Discrete convolution wavelet transform of signal and its application on BEV accident data analysis

•A signal can be decomposed and reconstructed by group filters by DCWT in time domain.•The decomposition filters of DCWT can be chosen freely in time domain.•The reconstruction filters group can be obtained by RC equation in frequency domain.•By correlation analysis, optimized filters are solved to get main features of signal.•Decomposition and reconstruction based on convolution method is easier to implement.•The computation cost of FMPA is very low. Thus, FMPA can be used in large-scale data. This paper introduces a new kind of signal decomposition and reconstruction method called Discrete Convolution Wavelet Transform (DCWT), and it is used to analyze the accident pattern data of battery electric vehicles (BEV). Normally, some feature signals directly related to accidents can be obtained from the BEV daily monitoring system, but how can we match pursuit those similar feature signals when BEV is running? The DCWT method is proposed from the Frequency Slice Wavelet Transform (FSWT) defined in frequency-domain, but DCWT is defined in time-domain by convolution filters. Though the original signal can be easily decomposed and reconstructed by FSWT, it is difficult to use in large-scale and real-time computation. At first, a simple signal Decomposition & Reconstruction Technical Framework (DRTF) is presented. In order to reconstruct the original signal completely, it is important to discuss the reconstruction condition (RC) of DCWT and the filter selection methods. By means of the correlation analysis, an filter optimization algorithm is designed to obtain the main features of pattern signals. Finally, a feature matching pursuit algorithm based on DCWT is proposed to find the accident feature in a real BEV accident data. Summarily, this paper presents a new convolution wavelet transform method, in which the original signal can be decomposed and reconstructed by two groups of filters. The decomposition filters can be designed as need and the reconstruction filters can also be obtained by RC equation, and both of them can be easily optimized in practice. By comparing analysis, DCWT method can fast decompose signal to obtain its feature signals. Some conclusions are drawn that the DCWT method is practical and will become a new idea of signal decomposing and signal identifying.