Failure Prediction with Construction Machinery Oil Analysis Results

Abstract

With the advancement of technology and widespread use of the internet, the concept of big data, which we often hear about, has emerged. Transforming big data, briefly defined as a stack of unstructured data, into meaningful information and revealing hidden patterns can be achieved by different methods. The use of this group of methods and algorithms, called data mining, with artificial intelligence and statistics, enables more understandable, meaningful and effective decisions to be made. This helps to optimize profits by reducing costs and increasing performance.
In this study; By Borusan Cat, Caterpillar Inc. Using oil analysis data obtained from construction machines, a model has been developed that will enable early malfunction detection of machines and identification of vehicle maintenance needs. With the developed system, the oil analysis data of the vehicles are used to predict the number of working hours in which the vehicle will break down, using machine learning methods, in advance. Then, forecast data is integrated into decision mechanisms in business processes, and finally, the information obtained is reported using data visualization technologies and made traceable through summary data.
The system developed in this personalized product-focused study, which is the subject of Industry 4.0, can be easily adapted to the operation of different machines. In this way, it will be easier to track and control the vehicles, and it will be possible to detect any malfunctions that may occur in the vehicles without stopping the flow of the process. By contributing to the extension of the life of the machines used with the proposed approach, eliminating the costs of purchasing a new machine, which can be quite costly, or purchasing repairs and spare parts due to possible damage, will provide significant returns to companies in terms of cost and time. Another important contribution of the system will undoubtedly be environmental sustainability.