Quality Monitoring and Prognostic of Electronics Using Multidimensional Time Series Method

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.190-191.1029 ◽

2012 ◽

Vol 190-191 ◽

pp. 1029-1032

Author(s):

Bo Wan ◽

Li Wang ◽

Gui Cui Fu

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Quality Monitoring ◽

One Dimensional ◽

Series Analysis ◽

Degradation Data ◽

Multidimensional Time Series ◽

Time Series Method

This paper presents a quality monitoring and prognostic method to evaluating quality of electronics through monitoring degradation path. Electronics multiple performance parameter degradation data are treated as multidimensional time series and described using multidimensional time series model to take into account implements of stochastic nature of environmental variables and to predict long-term trend of performance degradation. A degradation test is processed for certain electronics and three kinds of performance parameters degradation data are monitored for prognostics. A comparison between the predicted degradation path using multidimensional time series analysis, the predicted degradation path using one-dimensional time series analysis and the real degradation path of the electronics is processed and the results show that the degradation path prediction using the suggested method is more effective than one-dimensional time series analysis.

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Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series

Electronics ◽

10.3390/electronics10101151 ◽

2021 ◽

Vol 10 (10) ◽

pp. 1151

Author(s):

Carolina Gijón ◽

Matías Toril ◽

Salvador Luna-Ramírez ◽

María Luisa Marí-Altozano ◽

José María Ruiz-Avilés

Keyword(s):

Time Series ◽

Deep Learning ◽

Time Series Analysis ◽

Data Traffic ◽

Medium Term ◽

Short Term ◽

Traffic Forecasting ◽

Series Analysis ◽

Radio Planning

Network dimensioning is a critical task in current mobile networks, as any failure in this process leads to degraded user experience or unnecessary upgrades of network resources. For this purpose, radio planning tools often predict monthly busy-hour data traffic to detect capacity bottlenecks in advance. Supervised Learning (SL) arises as a promising solution to improve predictions obtained with legacy approaches. Previous works have shown that deep learning outperforms classical time series analysis when predicting data traffic in cellular networks in the short term (seconds/minutes) and medium term (hours/days) from long historical data series. However, long-term forecasting (several months horizon) performed in radio planning tools relies on short and noisy time series, thus requiring a separate analysis. In this work, we present the first study comparing SL and time series analysis approaches to predict monthly busy-hour data traffic on a cell basis in a live LTE network. To this end, an extensive dataset is collected, comprising data traffic per cell for a whole country during 30 months. The considered methods include Random Forest, different Neural Networks, Support Vector Regression, Seasonal Auto Regressive Integrated Moving Average and Additive Holt–Winters. Results show that SL models outperform time series approaches, while reducing data storage capacity requirements. More importantly, unlike in short-term and medium-term traffic forecasting, non-deep SL approaches are competitive with deep learning while being more computationally efficient.

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