Abstract
An Air Pressure System (APS) is one of the crucial components of an automobile. Its failure leads to financial loses, and it may lead to loss of lives. Thus predicting such failure is a critical problem that requires a rigorous solution. Recently, many researchers have presented machine learning techniques to deal with APS failure detection. One of the major challenges in dealing with APS failure data is the presence of high class imbalance. Conventional classification criteria may not be able to efficiently handle such data. In this paper, a new machine learning method for APS failure detection is proposed. It is designed to specifically deal with the class imbalance. The method uses a linear decision boundary by maximizing Area Under the Curve (maxAUC) criterion. The proposed method was experimentally validated on an industrial dataset of APS failure. The results of the proposed method are thoroughly compared with existing linear as well as non-linear classifiers.
Highlights
As one of the crucial components of an automobile, Air Pressure System (APS) plays a vital role in gauging brakes, shifting gears, adjusting seats, and controlling suspensions
The node selection is done based on a weighted Gini index value of the feature. When it comes to dealing with imbalanced datasets, in general, machine learning techniques can be broadly classified into two categories: techniques dealing at the data level and techniques dealing at the classifier level by modifying the algorithms to suit the imbalanced scenario
The number of true positives identified by the proposed method is 345, which is high in comparison with 283 for Logistic Regression (LogR), and 261 for cSVM
Summary
As one of the crucial components of an automobile, Air Pressure System (APS) plays a vital role in gauging brakes, shifting gears, adjusting seats, and controlling suspensions. Its major components in an automobile are: air drier, circuit protection valves, and control unit. APS failures can lead to huge financial losses, and be life-threatening sometimes Their detection before they occur is an important area of research. With the advent of Industrial Internet of Things (IIoT) and Industry 4.0, machine-learning based methods for APS failure detection (e.g., [2]) is gaining popularity. One of the major challenges in APS failure detection using machine learning is the presence of high-imbalance class distribution. It is widely known that developing a machine-learning based system using imbalance data samples is not straightforward. In this paper a new machine-learning method for data classification is proposed that can handle the imbalanced data problem in APS failure detection.
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