Artificial Neural Networks (ANN) is implemented to build a maintenance action recommendation system for Bulk Good System (BGS). The BGS contains typical components of an industrial facility that operates with bulk goods i.e. storage containers, conveyors, weighing systems, control systems, sensor technology, Human-Machine-Interface, etc. The proposed ANN-based system can predict machine condition based on the data inputs it gets and recognizes machine trends based on some given features.

1. Maintenance Action
Recommendation System for
Bulk Good System using
Artificial Neural Networks
Presented By
Ashik Takvir
Alaul Ashraf Showrav
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2. Agenda
 Introduction
 Problem Statement
 Artificial Neural Network (ANN)
 Understanding Datasets
 Implementation of ANN
 Results
 Recommendation System
 Conclusion
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3. Introduction
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Artificial Neural
Network
Fault detection
Machine
availability
• Fault detection. • Predicts machine
health.
• Predicts Maintenance
action.
• Uninterrupted
production.
• Higher accuracy
and efficiency.

4. Problem Statement
Bulk Good System:
 Four stations.
 Each station has five sensors.
 Overall system health determined by individual station health.
 Implementation of ANN in MATLAB environment to predict system
health.
 Testing, validating and comparing prediction with actual data.
 Recommend maintenance action.
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5. Artificial Neural Network (ANN)
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 Computing systems inspired by
the biological neural networks.
 Based on a collection of nodes
called artificial neurons.
 Consist of input layer, hidden
layer and output layer.
Fig: Analogy of ANN with biological neuron
Fig: A simple neural network

6. Understanding datasets
Two normalized datasets: training and testing.
3300 and 1556 readings for training and testing respectively.
Station or system health condition classified as:
1 = Normal (Health = 1)
2 = Warning (Health = 0.8)
3 = Critical (Health = 0.7).
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7. Implementation of ANN
 Neural Network Toolbox in MATLAB is used.
 Feedforward Backpropagation is used for weights and bias values.
 Ten neurons per hidden layer.
 TANSIG and PURELIN activation function.
 Training data divided into three parts: Training, Validation and Testing.
 Performance and regression graph is analysed to detect any overfitting.
 Toolbox iterations stop at best mean-squared error (MSE).
 After training, the ANN is tested using 1556 set of testing data.
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8. 8
Station
1
Station
2
Station
3
Station
4
System
Sensor
signals
Input Station
health as
input
Final
Output
Initial
output
Overall
system
health
Station health
condition
Implementation of ANN

9. Implementation of ANN
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Fig: Neural Network Block for the overall system
Fig: Neural Network Block for four stations

10. Implementation of ANN
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Fig: Station 1 performance and regression graphs.

11. Implementation of ANN
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Fig: System performance and regression graphs.

12. Results
System health
Condition
Predicted
output
(average)
Actual output Accuracy (%) Overall
accuracy (%)
Normal 605 617 98 89
Warning 788 811 97
Critical 163 128 73
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Taking the average of twelve trials, comparisons between predicted output and
actual output is given below.

13. Recommendation System
 Code is written using MATLAB for the recommendation system algorithm.
 Operator will see the overall system health value.
 If health >= 0.9, system is NORMAL.
 If health < 0.9 and >= 0.75, system is in WARNING state.
 If health < 0.75, system is in CRITICAL state.
 Operator’s action is explained by the following flowchart.
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14. Recommendation System
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System
health
Normal
Warning
Future
Maintenance
Instant
Maintenance
Lowest
station
health
Lowest
sensor
mean
Maintenance
of faulty
sensor
Critical Instant
Maintenance

15. Conclusion
 System health condition of BGS is predicted using ANN.
 Predictions are fairly accurate.
 Reliable for future maintenance scheduling.
 More training data and more neurons may improve its accuracy.
 Other classifier tools such as Bayes classification, SVM can be used.
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