Dual Features Functional Support Vector Machines for Fault Detection of Rechargeable Batteries

Abstract

The early detection of faulty batteries is a critical work in the manufacturing processes of a secondary rechargeable battery. Conventional approaches use original performance degradation profiles of remaining capacity after recharge in order to detect faulty batteries. However, original degradation profiles with right-truncated test duration may not be effective in detecting faulty batteries. In this correspondence, we propose dual features functional support vector machine approach that uses both first and second derivatives of degradation profiles for early detection of faulty batteries with the reduced error rate. The modified floating search algorithm for the repeated feature selection with newly added degradation path points is presented to find a few good features for the enhanced detection while reducing the computation time for online implementation. After that, an attribute sampling plan considering time-varying classification errors is presented to determine the optimal number of test cycles and sample sizes by minimizing our proposed cost function. The real-life case study is presented to illustrate the proposed methodology and show its improved performance compared to existing approaches. The proposed method can be applied in a wide range of manufacturing processes to assess time-dependent quality characteristics.