Error Fusion of Hybrid Neural Networks for Mechanical Condition Dynamic Prediction

Zhang, Wentao; Liu, Yucheng; Zhang, Shaohui; Long, Tuzhi; Liang, Jinglun

doi:10.3390/s21124043

Cited by 5 publications

(4 citation statements)

References 30 publications

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“…These four kinds of measurement indexes with their formulas are shown in (35)–(38).

represents the number of samples,

represents the observed value, and

represents the predicted value [ 38 ].…”

Section: Simulation Results and Analysismentioning

confidence: 99%

Adaptive Data Fusion Method of Multisensors Based on LSTM-GWFA Hybrid Model for Tracking Dynamic Targets

Yin

Wang

et al. 2022

Sensors

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In preparation for the battlefields of the future, using unmanned aerial vehicles (UAV) loaded with multisensors to track dynamic targets has become the research focus in recent years. According to the air combat tracking scenarios and traditional multisensor weighted fusion algorithms, this paper contains designs of a new data fusion method using a global Kalman filter and LSTM prediction measurement variance, which uses an adaptive truncation mechanism to determine the optimal weights. The method considers the temporal correlation of the measured data and introduces a detection mechanism for maneuvering of targets. Numerical simulation results show the accuracy of the algorithm can be improved about 66% by training 871 flight data. Based on a mature refitted civil wing UAV platform, the field experiments verified the data fusion method for tracking dynamic target is effective, stable, and has generalization ability.

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“…These four kinds of measurement indexes with their formulas are shown in (35)–(38).

represents the number of samples,

represents the observed value, and

represents the predicted value [ 38 ].…”

Section: Simulation Results and Analysismentioning

confidence: 99%

Adaptive Data Fusion Method of Multisensors Based on LSTM-GWFA Hybrid Model for Tracking Dynamic Targets

Yin

Wang

et al. 2022

Sensors

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show abstract

“…According to the relevant literature [21,22] , we install two inertial sensors and an EEG sensor on the thighs and head of each subject respectively after comprehensive consideration, which can realistically simulate the real scene of people putting their smartphones in their trouser pockets. Each inertial sensor (WIT Inc., CHN) has built-in ICM42605 (3-axis accelerometer and 3-axis gyroscope) and MMC3630 (3-axis magnetometer), which have the characteristics of small size, wearable, and low power consumption [23,24] . The specific parameters of inertial sensor are shown in [25] used the VR environment and a pad-mounted pressure sensor to analyze the relationship between gait and emotional state.…”

Section: Data Collectionmentioning

confidence: 99%

Attention-Based CNN Fusion Model for Emotion Recognition During Walking Using Discrete Wavelet Transform on EEG and Inertial Signals

Zhao,

Guo,

Chen

et al. 2024

Big Data Min. Anal.

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Walking as a unique biometric tool conveys important information for emotion recognition. Individuals in different emotional states exhibit distinct walking patterns. For this purpose, this paper proposes a novel approach to recognizing emotion during walking using electroencephalogram (EEG) and inertial signals.Accurate recognition of emotion is achieved by training in an end-to-end deep learning fashion and taking into account multi-modal fusion. Subjects wear virtual reality head-mounted display (VR-HMD) equipment to immerse in strong emotions during walking. VR environment shows excellent imitation and experience ability, which plays an important role in awakening and changing emotions. In addition, the multi-modal signals acquired from EEG and inertial sensors are separately represented as virtual emotion images by discrete wavelet transform (DWT). These serve as input to the attention-based convolutional neural network (CNN) fusion model. The designed network structure is simple and lightweight while integrating the channel attention mechanism to extract and enhance features. To effectively improve the performance of the recognition system, the proposed decision fusion algorithm combines Critic method and majority voting strategy to determine the weight values that affect the final decision results. An investigation is made on the effect of diverse mother wavelet types and wavelet decomposition levels on model performance which indicates that the 2.2-order reverse biorthogonal (rbio2.2) wavelet with two-level decomposition has the best recognition performance.Comparative experiment results show that the proposed method outperforms other existing state-of-the-art works with an accuracy of 98.73%.

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“…Firstly, we extract the interval of the squared prediction error (SPE), which fluctuates around multiple thresholds. [28][29][30] Next, we apply the global-local percentile method to these intervals and identify the TSP as the maximum slope of the initial segment. It is important to note that the global and local methods use different percentiles, with the former being dependent on the latter.…”

Section: Introductionmentioning

confidence: 99%

GELU‐LSTM‐encoder‐decoder fault prediction for batch processes based on the global–local percentile method

Gao,

Li,

et al. 2024

Can J Chem Eng

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Fault prediction is a crucial strategy for ensuring the safety and efficiency of batch processes, and it aims to predict the degradation trend and isolate the fault. Operating mistakes or external environment changes can lead to the degradation of batch processes, eventually resulting in faults. The point at which degradation begins is known as the time to start prediction (TSP). Determining the TSP provides more reliable degradation information for fault prediction and enhances prediction accuracy. Nevertheless, it is difficult to obtain TSP in batch processes because of some complex data features, such as multi‐phase and strong dynamics, which result in a significant loss of fault prediction. To address these challenges, a fault prediction method is proposed based on the global–local percentile method applied to Gaussian error linear unit‐long short‐term memory‐encoder‐decoder (GELU‐LSTM‐encoder‐ED). Firstly, squared prediction error (SPE) is normalized to eliminate differences in data features. Secondly, a global–local percentile method is proposed to determine the TSP. The period is divided based on the threshold fluctuation to derive the model input. Then the global percentile model is employed to identify the interval to which the TSP belongs. Subsequently, the local percentile model is employed to analyze this interval and identify the TSP. Thirdly, the LSTM based on the encoder‐decoder model, enhanced with the GELU, is utilized to improve the performance of fault prediction. Finally, two fault datasets on the penicillin fermentation process and Escherichia coli fermentation process are employed to demonstrate the performance of the presented method compared with other shared methods.

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Error Fusion of Hybrid Neural Networks for Mechanical Condition Dynamic Prediction

Cited by 5 publications

References 30 publications

Adaptive Data Fusion Method of Multisensors Based on LSTM-GWFA Hybrid Model for Tracking Dynamic Targets

Adaptive Data Fusion Method of Multisensors Based on LSTM-GWFA Hybrid Model for Tracking Dynamic Targets

Attention-Based CNN Fusion Model for Emotion Recognition During Walking Using Discrete Wavelet Transform on EEG and Inertial Signals

GELU‐LSTM‐encoder‐decoder fault prediction for batch processes based on the global–local percentile method

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