Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-05-20 Cooperative journals: 《计算机应用研究》
Abstract: According to current algorithms for rotating machines largely depending on expert prior knowledge, the paper proposed an adaptive fault recognition algorithm based on shift invariant dictionary learning and sparse coding. Firstly, it segmented and smoothed vibration signals to decrease the complexity. Then, it used shift invariant dictionary learning with adaptive penalty factor to learn shift invariant bases in different fault states. After that, it used an efficient sparse coefficient solver called Feature Sign Search for reconstructing signal to be recognized. Lastly, residual was an evidence to determining fault state the signal belonging to. In the experiments of rolling bearing datasets and vibration signals of real aero-engine demonstrate its higher accuracy than up-to-date algorithms and feasibility for practical applications.