A bidirectional weighted boundary distance algorithm for time series similarity computation based on optimized sliding window size

Abstract

The existing method of determining the size of the time series sliding window by empirical value exists some problems which should be solved urgently, such as when considering a large amount of information and high density of the original measurement data collected from industry equipment, the important information of the data cannot be maximally retained, and the calculation complexity is high. Therefore, by studying the effect of sliding window on time series similarity technology in practical application, an algorithm to determine the initial size of the sliding window is proposed. The upper and lower boundary curves with a higher fitting degree are constructed, and the trend weighting is introduced into the LB Hust distance calculation method to reduce the difficulty of mathematical modeling and improve the efficiency of data similarity computation.

Review of Key Technologies and Progress in Industrial Equipment Health Management

“…At present, the commonly used selection methods are based on single criterion and multiple criteria. The single criterion mainly includes Fisher discrimination, information gain, kernel density estimation, the distance between classes, and manifold learning [18]. However, the single criterion method ignores the influence of other related factors in feature selection and has limitations.…”

A Remaining Useful Life Prognosis of Turbofan Engine Using Temporal and Spatial Feature Fusion

“…Among them, the hybrid model composed of CNN [ 8 , 9 , 10 ] and LSTM is the most common one in the field of RUL prediction of turbofan engine. CNN has a strong feature extraction ability, which cannot only extract local abstract features, but also process the data with multiple working conditions and multiple faults [ 11 , 12 , 13 ], especially the one-dimensional CNN can be well applied to the time series analysis generated by sensors (such as gyroscope or accelerometer data [ 14 , 15 , 16 ]). It can also be used to analyze signal with fixed length period (such as audio signal).…”

A Fault Diagnosis Method for Rolling Bearings Based on Parameter Transfer Learning under Imbalance Data Sets

“…Although transfer learning has achieved promising results in fault diagnosis of machinery, the methods commonly have the following shortcomings: first, most of these them still need a certain amount of labeled data, for example, reference [10] and [11] require more than 10 target training samples to achieve effective recognition accuracy; Second, we need to do a lot of preprocessing work, such as to extract features [12] from spectrum data rather than the original vibration data; and finally, these methods only transfer the simulation experiment data set to another simulation experiment data set [13], and the speed, loading, and fault degree of these data sets changed slightly, so the generalization ability of these methods are limited. To deal with the above-mentioned limitations, a new deep transfer learning network, named the transferred discriminator network (TD), is proposed for fault diagnose of rolling bearings in this paper.…”