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An anomaly detection method for mechanical monitoring data based on kernel estimation LOF

A technology of monitoring data and detection methods, which is applied in computer parts, calculation, pattern recognition in signals, etc.

Active Publication Date: 2019-01-15
XI AN JIAOTONG UNIV
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Problems solved by technology

At present, there is no published literature on abnormal section detection of mechanical monitoring data

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  • An anomaly detection method for mechanical monitoring data based on kernel estimation LOF
  • An anomaly detection method for mechanical monitoring data based on kernel estimation LOF
  • An anomaly detection method for mechanical monitoring data based on kernel estimation LOF

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Embodiment Construction

[0033] The present invention will be described in further detail below in conjunction with the accompanying drawings.

[0034] Such as figure 1 As shown, a method for abnormal segment detection of mechanical monitoring data based on kernel estimation LOF includes the following steps:

[0035] 1) Use length w 1 The sliding time window of the mechanical monitoring data is divided into N data segments, and the length increment Δw of the sliding time window is defined, so that the overlapping rate of two adjacent windows is 90%, and a set D of data segments is obtained;

[0036] 2) Extract the feature index of each data segment from the time domain, frequency domain, and time-frequency domain respectively to form a feature index set in Indicates that the length of the sliding window is w 1 When is the characteristic index vector of the i-th data segment, the characteristic index includes mean value, maximum value, minimum value, peak-to-peak value, variance, kurtosis, root m...

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Abstract

An anomaly detection method for mechanical monitoring data based on kernel estimation LOF is disclosed. Firstly, a mechanical monitoring signal is divided into a plurality of data segments by using asliding window with a fixed length. Extracting feature index vectors of each data segment; setting the initial value of parameter k in LOF algorithm and calculating the local anomaly factor value of each data segment, according to Nadaraya-Watson kernel estimation method, we can get the estimation value sequence of local anomaly factor; then the k-value is iterated and the threshold value S is setto realize the adaptive selection of k-value. The length of the sliding window is iterated. When the length of the sliding window is iterated to the preset value, the sequence containing the maximumlocal anomaly factor estimation value is selected. Finally, the threshold value T is calculated, the abnormal segment is screened out, and the abnormal segment detection is completed. The invention improves the traditional LOF algorithm based on the kernel estimation, realizes the adaptive selection of the parameter k and the length of the abnormal section, improves the ability of the algorithm todetect the abnormal section of the mechanical monitoring data, and has an ideal effect on the quality improvement of the mechanical monitoring data.

Description

technical field [0001] The invention belongs to the technical field of improving the quality of mechanical monitoring data, and in particular relates to a method for detecting abnormal segments of mechanical monitoring data based on kernel estimation LOF. Background technique [0002] In the field of mechanical fault diagnosis, due to the wide distribution of mechanical equipment, numerous measuring points, high data sampling frequency, and long service life, a large amount of monitoring data has been accumulated. By analyzing and processing massive amounts of monitoring data, various information about equipment operation can be mined to discover equipment failures in advance. However, the working environment of mechanical equipment is often very harsh, resulting in a large number of drifting, distorted, and incomplete dirty data mixed in the monitoring data. operation and maintenance strategy. Abnormal segment data is a kind of dirty data, which has nothing to do with the...

Claims

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Application Information

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IPC IPC(8): G06K9/00
CPCG06F2218/10G06F2218/20
Inventor 雷亚国李则达许学方周昕李乃鹏
Owner XI AN JIAOTONG UNIV
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