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

Peng, Cheng; Tang, Zhaohui; Gui, Weihua; Chen, Qing; He, Jing

doi:10.3934/jimo.2019107

Cited by 5 publications

(4 citation statements)

References 33 publications

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“…After performing the above operations and obtaining the spatial–temporal similarity and sensor parameter error, firstly the sensor parameter error is converted into sensor parameter similarity. To combine the similarities of different parts [ 19 , 20 ], a weighted training model is used to allocate weights to these two similarities. In order to compare the similarity of each matching cycle, a similarity formula is defined.…”

Section: Definition Of Termsmentioning

confidence: 99%

Turbofan Engine Health Assessment Based on Spatial–Temporal Similarity Calculation

Peng,

Hu,

Tang

2023

Sensors

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Aiming at the problem of the remaining useful life prediction accuracy being too low due to the complex operating conditions of the aviation turbofan engine data set and the original noise of the sensor, a residual useful life prediction method based on spatial–temporal similarity calculation is proposed. The first stage is adaptive sequence matching, which uses the constructed spatial–temporal trajectory sequence to match the sequence to find the optimal matching sample and calculate the similarity between the two spatial–temporal trajectory sequences. In the second stage, the weights of each part are assigned by the two weight allocation algorithms of the weight training module, and then the final similarity is calculated by the similarity calculation formula of the life prediction module, and the final predicted remaining useful life is determined according to the size of the similarity and the corresponding remaining life. Compared with a single model, the proposed method emphasizes the consistency of the test set and the training set, increases the similarity between samples by sequence matching with other spatial–temporal trajectories, and further calculates the final similarity and predicts the remaining use through the weight allocation module and the life prediction module. The experimental results show that compared with other methods, the root mean square error (RMSE) index and the remaining useful life health score (Score) index are reduced by 12.6% and 14.8%, respectively, on the FD004 dataset, and the RMSE index is similar to that in other datasets; the Score index is reduced by about 10%, which improves the prediction accuracy of the remaining useful life and can provide favorable support for the operation and maintenance decision of turbofan engines.

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Section: Definition Of Termsmentioning

confidence: 99%

Turbofan Engine Health Assessment Based on Spatial–Temporal Similarity Calculation

Peng,

Hu,

Tang

2023

Sensors

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“…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.…”

Section: ) Feature Selectionmentioning

confidence: 99%

Review of Key Technologies and Progress in Industrial Equipment Health Management

et al. 2020

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Industrial equipment health management is an extension of traditional fault diagnosis, which is a frontier research field of information science. The definition and application area of industrial equipment health management are first clarified, and then technologies related to health management are introduced, including feature analysis, fault diagnosis, fault prognosis and other technologies. The difficulties and bottlenecks of various technologies are expounded through comparative analysis, and a feasible scheme for future health management and integration is also discussed in this paper.

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“…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).…”

Section: Introductionmentioning

confidence: 99%

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

Cheng

Chen

et al. 2021

Sensors

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The prognosis of the remaining useful life (RUL) of turbofan engine provides an important basis for predictive maintenance and remanufacturing, and plays a major role in reducing failure rate and maintenance costs. The main problem of traditional methods based on the single neural network of shallow machine learning is the RUL prognosis based on single feature extraction, and the prediction accuracy is generally not high, a method for predicting RUL based on the combination of one-dimensional convolutional neural networks with full convolutional layer (1-FCLCNN) and long short-term memory (LSTM) is proposed. In this method, LSTM and 1- FCLCNN are adopted to extract temporal and spatial features of FD001 andFD003 datasets generated by turbofan engine respectively. The fusion of these two kinds of features is for the input of the next convolutional neural networks (CNN) to obtain the target RUL. Compared with the currently popular RUL prediction models, the results show that the model proposed has higher prediction accuracy than other models in RUL prediction. The final evaluation index also shows the effectiveness and superiority of the model.

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

Cited by 5 publications

References 33 publications

Turbofan Engine Health Assessment Based on Spatial–Temporal Similarity Calculation

Turbofan Engine Health Assessment Based on Spatial–Temporal Similarity Calculation

Review of Key Technologies and Progress in Industrial Equipment Health Management

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

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