Improved Complete Ensemble Empirical Mode Decomposition With Adaptive Noise Research Articles

Research on Dual-Grating Spacing Calibration Method Based on Multiple Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise Combined with Hilbert Transform

The paper proposes a method for the calibration of spacing in dual-grating based on Multiple Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) combined with Hilbert Transform (HT), referred to as Multiple ICEEMDAN-HT. This method addresses the potential impact of nonlinear factors on phase extraction accuracy, consequently on ranging precision in the homodyne interference of the dual-grating. Building upon the ICEEMDAN algorithm, the signal undergoes iterative decomposition and reconstruction using the sample entropy criterion. The intrinsic mode functions (IMFs) obtained from multiple iterations are then reconstructed to obtain the complete signal. Through a simulation and comparison with other signal decomposition methods, the repeatability and completeness of signal reconstruction by Multiple ICEEMDAN are verified. Finally, an actual dual-grating ranging system is utilized to calibrate the spacing of the planar grating. Experimental results demonstrate that the calibration relative error of the Multiple ICEEMDAN-HT phase unwrapping method can be reduced to as low as 0.07%, effectively enhancing the signal robustness and spacing calibration precision.

Traffic Flow Prediction by an Ensemble Framework with Data Denoising and Functional Principal Components Analysis

Accurate traffic flow data are important for congestion identification and real-time traffic control. Raw traffic flow data may be disturbed by different noises during the acquisition process, which leads to the degradation of model prediction performance. To address this problems, this paper proposes a data denoising method based on the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) combined with wavelet threshold denoising (WTD) to suppress potential outliers in the data, and then introduces functional principal component analysis to accomplish the traffic forecasting task. We use the proposed framework for real traffic flow prediction and validate the prediction performance of the proposed framework using root mean square error and mean absolute error. In the prediction performance comparison, five denoising methods are considered: empirical mode decomposition (EMD), ensemble empirical mode decomposition, complete ensemble empirical mode decomposition with adaptive noise, ICEEMDAN, and WTD. The prediction results show that the model combined with the denoising method outperforms the model without the denoising method. ICEEMDAN combined with WTD’s denoising method improves prediction performance more than other methods. Furthermore, the WTD method with dmey type achieves higher accuracy than other types.

ICEEMDAN-Informer-GWO: a hybrid model for accurate wind speed prediction.

Extensive research has been diligently conducted on wind energy technologies in response to pressing global environmental challenges and the growing demand for energy. Accurate wind speed predictions are crucial for the effective integration of large wind power systems. This study presents a novel and hybrid framework called ICEEMDAN-Informer-GWO, which combines three components to enhance the accuracy of wind speed predictions. The improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) component improves the decomposition of wind speed data, the Informer model provides computationally efficient wind speed predictions, and the grey wolf optimisation (GWO) algorithm optimises the parameters of the Informer model to achieve superior performance. Three different sets of wind speed prediction (WSP) models and wind farm data from Block Island, Gulf Coast, and Garden City are used to thoroughly assess the proposed hybrid framework. This evaluation focusses on WSP for three specific time horizons: 5minutes, 30minutes, and 1hour ahead. The results obtained from the three conducted experiments conclusively demonstrate that the proposed hybrid framework exhibits superior performance, leading to statistically significant improvements across all three time horizons.

MEMS Gyroscope Temperature Compensation Based on Improved Complete Ensemble Empirical Mode Decomposition and Optimized Extreme Learning Machine

Herein, we investigate the temperature compensation for a dual-mass MEMS gyroscope. After introducing and simulating the dual-mass MEMS gyroscope’s working modes, we propose a hybrid algorithm for temperature compensation relying on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), sample entropy, time–frequency peak filtering, non-dominated sorting genetic algorithm-II (NSGA II) and extreme learning machine. Firstly, we use ICEEMDAN to decompose the gyroscope’s output signal, and then we use sample entropy to classify the decomposed signals. For noise segments and mixed segments with different levels of noise, we use time–frequency peak filtering with different window lengths to achieve a trade-off between noise removal and signal retention. For the feature segment with temperature drift, we build a compensation model using extreme learning machine. To improve the compensation accuracy, NSGA II is used to optimize extreme learning machine, with the prediction error and the 2-norm of the output-layer connection weight as the optimization objectives. Enormous simulation experiments prove the excellent performance of our proposed scheme, which can achieve trade-offs in signal decomposition, classification, denoising and compensation. The improvement in the compensated gyroscope’s output signal is analyzed based on Allen variance; its angle random walk is decreased from 0.531076°/h/√Hz to 6.65894 × 10−3°/h/√Hz and its bias stability is decreased from 32.7364°/h to 0.259247°/h.

Refined offshore wind speed prediction: Leveraging a two-layer decomposition technique, gated recurrent unit, and kernel density estimation for precise point and interval forecasts

Precise forecasting of offshore wind speeds is paramount for various applications, including offshore wind energy production, disaster prevention and mitigation, and maritime navigation. This study introduces a novel model for point and interval prediction of offshore wind speed, incorporating an innovative two-layer decomposition technique, gated recurrent unit (GRU), and kernel density estimation (KDE). Initially, the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is utilized to decompose the raw series into multiple subsequences, thus alleviating the inherent data nonstationarity. Subsequently, we employ the refined composite multiscale fuzzy entropy (RCMFE) to reconstruct the subsequences into multiple components, thereby reducing the computational complexity. Variational mode decomposition (VMD) is utilized to enhance the modeling accuracy to decompose the high-frequency component, thereby generating a set of submodels. The GRU, a specialized recurrent neural network architecture, is leveraged to forecast the remaining components and submodels, producing a series of subresults. The subresults are linearly summed to obtain the final point prediction result. Finally, based on point prediction, employing KDE to gauge the probability density distribution of prediction errors culminates in the derivation of the offshore wind speed prediction interval. The experimental results substantiate the superior predictive performance of the proposed prediction model.

Forecasting carbon price with attention mechanism and bidirectional long short-term memory network

To improve the precision of carbon price forecasting, our study aims to propose a novel hybrid forecasting model which integrates recurrent neural networks and attention mechanisms. First, the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) algorithm is employed to decompose carbon prices into several regular intrinsic mode functions (IMFs) and a residual. Second, multiscale entropy is utilized to differentiate and reconstruct these components to reduce cumulative errors in subsequent forecasting. Subsequently, a bidirectional long short-term memory network (Bi-LSTM) equipped with attention mechanisms is used to forecast each reconstructed component. Attention mechanisms identifies crucial sequence elements, assigns different weights to hidden information, and extracts richer information from the series. Finally, the results of all components are integrated to obtain the final forecasting result. Empirical analysis conducted on real datasets from the Guangdong and Hubei carbon markets demonstrates that the proposed hybrid model outperform prevailing mainstream forecasting models in terms of both horizontal and directional forecasting metrics.

An alternative 2D ICEEMDAN-based denoising method and its application in processing magnetic anomaly data

Due to environmental noise and human factors, magnetic data collected in the field often contain various noises and interferences that significantly affect the subsequent data processing and interpretation. Empirical Mode Decomposition (EMD), an adaptive multiscale analysis method for nonlinear and non-stationary signals, is widely used in geophysical and geodetic data processing. Compared with traditional EMD, Improved Complete Ensemble EMD with Adaptive Noise (ICEEMDAN) is more effective in addressing the problem of mode mixing. Based on the principles of 1D ICEEMDAN, this paper presents an alternative algorithm for 2D ICEEMDAN, extending its application to two-dimensional scenarios. The effectiveness of the proposed approach is demonstrated through synthetic signal experiments, which show that the 2D ICEEMDAN exhibits a weaker mode mixing effect compared to the traditional bidimensional EMD (BEMD) method. Furthermore, to improve the performance of the denoising method based on 2D ICEEMDAN and preserve useful signals in high-frequency components. Synthetic magnetic anomaly data testing indicates that our denoising method effectively preserves signal continuity and outperforms traditional soft thresholding methods. To validate the practical application of this improved threshold denoising method based on 2D ICEEMDAN, it is applied to ground magnetic survey data in the Yandun area of Xinjiang. The results demonstrate the effectiveness of the method in removing noise while retaining essential information from practical magnetic anomaly data. In particular, practical applications suggest that 2D ICEEMDAN can extract trend signals more accurately than the BEMD. In conclusion, as a potential tool for multi-scale decomposition, the 2D ICEEMDAN is versatile in processing and analyzing 2D geophysical and geodetic data.

Sub-ppm NDIR [formula omitted] gas sensor using ICEEMDAN-SG joint denoising algorithm

Due to the excellent insulation and arc extinguishing properties of SF6 gas, it is widely used in power equipment such as gas insulated switchgear or gas circuit breaker. Therefore, detection of SF6 gas leakage with high sensitivity is necessary. Non dispersive infrared (NDIR) technology have advantages such as miniaturization, low power consumption, simple structure, low cost, and non-destructive testing, making it suitable for building large-scale sensor networks. However, NDIR sensors are susceptible to noise coming from the emitter, the detector and the circuits, which receives little research attention. In this study, we propose a new denoising method by combining improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) with Savitzky-Golay (S-G) to the noise suppression, which achieves a 7 dB signal-to-noise ratio improvement over the traditional wavelet threshold denoising, making the detection limit of the sensing system reach the level below 1 ppm.

Permutation fuzzy entropy based ICEEMDAN de-noising for inertial sensors

As one of the critical factors affecting the accuracy of inertial navigation system (INS) positioning, random noise will lead to the accumulation of INS positioning errors, even the failure of the system. Previous studies mainly focus on achieving noise reduction by adopting either the wavelet decomposition method or some relevant methods of the empirical mode decomposition (EMD). However, the former is limited by Heisenberg’s uncertainty principle when processing non-stationary signals, and the latter exhibits numerous mode-mixing phenomena during the decomposition process. In this paper, a permutation fuzzy entropy (PFE) based improved complete ensemble EMD with adaptive noise (ICEEMDAN) de-noising method is proposed to utilize the ICEEMDAN method for signal decomposition, and the PFE as an index to distinguish the effective intrinsic mode functions (IMFs) and the noisy IMFs during the decomposition process. In addition, simulated signals with different noise levels and field data with different durations are utilized to design a group of experiments to test the de-noising performance of the PFE-based ICEEMDAN de-noising method. The experimental results demonstrate that the advantages of our proposed method consists of three main parts: (1) The ICEEMDAN method more effectively alleviates the mode-mixing problem compared to the complete ensemble EMD with adaptive noise (CEEMDAN), thereby enhancing the accuracy of inertial measurement unit (IMU) signal reconstruction; (2) the PFE is more preferable than the permutation entropy for classify IMFs decomposed by the ICEEMDAN method; (3) our proposed method can effectively reduce IMU signal noise and improve the positioning accuracy of inertial sensor.

A multi-stage forecasting system for daily ocean tidal energy based on secondary decomposition, optimized gate recurrent unit and error correction

Tidal energy, as a new energy, has very high research potential and practical application value. For the characteristics of tidal energy such as nonstationarity and nonlinearity, a multi-stage forecasting system for daily ocean tidal energy is proposed. It is based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), refined composite multi-scale dispersion entropy (RCMDE), empirical mode decomposition based on time-varying filter modified by white shark optimizer (WSOTVFEMD), improved gate recurrent unit using parasitic salp swarm algorithm based on differential evolution (PDESSAGRU) and error correction using CNN (CNN-EC), named as ICEEMDAN-RCMDE-WSOTVFEMD-PDESSAGRU–CNN–EC. Firstly, decompose tidal energy into a series of intrinsic mode functions (IMFs) by ICEEMDAN, and divide IMFs into high-complexity and low-complexity components by RCMDE. Next, secondly decompose the reconstructed high-complexity components into high-complexity parts by WSOTVFEMD. Afterwards, separately predict each component of the high-complexity parts and the low-complexity components by PDESSA, and reconstruct the predicted results to obtain original predicting results. In the end, decompose the error into error IMFs (EIMFs) by ICEEMDAN, predict EIMFs with convolutional neural network (CNN) respectively to acquire error predicting results, and reconstruct original predicting results and error predicting results to acquire the final results. Taking the tidal energy of the American cities including San Francisco, Sitka, and Wauna as case studies, the results show that the proposed system has high prediction accuracy after experiments with 13 comparative models in each city.

A multi-factor combination prediction model of carbon emissions based on improved CEEMDAN.

As the global greenhouse effect intensifies, carbon emissions are gradually becoming a hot topic of discussion. Accurate carbon emissions prediction is an important foundation to realize carbon neutrality and peak carbon dioxide emissions. To accurately predict carbon emissions, a multi-factor combination prediction model based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), bidirectional long short-term memory optimized by lemurs optimizer (LOBiLSTM) and least squares support vector machine optimized by lemurs optimizer (LOLSSVM), named ICEEMDAN-LOBiLSTM-LOLSSVM, is proposed. Firstly, the influencing factors of carbon emissions are selected by Spearman correlation coefficient, and carbon emissions are decomposed into intrinsic mode functions (IMFs) by ICEEMDAN. Secondly, the influencing factors and IMFs are input into LOBiLSTM and LOLSSVM respectively for prediction. Then, the point prediction results are obtained by weighting the prediction results of LOBiLSTM and LOLSSVM. Finally, probability density function of point prediction error is calculated by kernel density estimation, and the interval prediction results are calculated according to different confidence intervals. Carbon emissions of China and Germany are selected to verify the superiority of ICEEMDAN-LOBiLSTM-LOLSSVM. The experiment shows that RMSE, MAE, MAPE, and R2 of the proposed model are 0.4468, 0.3612, 0.0120, and 0.9839 respectively for China, which is the best among the nine models, as well as for Germany.

Short-Term Photovoltaic Output Prediction Based on Decomposition and Reconstruction and XGBoost under Two Base Learners

Photovoltaic power generation prediction constitutes a significant research area within the realm of power system artificial intelligence. Accurate prediction of future photovoltaic output is imperative for the optimal dispatchment and secure operation of the power grid. This study introduces a photovoltaic prediction model, termed ICEEMDAN-Bagging-XGBoost, aimed at enhancing the accuracy of photovoltaic power generation predictions. In this paper, the original photovoltaic power data initially undergo decomposition utilizing the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) algorithm, with each intrinsic mode function (IMF) derived from this decomposition subsequently reconstructed into high-frequency, medium-frequency, and low-frequency components. Targeting the high-frequency and medium-frequency components of photovoltaic power, a limiting gradient boosting tree (XGBoost) is employed as the foundational learner in the Bagging parallel ensemble learning method, with the incorporation of a sparrow search algorithm (SSA) to refine the hyperparameters of XGBoost, thereby facilitating more nuanced tracking of the changes in the photovoltaic power’s high-frequency and medium-frequency components. Regarding the low-frequency components, XGBoost-Linear is utilized to enable rapid and precise prediction. In contrast with the conventional superposition reconstruction approach, this study employs XGBoost for the reconstruction of the prediction output’s high-frequency, intermediate-frequency, and low-frequency components. Ultimately, the efficacy of the proposed methodology is substantiated by the empirical operation data from a photovoltaic power station in Hebei Province, China. Relative to integrated and traditional single models, this paper’s model exhibits a markedly enhanced prediction accuracy, thereby offering greater applicational value in scenarios involving short-term photovoltaic power prediction.

Forecasting China carbon price using an error-corrected secondary decomposition hybrid model integrated fuzzy dispersion entropy and deep learning paradigm.

Forecasting China's carbon price accurately can encourage investors and manufacturing industries to take quantitative investments and emission reduction decisions effectively. The inspiration for this paper is developing an error-corrected carbon price forecasting model integrated fuzzy dispersion entropy and deep learning paradigm, named ICEEMDAN-FDE-VMD-PSO-LSTM-EC. Initially, the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is used to primary decompose the original carbon price. Subsequently, the fuzzy dispersion entropy (FDE) is conducted to identify the high-complexity signals. Thirdly, the variational mode decomposition (VMD) and deep learning paradigm of particle swarm optimized long short-term memory (PSO-LSTM) models are employed to secondary decompose the high-complexity signals and perform out-of-sample forecasting. Finally, the error-corrected (EC) method is conducted to re-modify and strengthen the above-predicted accuracy. The results conclude that the forecasting performance of ICEEMDAN-type secondary decomposition models is significantly better than the primary decomposition models, the deep learning PSO-LSTM-type models have superiority in forecasting China carbon price, and the EC method for improving the forecasting accuracy has been proved. Noteworthy, the proposed model presents the best forecasting accuracy, with the forecasting errors RMSE, MAE, MAPE, and Pearson's correlation are 0.0877, 0.0407, 0.0009, and 0.9998, respectively. Especially, the long-term forecasting ability for 750 consecutive trading prices is outstanding. Those conclusions contribute to judging the carbon price characteristics and formulating market regulations.

An intelligent water supply pipeline leakage detection method based on SV-WTBSVM

Water supply pipeline leakage not only wastes resources but also causes dangerous accidents. Therefore, detecting the state of pipelines is a critical task. With the expansion of the scale of the water supply pipeline, the amount of data collected by the leak detection system is gradually increasing. Moreover, there is an imbalance of sample in the data. This makes the detection performance of traditional leakage detection methods deteriorate. To solve the above issues, this paper proposes a pipeline leakage intelligent detection method based on a support vector weighted twin-bound support vector machine (SV-WTBSVM). Noise in the data negatively affects the performance of the classifier. To eliminate the effect of noise, a hybrid denoising algorithm based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is used for denoising to filter out the noise in the data. Twin bound support vector machine (TBSVM) is a classical classification algorithm that has been widely used in the detection of pipeline leakage. To solve the decrease in classification accuracy caused by sample imbalance, the SV-WTBSVM algorithm oversamples the minority class samples based on the distance density and integrally undersamples the majority class samples to obtain a balanced sample. Since pipelines often have multiple working conditions, the SV-WTBSVM used for binary classification cannot meet this requirement, and this paper combines the SV-WTBSVM with the ‘one-to-one’ strategy to address the multi-classification problem. Finally, experiments have verified that the SV-WTBSVM algorithm not only retains the advantages of fast training speed and simple operation of the TBSVM but also improves the classification accuracy and generalization ability of the algorithm when dealing with imbalanced data.

De-noising classification method for financial time series based on ICEEMDAN and wavelet threshold, and its application

This paper proposes a classification method for financial time series that addresses the significant issue of noise. The proposed method combines improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and wavelet threshold de-noising. The method begins by employing ICEEMDAN to decompose the time series into modal components and residuals. Using the noise component verification approach introduced in this paper, these components are categorized into noisy and de-noised elements. The noisy components are then de-noised using the Wavelet Threshold technique, which separates the non-noise and noise elements. The final de-noised output is produced by merging the non-noise elements with the de-noised components, and the 1-NN (nearest neighbor) algorithm is applied for time series classification. Highlighting its practical value in finance, this paper introduces a two-step stock classification prediction method that combines time series classification with a BP (Backpropagation) neural network. The method first classifies stocks into portfolios with high internal similarity using time series classification. It then employs a BP neural network to predict the classification of stock price movements within these portfolios. Backtesting confirms that this approach can enhance the accuracy of predicting stock price fluctuations.

Hybrid carbon price forecasting using a deep augmented FEDformer model and multimodel optimization piecewise error correction

Accurate and robust carbon price forecasting is crucial for stabilizing the carbon financial market and reducing reliance on fossil resources. This study introduces a novel hybrid model, combining a deep augmented frequency enhanced decomposed transformer (DA-FEDformer), improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), and a multimodel optimization piecewise error correction, for accurate carbon price prediction. To leverage frequency domain information, the proposed DA-FEDformer model enhances the decoder and encoder layers by incorporating a multilayer perceptron layer. A novel improved kernel mean square error loss function is devised for the model, and the evolved sign momentum optimizer is employed for further optimization. To further improve the prediction accuracy, a data postprocessing method is integrated into the model. The DA-FEDformer predicts the carbon price, and the ICEEMDAN method decomposes the error sequence into subsequences. These subsequences are forecasted using five different models. Finally, the DA-FEDformer prediction results and the error correction results are combined to obtain the final carbon price sequence. This hybrid model is systematically evaluated using carbon price data from Hubei, Guangzhou, and Beijing in China. The results demonstrate that the hybrid model achieves favorable performance on three datasets. This innovative approach provides a promising solution for carbon price forecasting in China.

Multi-step power forecasting for regional photovoltaic plants based on ITDE-GAT model

With the integration of high proportion of distributed photovoltaic(PV), high-accuracy regional PV power forecasting technology can enhance the regional coordinated scheduling capability of the new power system. This paper proposes a regional PV power forecasting model based on an improved time-series dense encoder and graph attention network (ITDE-GAT), which takes into account the spatio-temporal correlations among the regional PV plants. Firstly, an improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN) is used to extract the clear-sky and fluctuation components from PV data. Secondly, the combined ITDE-GAT is applied to perform the regional PV power forecasting. Considering the static information, an improved dense encoder network (ITDE) is constructed to extract the temporal and spatial relationships of regional PV. Graph attention network (GAT) is then utilized to explore the spatial correlations among the regional PV. Finally, case study from two actual PV region datasets shows that the proposed model achieves higher forecasting accuracy and exhibits stronger generalization capabilities. The results demonstrate that compared to various advanced deep learning methods, the R2 evaluation metric of the approach proposed in this paper demonstrates, respectively, maximum improvements of 3.4 %, 6.5 %, and 7.8 % for the 1 h, 3 h, and 6 h ahead predictions.

A combined optimization prediction model for earth-rock dam seepage pressure using multi-machine learning fusion with decomposition data-driven

Seepage pressure data analysis is considered one of the most direct ways to evaluate seepage safety in dam engineering. Therefore, accurate and consistent prediction of seepage pressure is essential. However, satisfactory predictive performance is rugged due to the irregular, nonlinear, and non-stationary nature of seepage pressure monitoring data. This paper proposes a combined optimization prediction model for seepage pressure prediction to resolve the deficiency. The model is coupled with a data decomposition technology, multi-machine learning fusion technology, adaptive variable weight combined strategy, and an improved optimization algorithm. The interaction between high and low frequency components is eliminated by improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) technique to achieve the smoothing of non-stationary data. Multi-machine learning models are integrated by the combined strategy to compensate for the shortcomings of single models. Combined weights are adaptively changed by the kernel extreme learning machine (KELM) variable weighting strategy to overcome the limitations of traditional fixed weights. For the challenge of determining KELM parameters, the artificial electric field algorithm is improved to optimize KELM parameters to accelerate the determination of adaptive weights. The application of the proposed model to an earth-rock dam demonstrates enhanced predictive accuracy of seepage pressure by implementing strategies. The model is an effective tool for the seepage pressure prediction of dams and is conducive to dam operation and maintenance management.

Enhanced performance for simultaneous atmospheric N2O and CO measurements based on ICEEMDAN and GWO-LSSVM

A TDLAS-based sensor for simultaneous detection of N2O and CO has been developed by us, which is capable of monitoring the concentrations of both gases in real time with high stability and detection accuracy. Two distributed feedback (DFB) were deployed as laser sources, a 20 m multi-pass gas cell (MPGC) was installed for absorption, an InGaAs detector was used to receive the optical signals and graphical user interface (GUI) were designed based on the microcontroller. The modulation depth curves were obtained experimentally to obtain the lowest measured concentration. Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) was employed to increase signal-to-noise ratio (SNR) of the signals and GWO-LSSVM was performed to invert concentrations. The linearity of the sensor was analyzed by linearity experiments, which obtained the linearity of the sensor for N2O and CO, with linear correlation coefficients (R2) of 0.9990 and 0.998, respectively. The detection accuracy of the sensor was verified and the relative errors of the sensor were all less than 3.59 %. The stability and anti-interference ability of the sensor were verified, it was found that the standard deviation (SD) of N2O and CO measured by the sensor was 47.27 ppb and 48.31 ppb respectively. The limit of detection (LoD) for N2O and CO are 8.37 ppb and 5.91 ppb, respectively according to Allan-Werle deviation. The sensor has good performance in simultaneous detection of N2O and CO.

Trustworthy and intelligent fault diagnosis with effective denoising and evidential stacked GRU neural network

AbstractWith the advances in Internet-of-Things and data mining technologies, deep learning-based approaches have been widely used for intelligent fault diagnosis of manufacturing assets. However, uncertainty caused by the non-stationary process data such as vibration signal and noise interference in practical working environments will greatly affect the performance and reliability of predictions. The present paper develops a trustworthy and intelligent fault diagnosis framework based on a two-stage joint denoising method and evidential neural networks. The proposed denoising method integrating the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and the independent component analysis (ICA) method can effectively reduce data uncertainty caused by noise interference. The stacked gated recurrent unit (SGRU) model has been incorporated into the evidential neural networks as a deep classifier. The proposed evidential SGRU (ESGRU) method can quantify the prediction uncertainty, which estimates the prediction trustworthiness. Predictive entropy and reliability diagrams are used as calibration methods to validate the effectiveness of uncertainty estimation. The proposed framework is validated by two case studies of rolling bearing fault diagnosis in variable noise conditions. Experimental results demonstrate that the proposed method can achieve a high denoising effect and provide reliable uncertainty prediction results which are significant for practical applications.