Abstract
Many industries today are struggling with early the identification of quality issues, given the shortening of product design cycles and the desire to decrease production costs, coupled with the customer requirement for high uptime. The vehicle industry is no exception, as breakdowns often lead to on-road stops and delays in delivery missions. In this paper we consider quality issues to be an unexpected increase in failure rates of a particular component; those are particularly problematic for the original equipment manufacturers (OEMs) since they lead to unplanned costs and can significantly affect brand value. We propose a new approach towards the early detection of quality issues using machine learning (ML) to forecast the failures of a given component across the large population of units. In this study, we combine the usage information of vehicles with the records of their failures. The former is continuously collected, as the usage statistics are transmitted over telematics connections. The latter is based on invoice and warranty information collected in the workshops. We compare two different ML approaches: the first is an auto-regression model of the failure ratios for vehicles based on past information, while the second is the aggregation of individual vehicle failure predictions based on their individual usage. We present experimental evaluations on the real data captured from heavy-duty trucks demonstrating how these two formulations have complementary strengths and weaknesses; in particular, they can outperform each other given different volumes of the data. The classification approach surpasses the regressor model whenever enough data is available, i.e., once the vehicles are in-service for a longer time. On the other hand, the regression shows better predictive performance with a smaller amount of data, i.e., for vehicles that have been deployed recently.
Highlights
Heavy-duty vehicles are complex systems with a vast number of possible specifications, in which component breakdowns can originate from multiple sub-components that malfunction for different reasons
We present the two datasets, which were used to carry out the proposed forecasting method: Logged Vehicle Data (LVD), which basically includes usage and specification of the vehicles and is aggregated over time in a cumulative fashion; and Warranty Claim (WC) data, consisting of the claims’ information, as they are reported during the vehicles’ life time
This provides valuable knowledge, so that an original equipment manufacturers (OEMs) can react if there is an increase in the claim/failure ratio under the warranty period
Summary
Heavy-duty vehicles are complex systems with a vast number of possible specifications, in which component breakdowns can originate from multiple sub-components that malfunction for different reasons. In this day and age, such modern equipment logs large amounts of data using hundreds of sensors. This data can be potentially analyzed to provide early warnings about future quality issues. Some of these studies provide fault detection systems under the umbrella of statistical machine learning approaches, such as deep neural networks, recurrent neural networks, and support vector machines [5,9,10,11]
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