. This is achieved by repeatedly

presenting examples of the input/output relationship to the

model and adjusting the model coefficients in an attemptto minimize an error function between the historical

outputs and the outputs predicted by the model. Although

Fourth International Conference on Natural Computation

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DOI

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978-0-7695-3304-9/08 $25.00 © 2008 IEEE

DOI

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978-0-7695-3304-9/08 $25.00 © 2008 IEEE

DOI 10.1109/ICNC.2008.776

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some ANN models are not significantly different from a

number of standard statistical models, they are extremely

valuable as they belong to the class of data driven

approaches, whereas conventional statistical methods are

model driven. In the former, the data are used to

determine the structure of the model as well as the

unknown model parameters. The use of ANN models may

thus overcome the limitations of the traditional methods.Particularly, it is found suitable for prediction since neural

networks are best at identifying patterns or trends in data.

In our work, we design a neural network which

is trained using back propagation learning algorithm. The

trained network is used to predict valid PLC-program

Input and output (I/O) values and it’s pattern. At the end,

the performance of the network in predicting these

process parameters is studied

2. Literature review

More and more researchers and industrial partners are

attracted to this area, fault detection and monitoringsystem. So far, some relevant diagnostic methods have

been proposed. W. Hu, M. Schroeder, & A.G. Starr [5]

have proposed knowledge-based real-time PLC diagnosis

system. Their work is focused on acquiring knowledge

from the pneumatic & hydraulic circuit diagrams and

PLC-program. Later, simply, they retrieve PLC-data and

identify possible faults. In the process observation and

fault detection, Tord Alenjung, Markus, Bengt & Knut

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

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sufficient connection weights are used [10,11].

Consequently, we use one hidden layer in this work. Thenumber of nodes in the input and output layers are

restricted by the number of model inputs and outputs. The

input layer of the ANN model developed in this work has

two nodes, one for relay input and PLC-program input i.e. binary value. Similarly, the output layer has two nodes for

valid two bits, 0 & 1. The physical interpretation of theconnection weights is important; hence the smallest

network that is able to map the desired relationship should be used. Consequently, the model that has the optimum

number of nodes giving an optimum generalization is

retrained a number of times with different weights and

biases until no further improvement occurs. The modelarchitecture is shown below in Fig. 1.

Fig. 1 Two-layer feed forward Neural Network (fully

connected

4. Methodology

The steps for developing ANN models, as outlined by

Maier and Dandy [12], are used as guide in this work.

These include the determination of model inputs andoutputs, division and preprocessing of the available data,

the determination of appropriate network architecture,optimization of the connection training weights, stopping

criteria, and model validation. MATLAB is used tosimulate ANN operation in this work. The training

method used in this work back-propagation algorithm, is

considered a generalization of the delta rule for nonlinear

activation functions and multilayer networks. Thismethod is widely used supervised learning method

because of its weight error correct rules, can be illustrated

as follows:

This prediction model could be designed as follows. isthe estimated output, and is the corresponding residual,

t t t t e X NN O y ˆ)(ˆ +== (1)

The back-propagation training algorithm is an iterative

gradient designed to minimize the mean square error

between the actual output of multi-layer feed forward perceptron and the desired output. It requires continuous

differentiable non-linearity. The following assumes a

sigmoid logistic nonlinearity.

Step1: Initialize weights and offsets all weights and node

offsets to small random values.Step2: Present input and desired outputs

Present a continuous valued input vector X0, X1…..X N-1

and specify the desired output d0,d1,….dM-1. If the net is

used as a classifier them all desired outputs are typicallyset to zero except for that corresponding to the class the

input is from. That desired output is 1. The input could benew on each trial or samples from a training set could be

presented cyclically until stabilize.Step 3: Calculate Actual Output

Use the sigmoid non linearity from above and formulas

as in fig 3 to calculate output y0,y1….yM-1. Step 4: Adapt weights

Use a recursive algorithm starting at the output nodes and

working back to the first hidden layer. Adjust weights by')()1( i jijij xnt wt w δ +=+ (2)

In this equation )(t w ijis the weight from hidden node i

or from an input to node j at time t, '

jw , is either the

output of node i or is an input, η is a gain term, and jδ ,

is an error term for node j , if node j is an output node,

then

))(1( j j j j j yd y y −−=δ (3)

where jd is the desired output of node j and j y is the

actual output.

If node j is an internal hidden node, then

∑−=

k

jk

m

j j j j w x x δ δ )1( '' (4)

where k is over all nodes in the layers above node j .

Internal node thresholds are adapted in a similar manner

by assuming they are connection weights on links fromauxiliary constant-valued inputs. Convergence issometimes faster if a momentum term is added and

weight change are smoothed by

))1()(()()1('

−−++=+ t wt w xnt wt w ijiji jijij α δ ,where 0<α <1. (5)

Step 5: Repeat by going to step 2

5. Result & Discussion

The process of optimizing the connection weights is

known as ‘‘training’’ or ‘‘learning’’. This is equivalent to

the parameter estimation phase in conventional statisticalmodels. The aim is to find a global solution to what is

typically a highly nonlinear optimization problem. As in

our work, we do not consider time parameter so, feed-

forward network is used, rather than recurrent. The

method most commonly used for finding the optimum

weight combination for feed-forward neural networks is

the back-propagation algorithm, which is based on first-

order gradient descent. In this study, the general strategy

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adopted for finding the optimal parameters that control

the training process is as follows. For each trial number of hidden layer nodes, random initial weights and biases are

generated. The network is trained using the training data

set and validated with the validation data set. all the

illustrative diagrams are described as follows:

Fig 2 Plotted Raw data

Fig 3 Transformation (digitized)

Fig 4 Result of Simulation

In figure 2, the raw data from relays is plotted, strength of

signal being observed in vertically and time in horizontal

axis. Here, transformation of relay input values into

discrete is our first objective, which is accomplished.Those values are transformed into discrete or binary value

in figure 3 using threshold values described in section 4.

In figure 4, simulation result can be seen where asundesirable, predicted using applying 2 hidden layers. In

comparison to figure 4, the prediction is perfect in figure

5, which is produced using only one hidden layer. In this

way, more hidden layers cannot be considered alwayssuperior to less hidden layers, also can be seen in fig 6.

Table 1. Setting parameter of feed-forward neural

network INPUT HIDDEN

LAYER NODEnum

ITERATIO N

RESULT

2 1 2 100 BELOW 1002 1 3 45 BELOW 100

2 1 4 18 OK 100

2 1 5 16 100

2 1 6 15 100

2 1 8 14 100

Fig 6 Simulation result

Fig 7 using 1 hidden layer learning rates

Fig 8 using 2 hidden layers Learning rates

In the above figure (7, 8), the number of iteration is

mapped horizontally and error rate as vertically. From thefigure, higher the number of iterations lower will be error

rate. The error in figure 4 is less than in figure 3. Figure 4

is considered to be suitable; in 16 iterations it has got

error rate less than 0.005. In addition, the setting our feed-

forward neural network setting is described in the table 1.

Table 2. Setting parameter of feed-forward neural

network for sequence predictionINPUT HIDDEN

LAYER NODEnum

ITERATIO N

Error

3 1 3 50 0.0478

3 1 4 16 0.0438

3 1 5 18 0.0467

3 1 6 12 0.0457

3 1 7 15 0.0497

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Fig 9 PLC programming sequence data

Fig 9 Event of PLC programming prediction

In the above figure 7 is PLC logic sequence and figure 8

is to predict the sequence. It is useful for the logic

sequence operation error. The error is less than 0.05. In

addition, the setting our feed-forward neural network

setting is described in the table 2.

6. Conclusion

Monitoring of PLC controlled factory floor involvesobservation of PLC-program. PLC- control program

being lower level and inflexible in terms of programming,it becomes difficult to debug and troubleshoot. To detect

faults and abnormality, robust methods are required for prediction. Neural network is found to be suitable method

because of its inherent nature, parallel processing and

predictive. In this research, useful result has been

achieved Fig 4, in transformation of integer value intodigital. Importantly, prediction rate has been achieved

99.995%. This method, in the future, may be further

applied to other manufacturing processes too.

Acknowledgement

This research has been partially funded by BK21 (Brain

Korea 21), and UDMT Korea.

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PLC Control Logic Error Mo

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