Functionalized -hexalactones (FDHLs): Bio-derivable Monomers to Synthesize Renewable Polyester Thermoplastics

Most thermoplastics are made from limited fossil-fuel sources and these plastics have very deleterious impacts on the environment. Finding a renewable source to synthesize new thermoplastic polymers with tunable properties like biodegradability, heat resistance, and moisture resistance are ongoing research interests. Lignocellulosic biomass is a promising renewable feedstock for biobased monomers and polymers production. In this work, functionalized -hexalactone (FDHL) monomers are hypothesized to be synthesizable from lignocellulosic sourced hydroxymethyl furfural (HMF) and lignin-derived pendant groups, generating a variety of aliphatic polyesters and potentially overcome current polymer challenges. Achieving a higher glass transition temperature (Tg) is one of the main obstacles for the current bio-based thermoplastics. A successful FDHL monomer synthesis used commercially available methyl cyclopentanone-2-carboxylate as a starting material. Different bulky, lignin derivatives were incorporated as pendant groups (aromatic: phenol, 1-naphthol, and 2-phenyl phenol; alkyl: cyclohexanol) in the monomer to increase the glass transition temperature (Tg) beyond that possible from poly(δ-valerolactone). Different acidic to super basic organocatalysts were screened to polymerize these novel monomers in a controlled manner. The polymerizations were carried out at room temperature via ring-opening polymerization technique using benzyl alcohol (BnOH) as an initiator. Different aliphatic polyester polymers with higher molecular weight and low dispersity were synthesized in a controlled manner. Typical equilibrium polymerization behavior was observed at room temperature due to the low ring strain of monomers, and the reaction was observed to be pseudo-first-order to monomer concentration in solution. By adding one phenyl group at the δ-position we found the Tg about +6 °C, and additional phenyl groups at the δ-position yielded the Tg about 39.5 °C which is a 105 °C increase from the unsubstituted poly(δ-valerolactone)

Noise eliminated ensemble empirical mode decomposition scalogram analysis for rotating machinery fault diagnosis

Rotating machinery is one type of major industrial component that suffers from various faults and damage due to the constant workload to which it is subjected. Therefore, a fast and reliable fault diagnosis method is essential for machine condition monitoring. Artificial intelligence can be applied in fault feature extraction and classification. It is crucial to use an effective feature extraction method to obtain most of the fault information and a robust classifier to classify those features. In this study, an improved method, noise-eliminated ensemble empirical mode decomposition (NEEEMD), was proposed to reduce the white noise in the intrinsic functions and retain the optimum ensembles. A convolution neural network (CNN) classifier was applied for classification because of its feature-learning ability. A generalised CNN architecture was proposed to reduce the model training time. The classifier input used was 64×64 pixel RGB scalogram samples. However, CNN requires a large amount of training data to achieve high accuracy and robustness. Deep convolution generative adversarial network (DCGAN) was applied for data augmentation during the training phase. To evaluate the effectiveness of the proposed feature extraction method, scalograms from the related feature extraction methods such as ensemble empirical mode decomposition (EEMD), complementary EEMD (CEEMD) and continuous wavelet transform (CWT) were also classified. The effectiveness of the scalograms was also validated by comparing the classifier performance using greyscale samples from the raw vibration signals. The ability of CNN was compared with two traditional machine learning algorithms, k nearest neighbour (kNN) and the support vector machine (SVM), using statistical features from EEMD, CEEMD and NEEEMD. The proposed method was validated using bearing and blade datasets. The results show that the machine learning algorithms achieved comparatively lower accuracy than the proposed CNN model. All the outputs from the bearing and blade fault classifiers demonstrated that the scalogram samples from the proposed NEEEMD method obtained the highest accuracy, sensitivity and robustness using CNN. DCGAN was applied with the proposed NEEEMD scalograms to enhance the CNN classifier’s performance further and identify the optimal amount of training data. After training the classifier using the augmented samples, the results showed that the classifier obtained even higher validation and test accuracy with greater robustness. The test accuracies improved from 98%, 96.31% and 92.25% to 99.6%, 98.29% and 93.59%, respectively, for the different classifier models using NEEEMD. The proposed method can be used as a more generalised and robust method for rotating machinery fault diagnosis

Noise Eliminated Ensemble Empirical Mode Decomposition Scalogram Analysis for Rotating Machinery Fault Diagnosis

Rotating machinery is one of the major components of industries that suffer from various faults due to the constant workload. Therefore, a fast and reliable fault diagnosis method is essential for machine condition monitoring. In this study, noise eliminated ensemble empirical mode decomposition (NEEEMD) was used for fault feature extraction. A convolution neural network (CNN) classifier was applied for classification because of its feature learning ability. A generalized CNN architecture was proposed to reduce the model training time. A sample size of 64×64×3 pixels RGB scalograms are used as the classifier input. However, CNN requires a large number of training data to achieve high accuracy and robustness. Deep convolution generative adversarial network (DCGAN) was applied for data augmentation during the training phase. To evaluate the effectiveness of the proposed feature extraction method, scalograms from related feature extraction methods such as ensemble empirical mode decomposition (EEMD), complementary EEMD (CEEMD), and continuous wavelet transform (CWT) are classified. The effectiveness of scalograms is also validated by comparing the classifier performance using grayscale samples from the raw vibration signals. All the outputs from bearing and blade fault classifiers showed that scalogram samples from the proposed NEEEMD method obtained the highest accuracy, sensitivity, and robustness using CNN. DCGAN was applied with the proposed NEEEMD scalograms to further increase the CNN classifier’s performance and identify the optimal number of training data. After training the classifier using augmented samples, the results showed that the classifier obtained even higher validation and test accuracy with greater robustness. The proposed method can be used as a more generalized and robust method for rotating machinery fault diagnosis

Ensemble Augmentation for Deep Neural Networks Using 1-D Time Series Vibration Data

Purpose Deep Neural Networks (DNNs) typically require enormous labeled training samples to achieve optimum performance. Therefore, numerous forms of data augmentation techniques are employed to compensate for the lack of training samples. Methods In this paper, a data augmentation technique named ensemble augmentation is proposed to generate real-like samples. This augmentation method uses the power of white noise added in ensembles to the original samples to generate real-like samples. After averaging the signal with ensembles, a new signal is obtained that contains the characteristics of the original signal. The parameters for the ensemble augmentation are validated using a simulated signal. The proposed method is evaluated by 10 class-bearing vibration data using three Transfer Learning (TL) models, namely, Inception-V3, MobileNet-V2, and ResNet50. The outputs from the proposed method are compared with no augmentation and different augmentation techniques. Results The results showed that the classifiers with the ensemble augmentation have higher validation and test accuracy than all the other augmentation techniques. The robustness assessment conducted with noisy test samples and test samples from different loads showed that the classifiers could obtain much higher robustness when trained with samples from ensemble augmentation. Conclusion The proposed data augmentation technique can be applied to 1-D time series data to achieve robust classifiers

Noise eliminated ensemble empirical mode decomposition for bearing fault diagnosis

Although noise-assisted decomposition methods, ensemble empirical mode decomposition (EEMD) and complementary EEMD (CEEMD) can reduce the drawbacks of empirical mode decomposition (EMD), they cannot fully eliminate the presence of white noise. In this paper, a method named noise eliminated EEMD (NEEEMD) was proposed to reduce further the white noise in the intrinsic functions and keep the ensembles optimum. The NEEEMD algorithm also decomposes the ensemble of white noise signals using EMD and subtracts from the outputs of EEMD. A simulated signal was used to demonstrate the performance of NEEEMD using root-mean-square error (RRMSE) and time & envelope spectrum kurtosis (TESK). A sensitive mode (SM) selection method was proposed to select the most sensitive intrinsic mode functions (IMFs) from NEEEMD which takes multiplication of kurtosis in the time domain and energy-entropy in the frequency domain. Finally, to enhance the signal's fault-related impulses, an advanced filter called MOMEDA was applied to the most sensitive IMF. The significance of the proposed method was illustrated using the envelope spectrum from bearing signals containing different types of faults at various speeds and motor loads. The output of the proposed method, EEMD and CEEMD was compared using the envelope spectrum to identify fault characteristic impulses. Envelope spectrum analysis proved that our proposed method performed better in every case by providing more fault-related impulses

Performance evaluation of BPSO & PCA as feature reduction techniques for bearing fault diagnosis

Vibration-based signal processing is the most popular and effective approach for fault diagnosis of bearing. In this paper, time-frequency domain analysis, i.e. empirical mode decomposition (EMD) was applied to the raw vibration signal. Intrinsic mode function (IMF) containing the characteristics of vibration data was analysed to obtain 90 statistical features. Two feature reduction algorithms, namely principal components analysis (PCA) and binary particle swarm optimiser (BPSO) were applied individually for feature reduction. The reduced feature subsets were 12 and 35 for PCA and BPSO, respectively. K-Nearest Neighbours (K-NN) was used as an intelligent method for fault diagnosis. K-NN was applied to the entire feature set and individually on the selected feature subset of PCA and BPSO. The reduced feature subset with PCA performed the finest in all the measurements taken. For BPSO, although it effectively reduced the feature dimension and classification time, the testing accuracy was slightly lower. Comparing the output accuracy of the K-NN classifier for the selected methods demonstrated the effectiveness of PCA and BPSO as efficacious feature reduction technique

Leak diagnosis of pipeline based on empirical mode decomposition and support vector machine

The pipeline is used as a medium of transportation in global gas and oil industries, providing the most efficient, convenient and transportation method for natural gas and oil from downstream to upstream production of the economical mode of the power station, refineries, and domestic needs. However, the pipeline leakages become a major concern as their failure may contribute to operational and economic loss as well as environmental pollution. This paper proposed a system to detect pipe fault at different locations. Empirical Mode Decomposition (EMD) was applied for feature extraction using energy and kurtosis. The one-against-one (OAO) and one-against-all (OAA) multiclass SVM with radial basis function (RBF), polynomial and sigmoid kernel functions were implemented in order to classify the multiple fault locations from the extracted features. RBF kernel function recorded the highest classification accuracy for both OAO and OAA approaches with 97.77% and 96.29%, respectively, followed by slightly reduced accuracy for sigmoid whereas significantly low accuracy for the polynomial kernel. The outputs were further analysed to justify the performance of the classifiers. From all the cases, it was observed that OAO-SVM with RBF kernel performed the best for pipe fault diagnosis