Synchro-Transient-Extracting Transform for the Analysis of Signals with Both Harmonic and Impulsive Components

Time-frequency analysis (TFA) techniques play an increasingly important role in the field of machine fault diagnosis attributing to their superiority in dealing with nonstationary signals. Synchroextracting transform (SET) and transient-extracting transform (TET) are two newly emerging techniques that can produce energy concentrated representation for nonstationary signals. However, SET and TET are only suitable for processing harmonic signals and impulsive signals, respectively. This poses a challenge for each of these two techniques when a signal contains both harmonic and impulsive components. In this paper, we propose a new TFA technique to solve this problem. The technique aims to combine the advantages of SET and TET to generate energy concentrated representations for both harmonic and impulsive components of the signal. Furthermore, we theoretically demonstrate that the proposed technique retains the signal reconstruction capability. The effectiveness of the proposed technique is verified using numerical and real-world signals

Remaining service life prediction based on gray model and empirical Bayesian with applications to compressors and pumps

open access articleIn this study, a three-step remaining service life (RSL) prediction method, which involves feature extraction, feature selection, and fusion and prognostics, is proposed for large-scale rotatingmachinery in the presence of scarce failure data. In the feature extraction step, eight time-domain degradation features are extracted from the faulty variables. A fitness function as a weighted linear combination of the monotonicity, robustness, correlation, and trendabilitymetrics is defined and used to evaluate the suitability of the features for RSL prediction. The selected features are merged using a canonical variate residuals-based method. In the prognostic step, graymodel is used in combinationwith empirical Bayesian algorithm for RSL prediction in the presence of scarce failure data. The proposed approach is validated on failure data collected froman operational industrial centrifugal pump and a compressor

An adaptive synchroextracting transform for the analysis of noise contaminated multi-component non-stationary signals

The Synchro-Extracting Transform technique (SET) can capture the changing dynamic in a non-stationary signal which can be applied for fault diagnosis of rotating machinery operating under vary-ing speed or/and load conditions. However, the time frequency representation (TFR) of a signal pro-duced by SET can be affected by noise contained in the signal, which can largely reduce the accuracy of fault diagnosis. This paper addresses this drawback and presents a new extraction operator to im-prove the energy concentration of the TFR of a noise contaminated multi-component signal by using an adaptive ridge curve identification process together with SET. The adaptive ridge curve extraction is deployed to extract the signal components of a multi-component signal via an iterative approach. The effectiveness of the algorithm is verified using one set of simulated noise-added signals and two sets of experimental bearing and gearbox defect signals. The result shows that the proposed technique can accurately identify the fault components from noise contaminated multi-component non-stationary machine defect signals

SEntiMoji: An Emoji-Powered Learning Approach for Sentiment Analysis in Software Engineering

Sentiment analysis has various application scenarios in software engineering (SE), such as detecting developers' emotions in commit messages and identifying their opinions on Q&A forums. However, commonly used out-of-the-box sentiment analysis tools cannot obtain reliable results on SE tasks and the misunderstanding of technical jargon is demonstrated to be the main reason. Then, researchers have to utilize labeled SE-related texts to customize sentiment analysis for SE tasks via a variety of algorithms. However, the scarce labeled data can cover only very limited expressions and thus cannot guarantee the analysis quality. To address such a problem, we turn to the easily available emoji usage data for help. More specifically, we employ emotional emojis as noisy labels of sentiments and propose a representation learning approach that uses both Tweets and GitHub posts containing emojis to learn sentiment-aware representations for SE-related texts. These emoji-labeled posts can not only supply the technical jargon, but also incorporate more general sentiment patterns shared across domains. They as well as labeled data are used to learn the final sentiment classifier. Compared to the existing sentiment analysis methods used in SE, the proposed approach can achieve significant improvement on representative benchmark datasets. By further contrast experiments, we find that the Tweets make a key contribution to the power of our approach. This finding informs future research not to unilaterally pursue the domain-specific resource, but try to transform knowledge from the open domain through ubiquitous signals such as emojis.Comment: Accepted by the 2019 ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2019). Please include ESEC/FSE in any citation

Global hybrid simulations of soft X-ray emissions in the Earth’s magnetosheath

Earth’s magnetopause is a thin boundary separating the shocked solar wind plasma from the magnetospheric plasmas, and it is also the boundary of the solar wind energy transport to the magnetosphere. Soft X-ray imaging allows investigation of the large-scale magnetopause by providing a two-dimensional (2-D) global view from a satellite. By performing 3-D global hybrid-particle-in-cell (hybrid-PIC) simulations, we obtain soft X-ray images of Earth’s magnetopause under different solar wind conditions, such as different plasma densities and directions of the southward interplanetary magnetic field. In all cases, magnetic reconnection occurs at low latitude magnetopause. The soft X-ray images observed by a hypothetical satellite are shown, with all of the following identified: the boundary of the magnetopause, the cusps, and the magnetosheath. Local X-ray emissivity in the magnetosheath is characterized by large amplitude fluctuations (up to 160%); however, the maximum line-of-sight-integrated X-ray intensity matches the tangent directions of the magnetopause well, indicating that these fluctuations have limited impact on identifying the magnetopause boundary in the X-ray images. Moreover, the magnetopause boundary can be identified using multiple viewing geometries. We also find that solar wind conditions have little effect on the magnetopause identification. The Solar wind Magnetosphere Ionosphere Link Explorer (SMILE) mission will provide X-ray images of the magnetopause for the first time, and our global hybrid-PIC simulation results can help better understand the 2-D X-ray images of the magnetopause from a 3-D perspective, with particle kinetic effects considered

Twitter Sentiment Analysis via Bi-sense Emoji Embedding and Attention-based LSTM

Sentiment analysis on large-scale social media data is important to bridge the gaps between social media contents and real world activities including political election prediction, individual and public emotional status monitoring and analysis, and so on. Although textual sentiment analysis has been well studied based on platforms such as Twitter and Instagram, analysis of the role of extensive emoji uses in sentiment analysis remains light. In this paper, we propose a novel scheme for Twitter sentiment analysis with extra attention on emojis. We first learn bi-sense emoji embeddings under positive and negative sentimental tweets individually, and then train a sentiment classifier by attending on these bi-sense emoji embeddings with an attention-based long short-term memory network (LSTM). Our experiments show that the bi-sense embedding is effective for extracting sentiment-aware embeddings of emojis and outperforms the state-of-the-art models. We also visualize the attentions to show that the bi-sense emoji embedding provides better guidance on the attention mechanism to obtain a more robust understanding of the semantics and sentiments

Index Similarity Assisted Particle Filter for Early Failure Time Prediction with Applications to Turbofan Engines and Compressors

The particle filter (PF) has been widely studied in the prognostics’ field due to its ability to deal with nonlinear and non-stationary systems. However, there is no update of the model parameters during the prediction, preventing PF to work in its traditional way to generate accurate long-term predictions. In order to solve this problem, we put forward an improved PF that is based on a novel health index (HI) similarity matching method. This method is employed to search for similar HIs in the training library and construct an optimal “similar HI” for the system under study. Finally, the obtained HI is consistently fed into the PF to deliver precise state-of-health (SoH) estimates. The effectiveness of the proposed PF was validated on the CMAPSS datasets as well as data collected from an operational reciprocating compressor. We observed that the new similarity matching method demonstrated excellent performance in finding suitable HIs for failure time prediction. We also observed that the proposed PF framework had a superior prognostics performance over the standard PF. We obtained an averaged predictive accuracy of 96% (C-MAPSS data) and 92% (compressor data) when only the first 10% of the degradation data were used. This work highlights the promise of combining index similarity, Procrustes analysis and PF for complementing existing prognostic methods

Smoothed Finite Element Methods for Predicting the Mid to High Frequency Acoustic Response in the Cylinder-Head Chamber of a Diesel Engine

Modeling and simulation of the acoustic response in enclosed cavities of a diesel engine are of great significance for optimal design of an engine to achieve a better acoustic performance. Nevertheless, the use of the traditional finite element method (FEM) for the mid to high frequency acoustic prediction is limited by the well-known numerical dispersion errors and the tedious preprocessing of the model. Smoothed finite element methods (SFEMs) proposed originally for solid mechanics have been employed for the modeling of acoustic problems in the low to medium frequency ranges whilst acoustic modeling in the mid to high frequency range remains untouched. This paper comprehensively investigates into the performance of SFEMs in modeling and simulation of mid to high frequency acoustic problems. It is shown that the mass-redistributed edge-based smoothed finite element method (MR-ES-FEM) can yield an excellent prediction result in the mid to high frequency range in terms of accuracy, efficiency and robustness. The MR-ES-FEM is also used to simulate sound propagation in a cylinder head chamber of a four-cylinder diesel engine to prove its effectiveness. The findings presented in this paper offer an in-depth insight for engineers to select suitable numerical methods for solving mid to high frequency acoustic problems in the design of diesel engines.</p

Polydopamine-Functionalized Copper Peroxide/ZIF-8 Nanoparticle-Based Fluorescence-Linked Immunosorbent Assay for the Sensitive Determination of Carcinoembryonic Antigen by Self-Supplied H₂O₂ Generation

Copper peroxide/zeolitic imidazolate framework/polydopamine nanoparticles (CP/ZIF-8/PDA)-based fluorescence-linked immunosorbent assay (FLISA) was designed for the sensitive and high-throughput determination of carcinoembryonic antigen (CEA) by self-supplied H2O2 generation. Specifically, the CEA aptamer was modified on the surface of CP/ZIF-8/PDA to form an immunoprobe. The structures of CP and ZIF-8 could be broken under acidic conditions, and produced the Cu2+ and H2O2 due to the dissociation the CP. A subsequent Fenton-type reaction of Cu2+ and H2O2 generated hydroxyl radical (·OH). o-phenylenediamine (OPD) was oxidized by the ·OH to form 2, 3-diaminophenazine (DPA) with a significant fluorescence signal. CP/ZIF-8/PDA could be used as an efficient Fenton-type reactant to generate a large amount of ·OH to promote OPD oxidation. The sensitive detection of CEA could be realized. Under optimal conditions, the FLISA platform displayed a linear detection range from 0.01 to 20 ng mL−1 with a detection limit of 7.6 pg mL−1 for CEA. This strategy has great application potential for sensitive and high-throughput determination for other biomarkers in the field of biomedicine