Multichannel-Based Multiview Shallow Fusion for Time Series Classification and Its Application in Fault Diagnosis

Abstract

In the current time series classification (TSC) field, shallow concatenation, deep fusion, and hybrid ensemble multichannel frameworks (MCF) represented by convolution-based, deep learning, and hybrid methods have achieved competitive TSC performance. However, the massive kernels, deep fusion, and heterogeneous ensemble mechanisms, which are the core of the three frameworks, respectively, lead to overfitting risks. Therefore, in this article, a novel convolution-based TSC algorithm multichannel-based multiview shallow fusion (MC-MSF) within a new shallow fusion ensemble-based MCF is proposed. MC-MSF enhances feature diversity, quality, and classifier diversity while suppressing the overfitting risks via three shallow components. For feature diversity, the original series is mapped to the connected multichannel series spaces, and then diverse pooling features are extracted via a single-layer convolution with fewer kernels. For feature quality, the power of proportion of positive values (PPPV) features with adaptive powers are extracted based on alternating gradient descent, and the multiview shallow feature fusion is implemented to generate fused features. For classifier diversity, diverse linear classifiers are trained on the combined multiview feature vectors to ensemble homogeneously. The state-of-the-art TSC accuracy is achieved by MC-MSF via the sequential operation of three effective shallow components, as verified by comparative experiments on the public UCR and real excavator fault diagnosis application datasets.