首页|Monitoring bivariate autocorrelated process using a deep learning-based control chart: A case study on the car manufacturing industry
Monitoring bivariate autocorrelated process using a deep learning-based control chart: A case study on the car manufacturing industry
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
Elsevier
Due to the high frequency of sensor data collection in modern industries, consecutive observations are potentially autocorrelated. Failing to address this issue adequately when designing control charts can lead to numerous false alarms, thereby compromising the efficiency of the monitoring technique. On the other hand, thanks to the evolution of measurement equipment, nowadays more than one quality characteristics are often measured simultaneously. The Hotelling T2 chart is the most used approach to detect abnormal (or OOC) conditions of processes with multiple quality characteristics. The OOC condition could be due to factors such as shifts in process mean, variance-covariance matrix, outliers, or trends. Conventional statistical control charts have weaknesses in the detection of complex OOC scenarios, while the occurrence of such scenarios is particularly prevalent in today's modern industries. This paper presents a novel approach for developing control charts using machine learning techniques, specifically LSTM, to monitor bivariate autocorrelated processes. Utilizing a deep memory information structure and novel input features can efficiently capture the complex interdependencies and temporal patterns in autocorrelated processes, leading to suboptimal performance detecting process deviations. The proposed methodology is evaluated using Monte Carlo simulations, demonstrating improved performance compared to traditional control charts. A new practical usage is discovered for monitoring wheel alignment in a car manufacturing assembly using the 'x-wheel' device data, specifically designed to examine essential wheel angles. This real-life example demonstrates the feasibility of implementing the proposed method on production lines to oversee a correlated process with two variables.
Ali Yeganeh、Sandile Charles Shongwe、Adel Ahmadi Nadi、Mohsen Mehrab Ghuchani
展开 >
Department of Mathematical Statistics and Actuarial Science, Faculty of Natural and Agricultural Sciences, University of the Free State, Bloemfontein 9301, South Africa
Department of Statistics and Actuarial Science, Faculty of Mathematics, University of Waterloo, Waterloo, Canada
Department of Industrial Engineering Faculty of Engineering, Ferdowsi University of Mashhad, P.O. Box 91775-1111, Mashhad, Iran
NETL
NSTL
Elsevier
