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TABLE 6 Cost benefit analysis based on RD-LNN
Gain (byTPR)
Loss (byFPR)
Item
Remark
Cost/occurrence
$10 000/True positive
$100/False positive
Assumed based on the relevant articles
Number of occurrence
124 × 12month = 1488
(2minutes × 30 × 24hour × 365day) − 1488 = 524112
1year
Occurrence Rate
Recall = 12.0%
FPR = 1.9% − $ 995813
Test result
Cost/year Total cost
$1785600
$789787 Abbreviations: BCs, boundary conditions; FPR, false positive rate; RD-LNN, relative distance of the local nearest neighbor; TPR, true positive rate.
The decision tree which consists of three types of nodes (ie, root nodes, decision nodes, and terminal (or leaf) nodes) and branches also shows similar results. As we can expect from variable importance, the most important variable is on the root node which is located on the top of the decision tree.
4.4 Cost benefit analysis Based on the experiment, it suggests RD-LNN is able to detect three failures 2 minutes earlier among 25 paper breaks. In this section, we will analyze how much this proposed algorithm could make a contribution to the industries even though the performance does not look high enough to detect every failure before it occurs. Table 6 shows that even a small number of failure reduction improved by this algorithm can save a significant amount of cost for the industries every year. The gain is calculated based on the recall 12%, and the loss caused by the false alarm is estimated to find the total cost that we can save throughout a year. Ranjan et al 2 imply that it will cost more than 10 000 dollars for a break. We assumed that failure would occur 124 times for 1 month based on our dataset. Since the classification algorithm can detect 12% of failure, almost 1.7 million dollars can be saved per year by preventing 179 possible failures. However, we also need to consider the other side, a negative effect caused by a false alarm which gives the warning even though the machine is in the normal state. We assumed that this false alarm would cost 100 dollars because people might stop working and need to check the machine status to find out the problem. Based on the fact that data is captured by every 2 minutes, 1488, the number of failures that occurred every year, is subtracted from the total number of failures. The total loss caused by false alarm would be less than 1 million dollars due to the FPR which is 1.9%. If both positive and negative factors are considered together to find the total cost, we can conclude that the algorithm we propose here can save more than 700 thousand dollars in total for ayear. 5 DISCUSSION AND CONCLUSION It is crucial to detect the failure earlier to save cost and labor in a paper manufacturing facility. However, it is challeng- ing to detect machine failure in advance due to the fact that data is comprised of MSTS and failures which rarely occur during operation without any clear symptom where we call extremely rare event problems. In this research, two types of methods called CL-LNN, RD-LNN are proposed based on the nearest neighbor to extract proper features for early detection of paper manufacturing machinery. The data is preprocessed with several different steps: splitting data, stan- dardization, moving class label, second derivative, and time window processing. CL-LNN measures Euclidean distance to extract the class label of the nearest neighbor which will be fed into the decision tree classifier for the failure clas- sification. Another algorithm called RD-LNN extracts relative distance-generating numerical values which are suitable to be trained with SVM. Experiments are implemented on the dataset provided by the IISE 2019 data competition to show the competitiveness of our proposed methods. Dataset is preprocessed and proposed algorithms are implemented with other machine learning techniques. Through the experiment, it finds that RD-LNN is able to extract features effec- tively to detect the abnormal condition in the MSTS dataset which would make a considerable contribution to industries by saving cost.
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