Early Prediction of Quality Issues in Automotive Modern Industry

Abstract

<jats:p>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.</jats:p>

A Snapshot-Stacked Ensemble and Optimization Approach for Vehicle Breakdown Prediction

“…We built a forecasting system in which a supervised ML algorithm examined the contribution of the parameters to the claims. Considering results from our previous study [ 18 ], in which vehicle usage changed over time, we segmented the data into different parts. This segmentation process was done by assuming that the distribution of the vehicle usage logged in the same season over the years was relatively similar with respect to the other seasons.…”

“…We combined two data sets based on the vehicle’s “Chassis id”, “Date of readout, which is logged”, and “Date of failure report”. To merge these two datasets, we borrowed the method that we used in our previous study [ 18 ], where we took advantage of a Volvo expert’s knowledge to determine a time window of one month as an interval where the symptoms of failures are most likely to be visible. Therefore, the prediction horizon was set to 30 days for the developed model.…”

“…Earlier fault estimation was done through manual inspection and statistical approaches. Applying artificial intelligence techniques on sensor data automates fault detection, requiring less effort and achieving more accuracy in a timely fashion [ 18 ].…”

A Review on Machine Learning Approaches for Network Malicious Behavior Detection in Emerging Technologies

“…As a result, existing studies in the area of NIDSs, show that it is still very hard to develop a new NIDS to ensure three important properties such as robustness, scalability and high performance in emerging technologies, to effectively and efficiently prevent any types of malicious activities. In addition, experimental works show that locating the place of NIDS and the best configuration for deployment within shared environments in new computing technologies such as Cloud, edge, Fog computing and IoT, with various stakeholders is also a very challenging task [ 132 , 138 ]. Some of the important issues with respect to different phases of NADSs are sorted out as main challenges in the following.…”

CRFormer: Complementary Reliability Perspective Transformer for Automotive Components Reliability Prediction Based on Claim Data

“…By predicting future failures or claims of automobiles based on historical claim sequences, researchers can estimate automobile reliability. However, the limited information in claim sequences, especially when targeting early prediction, makes it challenging to achieve accurate automobile reliability prediction results [8].…”