Detection and recovery of anomalous vibration signal of rotating machinery based on LOF-MSAMP

Abstract The collected vibration signals of rotating machinery contain pulses, missing, and other low-quality anomalous data due to environmental noise interference, unstable data transmission, and data acquisition instrument failure. These low-quality data obstruct the analysis of the healthy operation condition of rotating machinery. This paper proposes a method for anomalous vibration signal detection and recovery based on the local outlier factor algorithm and the modified sparsity adaptive matching pursuit algorithm. The method combines the local outlier factor algorithm and compressive sensing theory to realize anomalous vibration signal detection and recovery. This paper evaluates the recovery performance both qualitatively and quantitatively and discusses how the proposed method’s hyperparameter selection affects the recovery results. A set of simulated signal and measured hob base signal are used to verify the proposed method. The results indicate that, when compared to the other seven reconstruction algorithms, the proposed method’s recovered signal has the lower error level and the higher waveform similarity which reaches more than 98% to the original signal, effectively improving data quality.