Topic
Condition monitoring
About: Condition monitoring is a research topic. Over the lifetime, 13911 publications have been published within this topic receiving 201649 citations.
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TL;DR: In this article, a diagnostic tool based on the wavelet transform is presented, able to detect and to quantify the wheel-flat defect of a test train at different speeds and to measure the train speed with proper accuracy.
86 citations
TL;DR: In this article, a spectral kurtosis-based approach is proposed for selecting the best demodulation band to extract bearing fault-related impulsive content from vibration signals contaminated with strong EMI.
86 citations
TL;DR: A novel way for condition monitoring of planetary gearboxes based on multivariate statistics is suggested and the emphasis is put on the algebraic and geometric interpretations of the PCA.
86 citations
TL;DR: A modified form of the correlation integral developed by Grassberger and Procaccia referred to as the partial correlation integral, which can be computed in real time is introduced, which is used to analyze machine vibration data obtained throughout a life test of a rolling element bearing.
86 citations
TL;DR: The proposed incorrect data detection method based on an improved local outlier factor (LOF) is proposed for data cleaning and is able to detect both missing segments and abnormal segments, effectively, and thus is helpful for big data cleaning of machinery condition monitoring.
Abstract: The presence of incorrect data leads to the decrease of condition-monitoring big data quality. As a result, unreliable or misleading results are probably obtained by analyzing these poor-quality data. In this paper, to improve the data quality, an incorrect data detection method based on an improved local outlier factor (LOF) is proposed for data cleaning. First, a sliding window technique is used to divide data into different segments. These segments are considered as different objects and their attributes consist of time-domain statistical features extracted from each segment, such as mean, maximum and peak-to-peak value. Second, a kernel-based LOF (KLOF) is calculated using these attributes to evaluate the degree of each segment being incorrect data. Third, according to these KLOF values and a threshold value, incorrect data are detected. Finally, a simulation of vibration data generated by a defective rolling element bearing and three real cases concerning a fixed-axle gearbox, a wind turbine, and a planetary gearbox are used to verify the effectiveness of the proposed method, respectively. The results demonstrate that the proposed method is able to detect both missing segments and abnormal segments, which are two typical incorrect data, effectively, and thus is helpful for big data cleaning of machinery condition monitoring.
86 citations