Abstract
Classical reliability assessment methods have predominantly focused on probability and statistical theories, which are insufficient in assessing the operational reliability of individual mechanical equipment with time-varying characteristics. A new approach to assess machinery operational reliability with normalized lifting wavelet entropy from condition monitoring information is proposed, which is different from classical reliability assessment methods depending on probability and statistics analysis. The machinery vibration signals with time-varying operational characteristics are firstly decomposed and reconstructed by means of a lifting wavelet package transform. The relative energy of every reconstructed signal is computed as an energy percentage of the reconstructed signal in the whole signal energy. Moreover, a normalized lifting wavelet entropy is defined by the relative energy to reveal the machinery operational uncertainty. Finally, operational reliability degree is defined by the quantitative value obtained by the normalized lifting wavelet entropy belonging to the range of [0, 1]. The proposed method is applied in the operational reliability assessment of the gearbox in an oxy-generator compressor to validate the effectiveness.
Highlights
Reliability is a worldwide popular expression that has been celebrated for years as a commendable attribute of a person or a thing
Since wavelets meet the demands of transient signal analysis and entropy is associated with the measurements of information uncertainty, it is useful to evaluate the operational reliability of mechanical equipment with the proposed normalized lifting wavelet entropy from condition monitoring information
A new machinery operational reliability assessment approach using the normalized lifting wavelet entropy from condition monitoring information is proposed, which is realized by analyzing condition monitoring information of mechanical equipment
Summary
Reliability is a worldwide popular expression that has been celebrated for years as a commendable attribute of a person or a thing. Since wavelets meet the demands of transient signal analysis and entropy is associated with the measurements of information uncertainty, it is useful to evaluate the operational reliability of mechanical equipment with wavelet entropy from condition monitoring information. Huang et al proposed an enhanced feature extraction model for machinery performance assessment, which is based on the lifting-based wavelet packet transform and sampling-importance-resampling methods [25]. Liao et al proposed a hybrid fault-feature extraction method by detecting localized defects and analyzing vibration signals of rolling element bearings via a customized multi-wavelet packet transform, which matches the diverse characteristics of hybrid fault and attains effective results [27]. A novel operational reliability assessment method based on normalized lifting wavelet entropy is proposed.
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