Tri-Partition State Alphabet-Based Sequential Pattern for Multivariate Time Series

Zhang, Zhiheng; Min, Fan; Chen, Gong-Suo; Shen, Shao-Peng; Wen, Zuo-Cheng; Zhou, Xiangbing

doi:10.1007/s12559-021-09871-4

Cited by 71 publications

(41 citation statements)

References 53 publications

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“…Additionally, several research have demonstrated effective attempts to enhance the pattern from multivariate time series. For example, Zhang et al [ 64 ] demonstrated the use of a tri-partition state alphabet-based sequential pattern to generate a compact, understandable, and scalable pattern for multivariate time series. As a result, these findings will be beneficial for future research in order to improve the MDFVI ’s conciseness.…”

Section: Experimental Setup and Performance Analysismentioning

confidence: 99%

Bearing Fault Diagnosis Using Multidomain Fusion-Based Vibration Imaging and Multitask Learning

Hasan

Islam

2021

Sensors

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Statistical features extraction from bearing fault signals requires a substantial level of knowledge and domain expertise. Furthermore, existing feature extraction techniques are mostly confined to selective feature extraction methods namely, time-domain, frequency-domain, or time-frequency domain statistical parameters. Vibration signals of bearing fault are highly non-linear and non-stationary making it cumbersome to extract relevant information for existing methodologies. This process even became more complicated when the bearing operates at variable speeds and load conditions. To address these challenges, this study develops an autonomous diagnostic system that combines signal-to-image transformation techniques for multi-domain information with convolutional neural network (CNN)-aided multitask learning (MTL). To address variable operating conditions, a composite color image is created by fusing information from multi-domains, such as the raw time-domain signal, the spectrum of the time-domain signal, and the envelope spectrum of the time-frequency analysis. This 2-D composite image, named multi-domain fusion-based vibration imaging (MDFVI), is highly effective in generating a unique pattern even with variable speeds and loads. Following that, these MDFVI images are fed to the proposed MTL-based CNN architecture to identify faults in variable speed and health conditions concurrently. The proposed method is tested on two benchmark datasets from the bearing experiment. The experimental results suggested that the proposed method outperformed state-of-the-arts in both datasets.

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Section: Experimental Setup and Performance Analysismentioning

confidence: 99%

Bearing Fault Diagnosis Using Multidomain Fusion-Based Vibration Imaging and Multitask Learning

Hasan

Islam

2021

Sensors

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“…Therefore, the CACO algorithm takes on a global optimization ability. In addition, some new optimization algorithms have been proposed in recent years [41][42][43][44][45][46][47][48][49][50][51][52][53][54][55].…”

Section: Chaotic Ant Colony Optimization Algorithmmentioning

confidence: 99%

Fault Diagnosis Using Cascaded Adaptive Second-Order Tristable Stochastic Resonance and Empirical Mode Decomposition

Cui

Guan

2021

Applied Sciences

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Aiming at the problems of poor decomposition quality and the extraction effect of a weak signal with strong noise by empirical mode decomposition (EMD), a novel fault diagnosis method based on cascaded adaptive second-order tristable stochastic resonance (CASTSR) and EMD is proposed in this paper. In the proposed method, low-frequency interference components are filtered by using high-pass filtering, and the restriction conditions of stochastic resonance theory are solved by using an ordinary variable-scale method. Then, a chaotic ant colony optimization algorithm with a global optimization ability is employed to adaptively adjust the parameters of the second-order tristable stochastic resonance system to obtain the optimal stochastic resonance, and noise reduction pretreatment technology based on CASTSR is developed to enhance the weak signal characteristics of low frequency. Next, the EMD is employed to decompose the denoising signal and extract the characteristic frequency from the intrinsic mode function (IMF), so as to realize the fault diagnosis of rolling bearings. Finally, the numerical simulation signal and actual bearing fault data are selected to prove the validity of the proposed method. The experiment results indicate that the proposed fault diagnosis method can enhance the decomposition quality of the EMD, effectively extract features of weak signals, and improve the accuracy of fault diagnosis. Therefore, the proposed fault diagnosis method is an effective fault diagnosis method for rotating machinery.

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“…Type of State Metric Combinations (r, c) I State (5, 1), (7,1), (4, 1), (7,8), (3,7), (9,1), (1,8), (5,8), (1,7), (0, 8) Tri-state (9,1), (9,8), (3,8), (5,1), (7,8), (3, 0), (7,1), (4,1), (4,7), (3, 1) IFS-tri-state (3,8), (9,1), (9,8), (5,1), (3, 0), (7,8), (8,8), (7,1), (4, 1), (4, 7) RFS-tri-state (3,8), (9, 1), (9, 8), (5, 1), (3, 0), (7,8), (8,8), (7, 1), (4, 1), (4, 7) II State (1,…”

Section: Datasetmentioning

confidence: 99%

“…Time-series analysis [1] has long been a subject that has attracted researchers from a diverse range of fields, including pattern discovery [2][3][4][5], clustering [6][7][8], classification [9,10], prediction [11], causality [12], and anomaly detection [13]. Time-series prediction is one of the most sought-after yet, arguably, the most challenging tasks [11].…”

Section: Introductionmentioning

confidence: 99%

Tri-Partition Alphabet-Based State Prediction for Multivariate Time-Series

et al. 2021

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Recently, predicting multivariate time-series (MTS) has attracted much attention to obtain richer semantics with similar or better performances. In this paper, we propose a tri-partition alphabet-based state (tri-state) prediction method for symbolic MTSs. First, for each variable, the set of all symbols, i.e., alphabets, is divided into strong, medium, and weak using two user-specified thresholds. With the tri-partitioned alphabet, the tri-state takes the form of a matrix. One order contains the whole variables. The other is a feature vector that includes the most likely occurring strong, medium, and weak symbols. Second, a tri-partition strategy based on the deviation degree is proposed. We introduce the piecewise and symbolic aggregate approximation techniques to polymerize and discretize the original MTS. This way, the symbol is stronger and has a bigger deviation. Moreover, most popular numerical or symbolic similarity or distance metrics can be combined. Third, we propose an along–across similarity model to obtain the k-nearest matrix neighbors. This model considers the associations among the time stamps and variables simultaneously. Fourth, we design two post-filling strategies to obtain a completed tri-state. The experimental results from the four-domain datasets show that (1) the tri-state has greater recall but lower precision; (2) the two post-filling strategies can slightly improve the recall; and (3) the along–across similarity model composed by the Triangle and Jaccard metrics are first recommended for new datasets.

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Tri-Partition State Alphabet-Based Sequential Pattern for Multivariate Time Series

Cited by 71 publications

References 53 publications

Bearing Fault Diagnosis Using Multidomain Fusion-Based Vibration Imaging and Multitask Learning

Bearing Fault Diagnosis Using Multidomain Fusion-Based Vibration Imaging and Multitask Learning

Fault Diagnosis Using Cascaded Adaptive Second-Order Tristable Stochastic Resonance and Empirical Mode Decomposition

Tri-Partition Alphabet-Based State Prediction for Multivariate Time-Series

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